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Since the introduction of sustainable development, industries have witnessed significant sustainability challenges. Literature shows that the food industry is concerned about its need for efficient and effective management practices in dealing with perishability and the requirements for conditioned storage and transport of food products that effect the environment. Hence, the environmental part of sustainability demonstrates its significance in this industrial sector. Despite this, there has been little research into environmentally sustainable inventory management of deteriorating items. This thesis presents mathematical modelling based research for production inventory systems in perishable food supply chains. In this study, multi-objective mixed-integer linear programming models are developed to determine economically and environmentally optimal production and inventory decisions for a two-echelon supply chain. The supply chain consists of single sourcing suppliers for raw materials and a producer who operates under a make-to-stock or make-to-order strategy. The demand facing the producer is non-stationary stochastic in nature and has requirements in terms of service level and the remaining shelf life of the marketed products. Using data from the literature, numerical examples are given in order to test and analyse these models. The computational experiments show that operational adjustments in cases where emission and cost parameters were not strongly correlated with supply chain collaboration (where suppliers and a producer operate under centralised control), emissions are effectively reduced without a significant increase in cost. The findings show that assigning a high disposal cost, limit or high weight of importance to perished goods leads to appropriate reduction of expected waste in the supply chain with no major cost increase. The research has made contributions to the literature on sustainable production and inventory management; providing formal models that can be used as an aid to understanding and as a tool for planning and improving sustainable production and inventory control in supply chains involving deteriorating items, in particular with perishable food supply chains.

Increasing legislative and societal pressures are forcing manufacturers to become environmentally-conscious and take responsibility for the fate of their goods after they have been used by consumers. As a result, some manufacturers operate hybrid systems which produce new goods and recover used goods. Product recovery describes the process by which used products are returned to their manufacturers or sent to a specialised facility for recovery, before being sold on the original or a secondary market. The quality of the returned goods is a significant issue in product recovery systems as it can affect both the type of recovery and costs associated with it. Quality in product recovery systems has not been adequately studied, with many authors either ignoring the possibility of receiving lower quality returns, or assuming they are disposed of rather than recovered. However, such assumptions ignore the possibility that the firm might be able to salvage value from lower quality returns by using them for parts or materials. This thesis presents four models that investigate the importance of considering the quality of returns in the management of inventory in a product recovery system, by examining the cost-effectiveness of recovering both high quality and low quality returns. The first model is a deterministic lot-sizing model of a product recovery system. It was found that performing both high and low quality recovery reduced the sensitivity of the optimal cost to operational restrictions on the choice of decision variables. The second model is a discrete-time, periodic-review model formulated as a Markov decision process (MDP) and introduces uncertainty in demand, returns, and the quality of the returns. It was found that performing both types of recovery can lead to cost savings and better customer service for firms through an increased fill rate. The third model addresses those industries where produced and recovered goods cannot be sold on the same market due to customers’ perceptions and environmental legalisation. Using an MDP formulation, the model examines a product recovery system in which produced and recovered goods are sold on separate markets. The profitability of offering two-way substitution between these markets was investigated. It was found that offering substitution can allow firms to increase both their profits and fill rates. The fourth model examines the issue of separate markets and substitution in the continuous time domain using a semi-Markov decision process. The continuous nature of the model allows more detailed examination of the substitution decision. It was found that offering substitution can allow firms to increase their profit and in some cases also increase their fill rate. In some cases, production is performed less frequently when downward substitution can be offered, and recovery is performed less often when upward substitution can be offered. The findings of this thesis could be used to help a firm that is currently recovering high quality returns assess the cost-effectiveness of also recovering lower quality returns. Recovering low-quality items, rather than disposing of them, may allow a firm to increase the amount it recycles. The findings highlight the importance of considering the quality of returns when managing a product recovery system as they show that economic gains can be achieved by reusing rather than refusing low quality returns.

In today’s global and competitive markets selling products at competitive prices, coordination of supply chain configuration, and environmental and ecological consciousness and responsibility become important issues for all companies around the world. The price of products is affected by costs, one of which is inventory cost. Inventory does not give any added value to products but must be kept in order to fulfill the customer demand in time. Therefore, this cost must be kept at the minimum level. In order to reduce the amount of inventory across a supply chain, coordination of decisions among all players in the chain is necessary. Coordination is needed not only for a two-level supply chain involving a manufacturer and its customers, but also for a complex supply chain of multiple tiers involving many players. With increasing attention being placed to environmental and ecological consciousness and responsibility, companies are keen to have a reverse supply chain where used products are collected and usable components remanufactured and reused in production to minimize negative impacts on the environment, adding further complexity to decision making across a supply chain. To deal with the above issues, this thesis proposes and develops the mathematical models and solution methods for coordinating the production inventory system in a complex manufacturing supply chain involving reverse logistics and multiple products. The supply chain consists of tier-2 suppliers for raw materials, tier-1 suppliers for parts, a manufacturer who manufactures and assembles parts into finished products, distributors, retailers and a third party who collects the used products and returns usable parts to the system. The models consider a limited contract period among all players, capacity constraints in transportation units and stochastic demand. The solution methods for solving the models are proposed based on decentralized, semi-centralized and centralized decision making processes. Numerical examples are used by adopting data from the literature to demonstrate, test, analyse and discuss the models. The results show that centralised decision making process is the best way to coordinate all players in the supply chain which minimise total cost of the supply chain as a whole. The results also show that the selection of the length of limited horizon/ contract period will be one of the main factors which will determine the type of coordination (decentralised, centralised or semi-centralised) among all players in the supply chain. We also found that the models developed can be viewed as generalised models for multi-level supply chain by examining the models using systems of different tiers from the literature. We conclude that the models are insensitive to changes of input parameters since percentage changes of the supply chain’s total cost are less than percentage changes of input parameters for the scenarios studied.

This thesis describes a stochastic ship routing problem with inventory management. The problem involves finding a set of least costs routes for a fleet of ships transporting a single commodity when the demand for the commodity is uncertain. Storage at consumption and supply ports is limited and inventory levels are monitored in the model. Consumer demands are at a constant rate within each time period in the deterministic problem, and in the stochastic problem, the demand rate for a period is not known until the beginning of that period. The demand situation in each time period can be described by a scenario tree with corresponding probabilities. Several possible solution approaches for solving the problem are studied in the thesis. This problem can be formulated as a mixed integer programming (MIP) model. However solving the problem this way is very time consuming even for a deterministic problem with small problem size. In order to solve the stochastic problem, we develop a decomposition formulation and solve it using a Branch and Price framework. A master problem (set partitioning with extra inventory constraints) is built, and the subproblems, one for each ship, involve solving stochastic dynamic programming problems to generate columns for the master problem. Each column corresponds to one possible tree of schedules for one ship giving the schedule for the ship for all demand scenarios. In each branch-and-bound node, the node problem is solved by iterating between the master problem and the subproblems. Dual variables can be obtained solving the master problem and are used in the subproblems to generate the most promising columns for the master problem. Computational results are given showing that medium sized problems can be solved successfully. Several extensions to the original model are developed, including a variable speed model, a diverting model, and a model which allows ships to do extra tasks in return for a bonus. Possible solution approaches for solving the variable speed and the diverting model are presented and computational results are given.

This thesis is an empirical and theoretical analysis of the impact of financing constraints on firm-level investment behaviour. Its primary objectives are to model this impact, and to test the restrictions these models place on the data. Chapter 1 contains a discussion of these themes, and provides an overview of the thesis. Chapter 2 addresses the empirical question of whether innovative firms are financially constrained. To answer this question, several structural investment equations are tested, and the sensitivity of physical investment expenditures to internal finance is compared across innovative and non-innovative firms. The investment expenditures of innovative firms are found to be more sensitive to cash flow than those of non-innovative firms. These results support the hypothesis that innovative firms are financially constrained. The third chapter builds a theoretical model to explain a widely reported fact in the inventory literature, which is that the variance of production exceeds the variance of sales. This fact contradicts a prediction of the standard Linear-Quadratic model of inventory investment, and for this reason is often referred to as the "excess variance of production" puzzle. In this chapter, a model of inventory investment is built. It is shown that when financing constraints are imposed on the model, it can explain the excess variance of production puzzle. In the absence of these constraints, the model does not deliver this result. The fourth chapter returns to the theme of identifying financially constrained firms. A weakness of existing tests of financing constraints is that they are not both direct and structural. This chapter addresses that criticism by constructing a model of investment from which is derived a simple and direct, structural test of the null hypothesis that a group of firms is financially constrained. The test is implemented on a panel of U.S. manufacturing firms. The results support the findings of existing tests.

The career development of construction managers is examined using semi-structured interviews with directors and senior executives of building and/or civil engineering contractors. A postal questionnaire developed and then refined from the interview data was distributed to junior, middle and senior construction managers. A total of 73 completed questionnaires formed the data base for the quantitative analysis. Qualitative and quantitative data were analysed to determine the educational profile of construction managers, their career path and intra and inter firm mobility. Additionally, the reasons for intra and inter firm mobility were analysed. Self-report data and the skills required both presently and in the future is a major component of the study. Consideration of the changing nature of construction management is reported. It is recommended that career development manuals be provided to enable contractors to manage their professional staff. Further research to establish self-perceptions of a career in construction management and the impact on career development is suggested. In addition, following the development of a skills and knowledge inventory, in the present study, it is recommended that further research is needed to determine the type of provision, duration and location of construction management courses.

Inventory record inaccuracy (IRI) often arises in retail environments due to unaccounted stock loss. Theft, misplacement, spoilage, and transaction errors will reduce the true inventory values without changing the inventory record. As previous inventory replenishment policies assume perfect record accuracy, increasing IRI can cause unexpected stockout events, mistimed reorders and replenishment freezes. Solutions to rectifying IRI vary from the use of improved tracking technologies to prevent it initially occurring at all to recounting programs which estimate true inventory value. Unfortunately, in retail environments, high‑tracking technology is unsuitable and continuous counting programs are too costly. To address the limitations of current solutions, we offer a Periodic Replenish and Recount Policy (PRRP) which accounts for stochastic stock loss and minimizes total costs including recounting. The theoretical foundation of PRRP allows for the discovery of both an optimal order quantity as well as optimal count frequency for a given inventory system. We find that in instances of stochastic stock loss, PRRP balances the trade-offs between shortage, surplus and counting costs.

This dissertation is motivated by observations of emerging retail practice and the corresponding customer requirements and behavior. Specifically, it includes the following topics: (1) omni-channel and e-commerce logistics, (2) inventory management, and (3) product strategy and pricing analytics. In the first chapter of the dissertation, we study the shipment consolidation policy facing an increasing frequency of orders per customer. Shipment consolidation (i.e., shipping multiple orders together instead of shipping them separately) is commonly used to decrease total shipping costs. However, when the delivery of some orders is delayed so they can be consolidated with future orders, a more expensive expedited shipment may be needed to meet shorter deadlines. In this paper, we study the optimal consolidation policy focusing on the trade-off between economies of scale due to combining orders and expedited shipping costs, in the setting of two warehouses. Our work is motivated by the application of fulfillment consolidation in e-commerce and omni-channel retail, especially with the rise of so-called on-demand logistics services. Sellers have the flexibility to take advantage of consolidation by deciding when to ship the orders and from which warehouse to fulfill the orders, as long as the orders' deadlines are met. The optimal policies and their structures are characterized. Using the insights of these structural properties, we propose two easily implementable heuristics that perform within 1-2% of the optimal solution and outperform other benchmark consolidation methods in numerical tests. In the second chapter of the dissertation, we study the inventory decision when there are explicit high service-level requirements. We consider a stochastic inventory model (under both backorder and lost-sales) with non-stationary demands, positive lead times, and sequential probabilistic service level constraints. This is a notoriously difficult problem to solve and, to date, not much progress has been made in understanding the structure of its optimal control, especially for the lost-sales inventory system. In this paper, we propose a simple order-up-to control, whose parameters can be calculated using the optimal solution of a deterministic approximation of the backorder inventory system, and show that it is asymptotically optimal for both the backorder and lost-sales systems in the regime of high service level requirement. This result contributes to the growing body of inventory literature that show the near-optimality of simple heuristic controls. Moreover, it also gives credence to the use of deterministic approximation for solving complex inventory problems in practice, at least for applications where the targeted service level is sufficiently high. In the third chapter of the dissertation, we study product strategy and pricing analytics, in settings where customers have both positive and negative product network externalities. One unique feature of luxury products is the coexistence of two opposite externalities: snob customers experience negative externalities with product sales while follower customers experience positive externalities. Motivated by several interesting and (perhaps) counter-intuitive practices in the luxury industry, we study the effect of these two opposite externalities with respect to the selling strategies from three perspectives: 1) the product-line strategy in a monopoly setting, 2) the pricing strategy in a competition setting, and 3) the product bundling strategy. We find that these two opposite externalities generally work in the same direction, although through different mechanisms.

Abstract: Traffic signs play a critical role in the safety and efficiency of any roadway, but with limited information on the current status of traffic sign inventories, the placement and condition of traffic signs goes largely unchecked. Therefore, the collection of a complete traffic sign inventory (TSI) is needed to ensure traffic signs meet the needs of the current driving population. However, the size of current global traffic sign networks makes applying traditional survey methods to the collection of a TSI difficult, if not economically infeasible. Therefore, there is room for technological and methodological advancement to create a new TSI process to inventory and analyze traffic signs. This thesis proposes the use of light detection and ranging (LiDAR) and video-log imaging to conduct an automated extraction of a TSI. The details of traffic sign location, orientation, placement, and panel classification define the fundamental components of a TSI.Traffic signs are extracted through a Gaussian mixture model, density clustered, and filtered for flatness before measuring the vertical and horizontal orientation through principal component analyses. Traffic sign placement is dependent on the location of lane markings. Therefore, the road surface near each traffic sign is extracted, rasterized, and intensity manipulated to determine the linear lane marking intensity edges. The markings are used to determine the lateral and vertical placement of the traffic signs. Sign classification is determined from video-log images by applying a trained GoogLeNet convolutional neural network. This completes the traditional TSI and creates a platform with which to analyze the efficacy of traffic sign placements. Additionally, the detail provided by LiDAR scans allows for the measurement of the visibility of the traffic signs, which is unavailable through traditional surveying methods. This is used to assess the time available to drivers to read and react to traffic signs placed along the segment. A 4-km test segment was utilized to assess the accuracy of the proposed method, providing an Eastbound TSI for 30 traffic signs.The intensity-based extraction of traffic signs had a precision, recall, and F1-Score of 98.3%, 92.06%, and 95.08%, respectively. The extraction of lane markings had a precision, recall, and F1-Score for the left-lane markings of 100%, 89.36%, and 94.38%, respectively. The corresponding values for right-lane markings were estimated as 93.47%, 86%, and 89.58%, respectively. The image classifier had a sample of 13,604 training images spanning 155 traffic sign classes and 10 false positive classes within Alberta. The structure is trained within a half hour on GPU and ~8 hours on CPU and produces 83.6% accuracy on the validation set. This translates to a Top-1 and Top-2 classification error of 10.35% and 3.24%, respectively. However, when applied to the original video-log images, the sliding window procedure used to apply the trained classifier to cropped image samples creates the opportunity for misclassifications across the video-log image. This reduces the accuracy of the classifier to 53.3%. Finally, the visibility assessment considered day and night-time conditions as well as the impact of consecutive placement (i.e. driver’s attention fixated on only the nearest sign). This provides a discussion of how available visibility affects different driving populations and which traffic signs are most susceptible to being missed while driving.This thesis presents a method for the expedited accurate extraction of a TSI, including location, orientation, placement, classification, and visibility. Utilizing the detail available from high-density LiDAR scans, the extractions are completed with a high degree of accuracy and with time benefits over the traditional manual methods. The contributions of this thesis include (i) proposing a method for the extraction of a TSI, (ii) assessing sign visibility, and (iii) creating Canada’s first traffic sign image database. This sets the stage for continued research into the extraction of TSIs, the continued development of a traffic sign image database for Canada, and guidance for industry professionals considering using LiDAR or video-logs for creating a TSI.

Even though flood damage cannot be fully controlled, its effect can be minimized to some extent by careful planning, flood mitigation measures, and an effective flood warning system. Therefore, flood warning systems with flood travel time and inundation area information, derived from accurate model prediction, can be very effective to reduce potential flood damage. While a one-dimensional (1D) model was developed in the former research for the flood warning system, there has not been many comparative assessment of model performance among 1D, two-dimensional (2D), and coupled one-dimensional and two-dimensional (coupled 1D/2D) models particularly in HEC-RAS. Therefore, this research is an extension of the prior research and was especially conducted to calculate and compare the predictive capability of 1D, 2D, and coupled 1D/2D HEC-RAS models for the computation of travel time of flood and extent of flooded area needed for a flood warning system. The research was carried out in the Grand River in Lake County, Ohio. The model performance of 1D, 2D and coupled 1D/2D models were evaluated and sensitivity analysis was conducted using the same set of flow conditions and geometric conditions. The analysis suggested that 2D model could incredibly improve the model performance compared to 1D and coupled 1D/2D models, which were evaluated through the model evaluation indicators for the observed and simulated model outputs. Additionally, sensitivity analysis of input parameters, including discharge and Manning’s roughness, revealed that the 2D model was comparatively less sensitive to the changes in model inflow and Manning's roughness compared to the coupled 1D/2D and 1D models. Furthermore, the flood travel time computed using 1D model was more predicted than that of the 2D model, indicating that the 2D model would be most appropriate to provide a safe evacuation time for the community before flood events. The 1D model consistently over predicted than that of the 2D model, which was also true for the estimation of the inundation flood zone (4.1% higher).In addition, the appropriate assessment of flood damage in the aftermath of major flooding is crucial for flood management agencies, emergency responders, and insurance companies. Therefore, damage assessment is an important step in the evaluation of the flood mitigation measures, vulnerability analysis and flood risk mapping. This is particularly true in a context that the damage assessment so far has been primarily relying on either the coarse resolution, 30m digital elevation model (DEM), or 1D hydraulic model. As this researcher is not aware of any explicit incorporation of 2D HEC-RAS model for the damage assessment among the scientific communities, another major objective of this analysis is to outline the effects of some of the key factors including the mode of hydraulic simulation (1D vs 2D), the effect of inventory data, and the effect of topography on the flood loss estimation. This was accomplished using the 1D and 2D HEC-RAS models to produce the flood depth grids from the varying degree of topographic resolutions including 30m, 10m and LiDAR-derived DEM with and without incorporating actual field survey of the river in each case. The flood loss was estimated using Hazards United States Multi-Hazards (HAZUS-MH) loss estimation software developed by Federal Emergency Management Agency (FEMA) software, for each building within study area for flood events of various recurrence intervals from 10 to 500-year return periods. This was accomplished by updating the default-building inventory within Lake County to represent the actual building information in the model. The analysis indicated that 1D model consistently overestimated the loss in general by 61.48% for the default database and 86.12% for updated inventory. The estimation of the 1D model was consistently larger compared to the 2D model for different set of topographic resolutions and recurrence intervals. These loss estimations significantly increased when analyzed using a coarse resolution terrain, which was true regardless of selecting 1D or 2D models. Furthermore, the 2D model showed a lesser percentage increase (10.45% in 10m DEM, and 25.49% in 30m DEM), whereas the 1D model exhibited a larger increment (23.17% in 10m DEM and 76.81% in 30m DEM). This analysis suggested that the loss estimation would decrease in general by 76.21% after incorporating additional local building data into the HAZUS-MH database. More specifically, this analysis concludes that 2D model with high-resolution topographic data, including the additional incorporation of local data, in HAZUS-MH database are tremendously essential for appropriate flood damage assessment.

The purpose of this study is to explore the concept of ‘persistence’ (or consistency) in firms’ operating performance. In this respect, the relationship between operating persistence and firm performance is the central focus of this study. While ostensibly both the level and persistence of operating performance are important to firms, given that firms often have to make a choice between these two elements, the study aims to enable businesses to identify situations where focusing on improving operating persistence may be more beneficial compared to improving the absolute level of operating performance in order to attain greater financial performance. In addition, the study seeks to understand the relationship between environmental dynamism, operating persistence and firm performance, and how operating slack buffers of excess inventory and capacity impact these relationships, which leads to a cost-benefit case for having these buffers. To accomplish these aims, the study uses rolling regression analysis on secondary data gathered for the period from 1990 to 2016. An important insight from this study is that it emphasizes businesses to become more consistent if they are making inconsistent ‘supernormal’ profits than focusing on the level of operating performance since such firms tend to have poor translation between operating performance and firm performance. Furthermore, the study identifies that capacity buffers are better suited to reducing the adverse effects of environmental dynamism on operating persistence and firm performance, as compared to inventory buffers.

The impact of public reports on price fluctuations has been widely investigated in many commodity markets, but little attention has been paid to the lumber market. In this thesis, we examine the impact of two housing market reports, namely the New Residential Construction (Housing Starts) and the New Residential Sales reports, on the U.S. lumber futures market. Our results suggest that the housing starts report does indeed affect lumber market volatility, while the New Residential sales report exerts a minor impact on lumber price volatility. Price volatility is measured by changes future contract prices for lumber. We further find that the effect of the two reports on volatility differs depending on the level of lumber inventory and the nature of the news. When inventory is low, larger-than-expected housing starts have the largest effect on lumber volatility. During periods of abundant inventory, lower-than-expected housing starts increase the volatility most. For the new home sales reports, we find that while lower-than-expected sales do not affect the volatility of lumber prices, larger-than-expected sales do increase the volatility.

The purpose of this study is to conduct baseline inventories of breeding birds and bat activity for the Dairymen’s Inc. property. In addition, I compared the breeding bird communities of two habitats, black ash swamp and alder thickets. I conducted line transects and point counts to collect data on the breeding birds. For bat activity levels, I conducted acoustic point counts on the Dairymen’s lakes. The data from this study, was used to predict the outcome of the white-nose syndrome and emerald ash borer becoming part of the landscape. The inventory data will be used to create a baseline for management practices and to monitor effects of environmental threats.

Production of natural gas, natural gas liquids and oil in the US has increased over the last 10 years. The increase is attributed to the development of two complementary technologies: directional drilling and hydraulic fracturing. However, the new energy development also expanded environmental impacts, including methane emissions, which drive radiative forcing of the atmosphere and global warming. Extensive measurements of methane emissions have been conducted over the past five years, and this work synthesizes this new information to create a comprehensive, spatially and temporally resolved inventory of methane and light hydrocarbon emissions from oil and gas production, using the Eagle Ford Shale production region as a case study. The inventory includes emissions of methane, ethane, propane, and butane from 12 emission sources (chemical injection pumps, compression systems, dehydrators, pneumatic controllers, liquid unloading, completion flow backs (pre-production), super-emitter sources, condensate flashing, water flashing, equipment leaks, processing plants, and compression stations). Total estimated methane emission are 64,002 kg/hr (95% CI: 58,380 kg/hr – 71,730 kg/hr), which constitute 1.2% of the methane produced in the region. The main contributors are flashing from condensate tanks, pneumatic controllers and mid-stream sources.

The biopharmaceutical industry now is entering a more mature stage of its existence. In order to prepare for increasingly fierce competition, firms are beginning to shift their focus from developing new technology to improving manufacturing operations. However, the nature of biomanufacturing poses many unique challenges for the industry. In this thesis, we consider three closely related inventory models to address the challenges these firms are facing. In the first model, we consider an inventory planning model with batch differentiation. Batch production is a natural constraint in biomanufacturing and the decision of how many batches to produce and how to differentiate the batches is crucial. In this work, we propose a series of heuristic algorithms using the idea of certainty equivalent control which converts a difficult stochastic integer programing to a deterministic problem. We show that our heuristic algorithms perform extremely well when the demand variation is small and we also explore how the trade-off between demand information and decision dynamics affects the performance. In the second model we combine the inventory model with a queuing model to address the tactical level supply chain management and coordination issue between the manufacturer and a third party contractor. Small biopharma firms usually outsource the filling/labeling/packing operations to a third party contractor. As a consequence, the firm experiences large uncertainty in the time until the outsourced order is returned. We show that a capacity reservation contract that places an order at fixed intervals with a capacity constraint can outperform the traditional inventory management $(r,q)$ policy by not only reducing the inventory cost at the firm, but also increasing the total profit of the entire system. In the third model, we extend the traditional process flexibility literature by integrating inventory capabilities and give general guidelines on how to design an effective supply chain network. Given the expensive inventory storage capability in the biopharmaceutical industry, firms are often interested in incorporating flexible plants that can produce multiple types of product into an existing dedicated supply chain to better respond to demand uncertainty. We develop models that help the firm explore many important strategic issues, such as whether a new plant should be flexible or not, and whether and which plants should be modified to have inventory capabilities.

Attempts have been made to understand the complex dynamic relationships involved in team sports and explain the nature of successful performance. In sports, these components are particularly relevant because athletes often attribute peak performances and outcomes to psychological states such as team flow. This thesis focused on exploring team flow state as an independent construct whose dimensions need to be identified by research. The purpose of this thesis was to conceptualize team flow state and develop an inventory to measure it.

This thesis proposes the Restaurant GHG Guideline, a holistic protocol, to document and assess the greenhouse gas emissions generated by processes that occur both directly and indirectly in the operation of a restaurant. Existing greenhouse gas (GHG) accounting protocols either have a narrow focus on emissions from processes that occur directly on the site of the building and indirectly as a result of purchased energy consumption on site or offer only general guidance for identifying emissions sources throughout organizations’ supply chains. For restaurant operations, many offsite processes are necessary to produce goods or services that are critical to their economic success, and therefore carry much weight in management decisions. By including emissions sources throughout a restaurant’s supply chain, this guideline identifies significant hot-spot emissions sources. It provides calculation methods for identifying GHG emissions generated at the scale of individual components, creating a more effective inventory for operators to develop targeted reduction initiatives. Historic operational data from a test case restaurant is used to illustrate how the specificity of the tool can help restaurant operators identify GHG emissions hot-spots at the level of individual components. By utilizing this guideline to identify these emission sources, restaurant operators can then create targeted reduction strategies.

Emissions of greenhouse gases from the combustion of fossil fuels, in particular carbon dioxide (CO2), are a major contributor to global climate change. In the United States 28% of carbon dioxide emissions from fossil fuel combustion are produced by road vehicles. This dissertation reports the results of three studies that improve on our knowledge of the spatial and temporal distribution of vehicle CO2 emissions in the U.S. over the last 35 years. Using bottom-up data assimilation techniques we produce several new high-resolution inventories of vehicle emissions, and use these new data products to analyze the relationships between emissions, population, employment, traffic congestion, and climate change at multiple spatial and temporal scales across the U.S. We find that population density has a strong, non-linear effect on vehicle emissions, with increasing emissions in low density areas and decreasing emissions in high density areas. We identify large biases in estimates of vehicle CO2 emissions by the most commonly used national and global inventories, and highlight the susceptibility of spatially-downscaled inventories to local biases in urban areas. We also quantify emissions of several air pollutants regulated by the U.S. Environment Protection Agency, including carbon monoxide, nitrogen oxides and particulate matter, at hourly and roadway scales for the metropolitan area surrounding Boston, MA. Emissions of these pollutants show high emissions gradients across identifiable spatial hotspots, considerable diurnal and seasonal variations, and a high sensitivity to the presence or absence of heavy-duty truck traffic. We also find that the impact of traffic congestion on air pollution emissions across the region is minimal as a share of the total emissions. We show that policies that combine a reduction in the number of vehicles on the road with a focus on improving traffic speeds have greater success in reducing emissions of air pollutants and greenhouse gases than policies that focus solely on improving traffic speeds. Finally, we estimate that regional emissions of carbon monoxide will increase by 3% in 2050, but with numerous localized increases of 25-50%, due to an expected rise in mean regional temperatures due to global climate change.

The efforts made towards deploying a mobile robotic system at Los Alamos National Laboratory are detailed in this thesis. The platform application is non-contact tasks related to inspection, inventory, and radiation surveying. It is intended for a Special Nuclear Material storage facility featuring a high radiation environment and a variety of storage modes. New robotic capabilities have been developed using several mobile platforms to address the requirements of this application. Many of challenges are common to any warehouse application, such as autonomous task planning, vision, navigation, and inventory data management. Others are specific to a nuclear laboratory environment, such as radiation measurement and analysis, response to radioactive contamination, criticality safety, and restrictive security measures. This thesis describes the progress made towards meeting these challenges, outstanding issues, and future work that is necessary to complete the project. Nuclear facilities are under ever-increasing demands to reduce worker radiation exposure. Since the vault is a high radiation area, it is one of the first targets at Los Alamos for the application of novel solutions. The deployment of this system promises to enhance worker safety by reducing their presence inside the vault and therefore total occupational dose. As robotic systems become more trusted in the nuclear weapons complex, it also has the potential to reduce total operator labor by performing time-consuming tasks autonomously.

The long-term permanency and profitability of a firm requires decisions to be made wisely and on time. For this purpose, it is essential to consider all aspects of a decision in terms of its impact on revenue, planning, scheduling, and inventory in an integrated framework.In this research paper, an interactive intelligent decision support system for making an integrated decision in the presence of demand uncertainty is proposed. The system operates in a multi-product, multi-period setting, and its objective is to maximize the profit of the firm over time. To achieve its objective, the system first obtains the optimal price and capacity plan for the coming periods. The output of this first step becomes the input for the second step, in which the problem of scheduling is solved. At the end, based on the scheduling step, the optimum inventory policy is determined.To cope with demand uncertainty in the pricing and planning phase, a robust optimization model is proposed in which the demand is considered to belong to an interval and there is no knowledge (such as statistical distribution) associated with thedemand. The robust problem is solved using a metaheuristic.During the scheduling step, a general setting for the problem is considered, in which each product is treated like a project with a flow network. To address the problem of scheduling, a simulation optimization method is applied in which the optimization step determines the dispatch rule of the jobs and the simulation step schedules the dispatched jobs on the production line.During the inventory step, the system obtains the best schedule for ordering and storing the raw material in order to minimize the inventory cost. For this purpose, a mixed integer mathematical model is proposed and a metaheuristic is applied to obtain the best solution.All modules of the proposed decision support system are supported with a database that stores the data obtained from the shop floor and the market. This database is used to assess the costs and parameters in models by applying a cost estimation support system.To evaluate the effectiveness of the proposed decision support system, it has implemented in a small size textile production line. The data generated by the system and its users are analyzed for a period of four months. Four indicators of profit per product, overall equipment effectiveness, percentage of realized schedule and work-in-progress are monitored during these four months and their values are compared against the same time period in previous year. The results show that the system has improved in terms of profitability, equipment effectiveness and production line control. However, the work-in-progress has not improved.

Retail Suppliers constantly face the issue of high inventory costs or high backlog costs.Thisstudy deals with a Retail Supplier who mainly supplies protective gear which are manufactured overseas (mainly Asia). This increases the complexity of the whole order-supply procedure. The products analyzed in this study are all products with high demand variability and volatility. The delivery lead times of the product are also unpredictable. The forecasting technique used by the Supplier does not take into account the demand variability. The Supplier also targets an average inventory turnover of 4 or higher for theseproducts. A simulation model is built in ARENA based on the Suppliers data for three main purposes a) To evaluate output performance characteristics of the current system. b) To assess if the Retail Supplier's target inventory turnover for the product under evaluation is an achievable target and a correct choice. c) To optimize the model to get the optimal order up to inventory quantities in order to minimize the total costs. The costs considered take into account holding costs, shortage costs and ordering costs. The three tests are conducted on the current system as well as the system introduced with the lead time variabilitybased on new data that was retrieved.The Newsvendor model is used to take into account the demand variability and serves as the basis for the demand module in the model[6]. The inventory policy used for the order-supply module is an order up to inventory system.The simulation model seeks to give optimized order up to inventory levels with minimum costs taking into account the lead time variability and target inventory turnovers using a heuristic optimization tool -OptQuest. The study provides an evaluation of the current system and an optimized solution for the values of the order up to levels. The simulation model also helps in concluding that a target inventory turnover of 4 or greater may not be the best for a D,E inventory categorized product.

The ability to accurately map and monitor forest carbon (C) has gained global attention as countries seek to comply with international agreements to mitigate climate change. However, attaining precise estimates of forest C storage is challenging due to the inherent heterogeneity occurring across different scales. To develop cost-effective sampling protocols, there is a need for more unbiased estimates of the current C stock, its distribution among forest compartments and its variability across different scales. As a contribution to this work, this dissertation used high-resolution field measurements of C collected from different forest compartments across a boreal forest stand in South East Norway. In the first paper, we combined the use of airborne scanning light detection and ranging (lidar) systems with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools across the forest. We found that predictor variables from lidar derived metrics delivered precise models of above and belowground tree C, which comprised the largest of the measured C pool in our study. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. In the search for an effective tool to measure and monitor forest C pools, we found the capabilities of lidar to map forest C encouraging. In the second paper, we used a geostatistical approach to analyze the fine-scale heterogeneity of the soil organic layer (forest floor) C storage. Our results showed that the C stocks were highly variable within each plot, with spatial autocorrelation distances < 3 m. Further, we established that a minimum of 20 to 25 inventory samples is needed to determine the organic layer C stock with a precision of �0.5 kg C m-2 in inventory plots of ~2000 m2. In the third paper, we investigated how the short-range spatial variability of organic layer C affects sampling strategies aiming to monitor and detect changes in the C stock. We found that sample repeatability rapidly declines with sample separation distance, and the a priori sample sizes needed to detect a change a fixed change in the organic layer C stock vary by a factor of ~4 over 15 to 125 cm separation distance. Unless care is taken by the surveyor to ensure spatial sampling precision, substantially larger samples sizes, or longer time intervals between baseline sampling and revisit are required to detect a change. In the final paper, we utilized the nested sampling protocol to investigate the spatial variability of organic layer C across different scales and incorporated inventory expenses in the development of a cost-optimal sampling approach. Because precise estimates are costly to obtain, it is of great interest for surveyors to develop cost-efficient sampling protocols aimed at maximizing the spatial coverage, while minimizing the estimate variance. We found that the majority of the estimate variance is confined within small subplots (100 m2) of the forest (25 km2), emphasizing the importance of considering the short-range variability when conducting a large-scale inventory. Further, this chapter demonstrated how optimal allocation of sampling units (plot, subplot and sample) is not only a function of the variance component within that dimension, but also changes with the sampling unit costs and the acceptable margin of error. We found that the costs of conducting an organic layer C inventory could be reduced by more than 60% by increasing the inventory uncertainty from �0.25 Mg C ha-1 to �0.5 Mg C ha-1. Finally, we established that sampling costs can be reduced with as much 80% by conducting a double sampling procedure that utilizes the correlation between organic layer C stock (r = 0.79 to 0.85) and measurements of layer thickness.

The purpose of this study was to develop and validate a five factor model (FFM) measure of forgiveness. Participants were 545 undergraduates currently enrolled in introductory and experimental psychology courses at the University of Kentucky (272 used for item construction, 273 for scale validation). Items were selected on the basis of convergent validity with five-factor model personality scales and forgiveness scales. Participants in the validation stage were administered the resulting Five Factor Forgiveness Inventory (FFFI), as well as the International Personality Item Pool-NEO (IPIP-NEO) and seven other existing forgiveness measures. Significant convergent validity was obtained for the vast majority of the seven FFFI subscales with their corresponding IPIP-NEO facet scales. Discriminant validity was good to excellent for all subscales, as well. FFFI subscales were all significantly correlated with the seven existing forgiveness measures, and the FFFI total score obtained significant incremental validity over all seven other forgiveness scales. Finally, multiple regression analyses revealed that FFFI scales accounted for additional variance in predicting results on existing forgiveness measures. The initial construction and validation of this measure provides a foundation for assessing forgiveness from the perspective of the FFM, thus providing a more comprehensive, nuanced understanding of the disposition to forgive.

Environmental compensation includes a range of activities intended to counterbalance such negative impacts of development projects that remain in the environment after all preventive and corrective measures have been fully implemented. Sweden, being a member state of the European Union (EU), must implement environmental compensation under EU directives such as the Habitat Directive. However, like in other countries, implementation is not yet widespread in Sweden, and new practices and guidelines remain to be developed both nationally and at European level. This need is all the more urgent considering that the European Commission estimates that, within the EU, about 100,000 hectares of land is converted from its natural state each year. The aim of this paper is to describe current environmental-compensation practices in Swedish road and railway projects and to discuss issues of vital importance to the development of compensation policy, such as what to compensate for, how much, and how. A national inventory was performed, for the first time in Sweden, to identify compensation measures in road and railway projects. Data were collected from a national mailing list including 141 officials at county administrative boards (CABs), internal e-mail correspondence within the Swedish Transport Administration and databases of court decisions. The inventory focused on compensation measures ordered by virtue of the Swedish Environmental Code. In addition, two case studies were carried ...

—Despite efforts by researchers worldwide to assess the biodiversity of plant groups, many locations on Earth remain poorly surveyed, resulting in inadequate or biased knowledge. Robust estimates of inventory completeness could help alleviate the problem. This study aimed to identify areas representing gaps in current knowledge of African palms, with a focus on Benin (West Africa). We assessed the completeness of knowledge of African palms, targeting geographic distance and climatic difference from well-known sites. Data derived from intensive fieldwork were combined with independent data available online. Inventory completeness indices were calculated and coupled with other criteria. Results showed a high overall value for inventory completeness, as well as an even distribution of well-known areas across the country. However, poorly-known areas were identified, which were in remote locations with low accessibility. This study illustrates how biodiversity survey and inventory efforts can be guided by existing knowledge. We strongly recommend the combination of digital accessible knowledge and fieldwork, coupled with expert knowledge, to obtain a better picture of inventory completeness in tropical ecosystems.

With university enrollment increasing, campuses are becoming under increased strain for parking. More students results in more cars on an already stressed parking infrastructure. In many instances, university parking management offices are being asked to accommodate this increase in parking without the construction of new parking infrastructure. In order to accommodate these demands, better parking management is required. This research develops a framework to evaluate current parking management practices and pinpoint methods to improve upon current practices. The framework is generalized for use by any size and type of university. Clemson University's campus in Clemson, SC was used as a trial site for this framework. This study evaluates Clemson University's Parking and Transportation office and the policies used to justify parking changes. The framework uses interviews, inventory and utilization data to evaluate the need for parking changes on campus. A documentation of this process is also created in this research. This case study can serve as a guide to university campuses undergoing significant growth, management change, or universities who simply want to improve parking management. It also provides insight into the collection of parking inventory and utilization data. The framework was developed in a small to medium sized university outside of an urban area. However, the framework can be tailored for use at any university regardless of size or location.

As computing becomes ubiquitous, software correctness has a fundamental role in ensuring the safety and security of the systems we build. To design and develop software correctly according to their formal contracts, CS students, the future software practitioners, need to learn a critical set of skills that are necessary and sufficient for reasoning about software correctness. This dissertation presents a systematic approach to both introducing these reasoning skills into the curriculum, and assessing how well the students have learned them. Specifically, it introduces a comprehensive Reasoning Concept Inventory (RCI) that captures the fine details of basic reasoning skills that are ideally learned across the undergraduate curriculum to reason about software correctness, to develop high quality software, and to understand why software works as specified. The RCI forms the basis for developing learning outcomes that help educators to assess the adequacy of current techniques and pinpoint necessary improvements. This dissertation contains results from experimentation and assessment over the past few years in multiple CS courses. The results show that the finer principles of mathematical reasoning of software correctness can be taught effectively and continuously improved with the help of the RCI using suitable teaching practices, and supporting methods and tools.

While genetic algorithms have been explored academically, and in at least two known cases, commercialized, they still seem to be an underutilized technique for solving engineering management problems. One of the reasons for this is the general lack of awareness and understanding about genetic algorithms. Another reason is that genetic algorithms have generally been viewed as being too costly in terms of computing resources to implement for complex problems. This paper examines an example management problem and an example genetic algorithm that was developed to solve it in order to illustrate the applicability of such algorithms to similar or more complex management problems. In an experiment described in this paper, a Java-based genetic algorithm package was used to implement an evolutionary process for finding optimal solutions to a selected aggregate planning problem. The performance and results of the process were compared to solutions to the same problem found using manual and non-evolutionary automated techniques. The results found using the experimental evolutionary process surpassed those obtained by the other techniques, both in terms of speed and optimization.

This dissertation examines the relationship between inventory cost (IC) and transportation cost (TC). The association of the two has been recognized for over a hundred years; accordingly, managers and academics have believed that these costs have offsetting properties such that combinations of inventory and transportation expenditures can be altered to fit different strategic objectives. However, the behavior of this relationship in practice has not been tested. The essays of this dissertation examine this relationship through multiple theoretical lenses and with multiple data sets. Essay 1 examines aggregate IC and TC in the United States from 1960-2009. This period coincides with the recognition of total cost management and the ensuing practice of inventory and transportation tradeoff. Essay 1 provides new insight into the macroeconomic relationship of IC and TC by providing a model of their relationship. Examination reveals distinct differences before and after transportation deregulation in the United States. Essay 2 continues the investigation of the relationship between IC and TC, this time using firm-specific, panel data that spans a five-year period from 2006-2011. Inventory theory is used to identify variables and build a model that forecasts firm inventory. To this base model, TC is added. Findings support the research in Essay 1 by confirming that firms do show indication of balancing IC and TC. Essay 3 examines the use of transportation benchmarking information and how it affects firm performance. Based on information processing theory, the impact of transportation benchmarking information on a firm's ability to reduce transportation cost (TC) is examined. A variable is created that is the ratio of a firm's transportation expenditure to the total transportation expenditure in the benchmarking consortium. Panel data is used to test the impact of this ratio on a firm's ability to reduce transportation costs. Empirical analysis shows that transportation costs are convex in the ratio, and the results support the efficacy of transportation expenditure benchmarking.

There is abundant anecdotal evidence substantiating Mori’s initial observation of the "uncanny valley", a point at which human response to non-human entities drops sharply with respect to comfort (Mori, 1970), and the construct itself has a long-standing history in both Robotics and Psychology. Currently, many fields such as design, training, entertainment, and education make use of heuristic approaches to accommodate the anticipated needs of the user/consumer/audience in certain important aspects. This is due to the lack of empirical substantiation or, in some cases, the impossibility of rigorous quantification; one such area is with respect to the user’s experience of uncanniness, a feeling of "eeriness" or "wrongness" when interacting with artefacts or environments. Uncanniness, however, continues to be defined and measured in a largely subjective way, and often after the fact; an experience or product’s uncanny features are pointed out after the item has been markedly avoided or complained about by the general public. These studies are among the first seeking to determine a constellation of personality traits and physiological responses that incline the user to have a more frequent or profound "uncanny" reaction when presented with stimuli meeting the criteria for a level of "eeriness". In study 1, 395 adults were asked to categorize 200 images as uncanny, neutral, pleasant, or other. In Study 2, physiological and eye-tracking data was collected from twenty two adults as they viewed uncanny, neutral and pleasant images culled from study 1. This research identifies components of the uncanny valley related to subjective assessment, personality factors (using the HEXACO and Anthropomorphic Tendencies Scale), and biophysical measures, and found that traits unique to Emotionality on the HEXACO inventory, compounded with a form of anthropomorphism demonstrates a level of relationship to the subjective experience of uncanny stimuli. There is evidence that HEXACO type and forms of anthropomorphic perception mediates the biophysical iv expression and the subjective perception of the stimuli. In keeping with psychological hypotheses, stimuli to which the participants had greatest response centered on death, the threat of death, or mismatched/absent facial features.

In 2008, the State of Florida established a recycling goal of 75% to be achieved by 2020. In response to the Florida goal Orange County (OC), Florida has made the development and implementation of an efficient strategy for landfill diversion of its solid waste a top priority. The Florida Department of Environmental Protection (FDEP) estimated that 23 % of municipal solid waste was generated by construction and demolition (C&D) activities in 2009, with only 30 percent of C&D debris being recycled. Therefore, OC decided to create a solid waste integrated resource plan (SWIRP) initially focused on the recovery and recycling of C&D materials (2010). For SWIRP development, OC decision makers need the best available data regarding C&D debris generation and composition and an understanding of the potential markets available for recycled materials. In this investigation debris generation was estimated over the period of 2001 to 2009 for the largest single governing body within OC, unincorporated OC (UOC), representing 65 percent of county population. The debris generation model was constructed for years 2001-2010 using area values for C&D activities in six sectors obtained from building permits and debris generation multipliers obtained from literature values. The benefit of the model is that as building permit information is received, debris generation estimations can also be expediently updated. Material composition fractions obtained from waste characterization studies of landfills in the Central Florida area were applied to the debris generation model resulting in a material iv composition for all sectors for years 2001-2010. The material composition of the debris stream was found to be, on average, concrete (53%) drywall (20%), wood (12%), a miscellaneous fraction (8%), asphalt roofing material (4%), metal (2%), cardboard (1%) and carpet and padding (1%). A market analysis was performed for concrete, drywall, wood, asphalt roofing shingles and residual screened materials (RSM). It was found that statewide, markets existed for 100 percent of the materials studied and could replace significant amounts of natural material feedstocks, but that the development of more local markets was vital to meeting OC’s diversion goal to minimize the cost of transporting recyclables.

This dissertation consists of three assays. The first assay examines the robustness of lead time demand models for the continuous review (r;Q) inventory policy. A number of classic distributions as well as distribution selection rules are examined under a wide variety of demand conditions. First, the models are compared to each other by assuming a known demand process and evaluating the errors associated with using a different model. Then, the models are examined using a large sample of simulated demand conditions. Approximation results of inventory performance measures - ready, rate excepted number of backorders and on-hand inventory levels are reported. Results indicate that distribution selection rules have great potential for modeling the lead time demand. Incorporating distributions that preserve higher moment information into an inventory control system to determine the desired performance measures is a challenging task. One difficulty in applying such distributions is estimating the parameters from the data. In most cases only the demand per period is available. Thus, the demand per period moment data must be combined with the knowledge of the lead-times to represent the moments of the lead-time demand. The other difficulty lies in deriving closed form expressions that utilize an appropriate parameter fitting procedure. The second assay addresses these challenging issues by utilizing new parameter fitting strategies. The experiment results, collected under across a large number of simulated demand conditions, indicate that the models that preserve more flexible distributional form yield more accurate inventory performance measure results. The focus of the third assay is to develop generic simulation optimization techniques based on sample average approximation (SAA) in order to set policy parameters of classical inventory systems having constrained service levels. This work introduces a policy optimization procedure for the continuous review (r;Q) inventory system having a ready rate service level constraint. Two types of SAA optimization procedures are constructed based on sampling from two different simulation methods: discrete-event and Monte-Carlo simulation. The efficiency of each sampling method is evaluated through a set of experiments under a compound Poisson demand process. In addition, the applicability of the proposed optimization procedure to the other re-order type inventory systems is discussed.

'Lean Manufacturing' is a production practice that aims to reduce shop floor inventory in a manufacturing firm. Its origins extending deep into Japanese history, manufacturing works on tight material control policies and aims to achieve as close to 'stockless production' and 'zero inventories' as possible. The goals of Lean Manufacturing or the seven zeroes (zero defects, setup time, breakdowns, handling, lead time and surging) hence all strive to achieve zero inventory by producing what is needed when needed, thereby eliminating storage costs and capital tied up in inventory. The history of manufacturing in America is explored and compared to that of far eastern societies. This thesis then introduces the development of JIT, Lean Manufacturing and their immediate vii effects on the world. Techniques are explored for implementing lean methods in an automobile manufacturing firm – whether (and how) an automobile manufacturer can bring down his costs using the same and what changes will need to be done to their existing manufacturing processes. Advantages, disadvantages and possible setbacks one may encounter while attempting to implement lean methods are also explored.

Oceanic islands, and the processes by which they are colonised from mainland source populations of flora and fauna, have been a major component of biogeographical research for over half a century. This study addresses the biogeographical relationship between the coastal dune insect fauna of mainland New Zealand (NZ) and those of Chatham Island (CI), an outlying oceanic island some 850 km distant across open ocean. I conducted a comprehensive entomological survey of Coleoptera, Diptera and Hymenoptera from the coastal dune habitat of the NZ east coast and CI. Over 5000 insect samples were collected, consisting of over 650 species. Comparison of the NZ and CI insect communities using the survey data showed that CI has a lower number of insect species per site than NZ. The degree to which the CI diversity was lower approximately equated with the relative dune area available in NZ and CI. Re-sampling techniques were used to create a null hypothesis assuming random immigration from NZ to CI. The observed number of taxa found on CI was compared to the null hypothesis. CI was found to have significantly fewer Hymenoptera and significantly more Diptera than NZ. The likelihood of a specific taxon immigrating successfully varied considerably between taxa. The higher proportions of Diptera and lower proportions of Hymenoptera on CI appeared to be driven by a few very successful or unsuccessful taxonomic groups. There was no clear effect due to taxonomic order on immigration likelihood. Factors which were deemed to have a potential effect on immigration success were modelled using GLM models. Abiotic and biotic effects were modelled against immigration likelihood. The factor most influencing immigration success were the relative difference in mean temperature, taxa with an NZ range which included sites of a similar mean temperature to the CI had a higher the probability of successful immigration to CI. Immigration success was also found to be significantly influenced by the number of sites a taxon had been collected from. The more widely distributed taxa were the most successful immigrants. Genetic analysis of mitochondrial cytochrome oxidase I sequences from six taxon groups showed that genetic divergence has occurred between NZ and CI populations. All but one of the taxon groups have a distinct monophyletic CI population. Two of the CI populations appear to derive from a NZ SI population. Randomisation was also used to detect whether the barcoding gap between NZ and CI populations varied significantly from that of randomised samples. For all the taxa tested the genetic divergence between NZ and CI populations was significantly different from a randomly determined sample pool. The results of this project provide valuable information for the ecology and conservation of NZ dune systems and extend the entomological knowledge of these rapidly declining areas of habitat and have given insight into the biogeographical relationship between NZ and CI dune insect populations and the factors which influence immigration between NZ and CI.

Abstract The List as Treasury in the Greek Worldby Athena E. KirkDoctor of Philosophy in Classics University of California, Berkeley Professor Leslie V. Kurke, Chair Some of the earliest written records in the greater ancient world are lists of objects: we find catalogues of gods, kings, jewels, archaic vocabulary items, and exotic birds in Sumerian, Egyptian, Akkadian and Hittite, and many scholars surmise that a penchant for this kind of record-keeping fueled the very invention of writing. The Greeks, however, have long been considered distinct from other literate peoples both for their innovations with regard to the writing system they borrowed from the Phoenicians and for their application of that system, as they (a) were the first to denote vowels with stand-alone symbols, and (b) seem to used the alphabet to record poetry, not archival information, before anything else. In fact, it is not until several hundred years after these first `literary' texts that the alphabetic Greeks begin to produce the government inventories, war memorials, or tribute lists akin to those of their Near Eastern and Mycenean predecessors. In this project, I study these kinds of official epigraphic written lists alongside lists from Archaic and Classical Greek literature in an effort to reorient the discourse surrounding the Greeks' literacy and use of writing, and its purported uniqueness. I work specifically with those lists that enumerate physical objects, beginning from the assertion that we can trace a tradition of listing objects in the Greek world that exists independent of the literacy versus orality binary invoked by most scholarship for the last several decades. By looking at, e.g., a catalogue of gifts in the Iliad alongside an inventory of dedications from an Athenian sanctuary, I suggest that lists themselves are the salient phenomenon to be identified and analyzed, rather than the medium (written or oral) in which we find them. My central thesis is that Greek object-lists in their disparate contexts--oral poetry, narrated prose history, publicly displayed records, performed drama--all share a common function vis-à-vis the objects they represent, namely, that when they are presented to their various audiences, they serve as surrogates for the objects in question and in many cases take on an authority beyond that of any physical collection, which ultimately perishes. In their role as extant text-monuments, I argue, they embody and preserve the details of remote times and spaces. I present four case studies of texts that contain lists from the archaic through the classical period, and one later example of the same tradition. The chronological progression emphasizes how the Greek literary and documentary traditions build upon and interact with one another, and by attending to the two together, I begin to build a more comprehensive portrait of the listmaking meme.

In today's competitive business environment, companies face enormous pressure and must continuously search for ways to design new products, manufacture and distribute them in an efficient and effective fashion. After years of focusing on reduction in production and operation costs, companies are beginning to look into distribution activities as the last frontier for cost reduction. In addition, an increasing number of companies, large and small, are focusing their efforts on their core competencies which are critical to survive. This results in a widespread practice in industry that companies outsource one or more than one logistics functions to third party logistics providers. By using such logistics expertise, they can obtain a competitive advantage both in cost and time efficiency, because the third party logistics companies already have the equipment, system and experience and are ready to help to their best efforts. In this dissertation, we developed an integrated optimization model of production, inventory and distribution with the goal to coordinate important and interrelated decisions related to production schedules, inventory policy and truckload allocation. Because outsourcing logistics functions to third party logistics providers is becoming critical for a company to remain competitive in the market place; we also included an important decision of selecting carriers with finite truckload and drivers for both inbound and outbound shipments in the model. The integrated model is solved by modified Benders decomposition which solves the master problem by a genetic algorithm. Computational results on test problems of various sizes are provided to show the effectiveness of the proposed solution methodology. We also apply this proposed algorithm on a real distribution problem faced by a large national manufacturer and distributor. It shows that such a complex distribution network with 22 plants, 7 distribution centers, 8 customer zones, 9 products, 16 inbound and 16 outbound shipment carriers in a 12-month planning period can be redesigned within 33 hours. In recent years, multi-agent simulation has been a preferred approach to solve logistics and distribution problems, since these problems are autonomous, distributive, complex, heterogeneous and decentralized in nature and they require extensive intelligent decision making. Another important part in this dissertation involved a development of an agent-based simulation model to cooperate with the optimal solution given by the optimization model. More specifically, the solution given by the optimization model can be inputted as the initial condition of the agent-based simulation model. The agent-based simulation model can incorporate many other factors to be considered in the real world, but optimization cannot handle these as needed. The agent-based simulation model can also incorporate some dynamics we may encounter in the real operations, and it can react to these dynamics in real time. Various types of entities in the entire distribution system can be modeled as intelligent agents, such as suppliers, carriers and customers. In order to build the simulation model more realistic, a sealed bid multiunit auction with an introduction of three parameters a, ß and y is well designed. With the help of these three parameters, each agent makes a better decision in a simple and fast manner, which is the key to realizing real-time decision making. After building such a multi-agent system with agent-based simulation approach, it supports more flexible and comprehensive modeling capabilities which are difficult to realize in a general optimization model. The simulation model is tested and validated on an industrial-sized problem. Numerical results of the agent-based simulation model suggest that with appropriate setting of three parameters the model can precisely represent the preference and interest of different decision makers.

We study inventory systems operating under an infinite-horizon, periodic-review base-stock control policy with stochastic demand and imperfect (i.e., less than 100% reliable) supply. We model demand using a general discrete distribution and replenishment lead time using a geometric distribution, resulting from a Bernoulli trial-based model of supply uncertainty. We develop a computational approach using a discrete time Markov process (DTMP) model to minimize the total system cost and obtain the optimal base-stock level when the backorder penalty is given. We develop a general, recursive solution for the steady state probability of each inventory level and use this to find the optimal base-stock level in this setting. Moreover, for specific demand distributions we are able to develop closed-form solutions for these outcomes. The lead-time demand (LTD) distribution can also be obtained from these recursive equations to determine the base-stock level when a target customer service level is specified in lieu of a backorder penalty cost. We conduct extensive computational experiments to observe the robustness of various approximate solutions under two scenarios for the lead-time distribution. The first scenario assumes a geometric lead time. The second scenario considers a general lead-time distribution. We conduct computational experiments to observe the conditions in which the DTMP model performs well, including situations where the demand and the lead-time distributions are specified separately, and where the LTD distribution is given and follows either a Beta distribution or a bimodal distribution. Finally, for a two-location inventory system consisting of a single retailer supplied by a single distributor, whose supply ultimately comes from an unreliable supplier upstream, we propose a computational approach to determine optimal or near-optimal base-stock levels at the retailer and distributor. We develop two decomposition-based approximation methods, solving the separate single-site inventory problems (distributor, retailer) sequentially, but with different methods to compute the implied backorder penalty at the distributor that induces near-optimal base-stock levels at both locations.

This report analyzes the current greenhouse gas emissions inventory for The University of Texas at Austin (UT-Austin), reviews the carbon reduction strategies being implemented at UT-Austin and other peer institutions, and offers recommendations for strategies that could reduce greenhouse gas emissions at UT-Austin in the future.

Trees are often planted in parking lots to reduce the environmental and visual impacts created by these paved surfaces. I used a two-phase cluster sampling scheme to assess the composition of trees within parking lots in Raleigh, NC, USA. These parking lots contain discrete unpaved areas, called inclusions, which I classified into four types by size and shape: inclusions smaller than 1,000 ft2 were categorized as compact “islands†or linear “slivers†, and inclusions larger than 1,000 ft2 were categorized as compact “chunks†or linear “rows†. I measured more than 1800 trees in 502 inclusions within 110 parking lots to determine the overall composition of trees and how this composition varied among different inclusion types. Raleigh’s parking lots were found to contain 44,000 ± 24,000 trees (with 95% confidence). These trees were estimated to provide benefits valued at more than one million dollars annually. Inclusions containing trees that appear to have existed before construction (“preserved†inclusions) were found to have more trees, canopy, and basal area per acre than other (“designed†) inclusions. Significant differences in tree composition and the proportion of trees meeting Raleigh’s mature shade tree size goals were associated with the size and shape of the inclusion used. A disproportionately high number of willow oaks and trees in larger inclusions were observed meeting Raleigh’s size goals for mature shade trees. Additionally, using GIS, parking coverage was found to vary greatly between different zoning classes, with the greatest coverage in commercial and industrial areas. These results indicate that the distribution and coverage of parking lots in Raleigh, and the composition and resulting benefits of trees within these lots, may depend upon planning and design decisions made before a parking lot is constructed. This study provides a methodology for assessing the composition of trees in a city’s parking lots, which allows comparisons to be made among different types of parking lots, inclusions, and trees. While few studies have addressed the composition of trees in parking lots, this area of research has both practical and scientific value. With additional research, we may better understand how to maximize the ecosystem services provided by trees in limited growing spaces, and use this knowledge to design parking lots with improved environmental impacts.

This research attempts to extend the inventory theoretic approach in three stand-alone papers. The first chapter introduces the inventory theoretic approach and gives a brief overview of the remaining chapters. Chapter two develops an optimization model based on the inventory theoretic approach in an effort to aide managers in selecting the best carrier/mode for their product. Findings suggest that total logistics costs are minimized by selecting a faster mode of transportation as the value of the product and the coefficient of variation in demand increase. The model extends the existing state of the art in the inventory theoretic transportation selection literature by precluding the need for conducting multiple experiments among all available transportation options. Chapter three uses the inventory theoretic approach in a discrete event simulation in an effort to investigate the accuracy of the numerical approach in estimating total logistics costs and rank-ordering the best to worst carriers. Empirical data for this study are gathered from a company that uses a portfolio of carriers to ship product world-wide. Findings suggest that the numerical approach used in the inventory theoretic approach is robust for selecting the best carriers. In addition, carrier schedules were found to have an impact on which carrier provides the lowest total logistics cost. Finally, delays such as equipment shortages, ordering errors, and carrier overbooking were quantified. The results suggest that delays should be tracked by shippers, because an excessive number of delays by a carrier can impact the rank-ordering of carriers.Chapter four extends the inventory theoretic approach to the postponement literature stream. A review of the postponement literature found that transportation uncertainty is largely ignored, lacks examples of an (s, Q) inventory model, and generally ignores the cost of in-transit stock, which is considered here. The fourth chapter also explores the concept of postponement as it relates to product life cycles. Empirical data for chapter four were gathered from a Global 500 Company. Results from this essay suggest that ignoring transportation uncertainty can underestimate the cost of using postponement and lead to the selection of a supply chain strategy that is more expensive. Other findings suggest that postponement strategies can be used for products with long product life cycles to reduce the total cost of a product. This is occurs for both products in the introduction/growth stage of the product life cycle as well as products at the mature/decline stage. Finally, this research suggests that fluctuations in currency exchange rates can be mitigated by use of an assemble-to-order strategy.

Politicians, accountants, and businesspersons have debated the merits of Last-in, First-out (LIFO) inventory accounting for decades. With the inauguration of a new president and increasingly global business markets, this matter is again rising to the forefront of policy debates. It is especially relevant given the likely adoption of International Financial Reporting Standards (IFRS) in the United States. Under IFRS, LIFO would no longer be an option for financial accounting purposes. Though researchers and pundits have written to express strong opinions on this matter, there exists no comprehensive analysis of the effects that a repeal of LIFO would have on the nation. This paper seeks to conduct this analysis and determine the potential effects of LIFO repeal through a qualitative analysis of the merits and shortcomings of LIFO as well as through a quantitative analysis of the industries that would be most affected by LIFO repeal and the tax consequences they would face. This paper looks to make an assessment of the feasibility of LIFO repeal based on these analyses, and to conclude whether LIFO repeal would be beneficial for the nation.

Urbanization has resulted in massive transformation of natural habitats with profound effects on biodiversity. Addressing the problems associated with anthropogenic deterioration of the environment requires solid understanding of the factors driving these changes. Habitat patch size, edge effects, and the presence of invasive species are among the key features of the urban landscape affecting local communities. My study focused on the effects of habitat edges, the exotic ant Nylanderia flavipes, and the invasive herb garlic mustard on local ant diversity in deciduous forest fragments of northeastern Ohio. In addition, I explored the value of common ant collecting techniques for estimating ant diversity.When compared to pitfall trapping, Winkler litter extraction showed greater species richness and higher abundance of ants and exhibited the advantage of a more complete inventory. Winkler extraction may completely replace pitfall trapping in temperate forests with well-developed litter layer, and inventorying programs may benefit from the inclusion of this technique as a rapid and effective ant-collecting tool. Our study documents predictable edge-associated changes in species richness, community composition and functional groups, occurring in local forest ant assemblages. Edge effects are most pronounced within the first 25m, which may have implications for the conservation of forest-dwelling fauna. At the sites where Nylanderia flavipes occurred, we found a 6-fold increase in ant abundance, with 87% of the ants being N. flavipes. The numerical dominance of this species did not lead to detectable changes in the species richness and abundance of the native ant community, but was responsible for decreased community evenness. This is likely indicative of a recent introduction with populations still being in their latency period. The high local abundance of N. flavipes may give it a competitive advantage and affect native ants through exploitative competition. Plots invaded by garlic mustard showed significant reduction in leaf-litter depth, correlated with increased abundance of nonnative earthworms, but no detectable effects on ant species richness or community composition. My results suggest that regional sylvan ant communities are unaffected by the generally presumed negative effects of garlic mustard, or that these effects are more subtle or confounded by other dominating factors.

In the first part, we develop a (Q,r) model based on fuzzy-set representations of various sources of uncertainty in the SC. Sources of risk and uncertainty in our model include demand, lead time, supplier yield, and penalty cost. The naturally imprecise nature of these risk factors in managing inventories is represented using triangular fuzzy numbers. In addition, we introduce a human risk attitude factor to quantify the decision maker's attitude toward the risk of stocking out during the replenishment period. The total cost of the inventory system is computed using defuzzification methods built from techniques identified in the literature on fuzzy sets. A numerical example is provided to compare our fuzzy-set computations with those generated by more traditional models that assume full knowledge of the distributions of the stochastic parameters in the system. Our analysis supports that fuzzy set theory provides a means of actually quantifying the cost of not knowing the underlying demand distribution and parameters. In the second part, we construct a serial multiple-level SC coordination model by (Q,r) policies in the same fuzzy risk environment presented in the first part. Heuristics analogous to classical (Q,r) policies are presented to determine local optimal policies for multiple installation instances on the basis of techniques identified in the literature on fuzzy sets. An external coordinator with three roles is introduced and a coordination process is implemented to improve SC performance to meet a target. Numerical examples and simulation results are given to illustrate heuristics and validate coordination processes. In the third part, we study the impact of continuous review inventory policy by a supplier with partial and random yield. We first examine revised EOQ model with a two-stage partial yield supplier, then we discuss (Q,r) policy under a supplier with one additional stage of supply stoppage. We assume that demand arrival follows Poisson process, and each supply stage duration is exponentially distributed. Two situations in (Q,r) policy: instant replenishment and non-zero lead time, are studied by Renewal Reward Processes, and in each case total logistics cost function with a fill rate constraint is derived. An efficient heuristics based on pattern search is put forward to search for the optimal policy. Numerical examples and Monte-Carlo simulation results are presented to evaluate our approaches.

Recent declines in reptile and amphibian populations across the globe have encouraged an increased desire to discover, document, and monitor these taxa. Arguably, the greatest cause is land-use change. Management interests for Bull Neck Swamp (BNS) encouraged research to inventory the reptile and amphibian community and to document possible impacts of land-use practices, such as silviculture and site preparation. Four habitat preserves were delineated based on plant community, leaving 1, 554ha (3, 841ac) available for management. Comparisons between habitat assemblages were used to determine if preserves were occupied by more vulnerable species and land-use effects on these species. However, variations in behavioral or environmental variables, and detection probabilities between capture techniques could provide misleading data for assemblage comparisons of community parameters. Therefore, 11 different capture techniques were employed to obtain better samples of habitat assemblages. To determine the accuracy of sampling techniques at inventorying species, techniques were categorized into primary (i.e., drift fence arrays with pitfall and funnel traps, visual encounter surveys, and coverboard arrays), secondary (i.e., road searches, polyvinyl chloride (PVC) piping grids, auditory surveys, and line transects), and tertiary (opportunistic encounters, aquatic funnel traps, crayfish traps, and basking traps) methodologies. All techniques had variable distributions and were evenly represented in all five areas when possible. All captured individuals were marked; snakes were double-marked with visible implant fluorescent elastomer to augment a concomitant laboratory experiment. Initial capture data were used to derive estimates of species richness (S) and modified Chao — Jaccard similarity indices (JSI). During May to August, 2005 and 2006, 1, 581 total captures represented 33 species, giving an estimated species richness of 34. Primary techniques sampled an estimated species richness of 14 and two unique species, species detected by only one sampling technique. Estimated species richness for secondary and tertiary techniques was 29 and 25, with three and seven unique species, respectively. If primary techniques alone were used, 59% of the reptile and amphibian community, including 10 unique species, would have been missed. Observed and estimated species richness for habitats ranged from 7 to 32 and 13 to 44, respectively. Chao — Jaccard similarity indices ranged from 0.59 to 1.0, with nine comparisons over 0.75, which indicated high similarity between habitat assemblages. These results suggested that land-use practices should be carefully planned and implemented to reduce effects to the reptile and amphibian community of BNS. Empirical results supported the use of elastomers for snakes. It is recommended that future inventory studies for all taxa employ as many capture techniques as logistically and spatially possible to derive accurate species richness. Also, assemblage comparisons should rely on species composition when determining conservation plans.

This study examined the reliability and validity of a new measure, an inventory designed for the assessment of Dependent Personality traits. The new scale, the Dependent Personality Inventory, uses a dichotomous (True/ False) answer format and produces 8 subscales which correspond with the individual criterion of the disorder as defined by the current Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV-TR). The scale was administered to a sample of 82 students attending a large mid-western university in the United States. Factor analysis of the DPI using a Principle Components Analysis produced two main factors. Additional analysis showed a differential correlation between these two factors and the Dependent Personality Questionnaire (DPQ; Hyler, 1994) and the Minnesota Multiphasic Personality Inventory-2 Social Introversion Subscales 1-3 (MMPI-2 Si1-3; Ben-Porath, Hostetler, Butcher & Graham, 1989). The DPQ correlated highly with the DPI factor 1(r=-.65, p

This research explores data inaccuracy and its impact on supply chain inventory performance. Theft and data entry error cause differences between the physical inventory levels and inventory levels recorded in the computer system. Physical inventory counts are one method commonly used to align physical and recorded inventory levels. Expected benefits of new technology, such as radio frequency identification (RFID) include improvements in the accuracy of supply chain data, including possibilities of real-time alignment of the data. This research looks at the factors of theft, order error, lead time, synchronization frequency, demand variability, stock-out policy, two separate ordering policies and their impact on inventory performance in a three-level supply chain setting for a single item. The order policies considered are the reorder point (ROP) policy with a fixed order quantity and the order-up-to (OUT) policy with a variable order quantity. Simulation modeling is used to analyze factor impacts on the service level degradation, inventory level degradation, and inventory inaccuracy. Factors and performance measures are analyzed at the system level and the individual echelon level. There were several findings. Inaccuracy at one echelon in the supply chain has a negative impact on inventory performance at other echelons. Inventory error increases order size and variability under the OUT policy with variable order quantities compounding the bullwhip effect. The ROP policy with fixed order quantities responds by increasing order placement frequency and experiences less service level degradation than the OUT policy. Dominating factors were order policy, stock-out policy, theft, and inventory synchronization frequency. In general, theft reduction is more effective for improving service level than increasing the frequency of synchronization. Increasing synchronization frequency eliminates warehouse denials (failures to order) and under-ordering. These conditions are caused by inaccuracy where the recorded inventory level dictates a ‘no order’ decision and the physical inventory level dictates an ‘order’ decision. Increasing the synchronization frequency only partially removes the impact of theft, including order variability under the OUT policy. Reduced inventory levels caused by shorted deliveries continue to cause service level degradation even when inventory is synchronized prior to every order placement.

This dissertation consists of four essays related to supply chain competition and coordination of operational and financial decisions. The first essay models dynamic oligopoly supply chain games. The results include circumstances in which an echelon base-stock policy is not a best competitive response. The second essay investigates how airlines might determine their network structures through a three-stage duopoly game. We find that at equilibrium either both airlines use hub-and-spoke networks or both use point-to-point networks. Furthermore, a hub-and-spoke network does not necessarily dominate a point-to-point network; and a high demand variance or a low mean demand generally favors a hub-and-spoke network. The third essay examines a model with a financial criterion and investigates the interdependence of a firm’s capital structure and its short-term operating decisions concerning inventories, dividends, and liquidity. We conclude that the optimal inventory policy does not depend on the firm’s capital structure, but the optimal dividend and liquidity policy does depend on the inventory decisions. The optimal capital structure depends only on the marginal tax rate, the interest rate for long-term bonds, and the decision maker’s interest rate for intertemporal tradeoffs. The fourth essay studies a serial multi-stage manufacturing system whose decision-maker maximizes the expected present value of dividends via periodic decisions concerning production quantities at each echelon, dividends, and short-term borrowing. We show that the liquidity constraint and multiple stages cause echelon base-stock policies to be sub-optimal.

This thesis presents a teaching job shop control system for running in assembly laboratories at colleges and universities in preparing Industrial Engineering students for challenges faced in real-world factories. Current techniques fail to encompass this idea of training for students like the proposed method does. Microsoft Access was used in creating a database that is the center point in this new system. Inventory is managed using this database system and added if parts are created in the manufacturing lab and moved to the assembly area. The system will stop if parts are low until new parts are created. In this new system, a pallet with an unfinished product on it moves down a conveyor system until it reaches the next workstation. At this station, the station operator scans a barcode on the pallet. This barcode contains what product is on this pallet. Based on this information, an ordered list of tasks appears on the workstation computer screen and must be done before the pallet can be moved on. When all tasks have been completed at a station, the station operator clicks 'done' on the screen and then can either move to the next pallet or end the run. Statistics are kept on the quality of the final products and parts as well as a work-in-process and on a goal percentage of good products out at the end of a one-hour time frame. Administrators will be able to assign tasks and parts to products and stations, as well as be able assign the goal ahead of time.