Search

In this paper, we provide a new approach to model the inter-departure times distribution in a PH / G /1 queue. This approach enables to further model the inter-departure times distribution in more general queues as well. Initially, we propose to express the Laplace–Stieltjes transform (LST) of inter-departure times in PH / G /1 queues by exploiting the probabilistic interpretation of phase-type distributions. Using this interpretation enables to eliminate the necessity of the matrix-geometric method, and thus significantly reduces the computational complexity. Then, we use the LST of inter-departure times distribution in a C m / G /1 queue to express this LST in a ME / G /1 queue, where ME is a Matrix-Exponential distribution. We validate it in a few ME / G /1 examples. Finally, we propose to approximate the LST of inter-departure times distribution in a G / G /1 queue by employing the above LST of the proper PH / G /1 queue. Without loss of generality, we demonstrate our proposed approximation by using the LST as obtained in a C m / G /1 queue, while illustrating by a few G / G /1 examples that the accuracy can be as good as one might want.

The collaboration of robots or other renewable resources for the efficient performance of complex operations is an essential part of modern industrial systems. Here we considered a key feature of the scheduling problem, namely, alternative modes by which to execute an operation. Every operation mode uses a specific set of collaborating resources to perform the operation. This structure allows the scheduling engineer to incorporate in each operation a set of activities using few resources, where each resource performs some of the activities during a portion of the operation's duration. As such, this setup allows the reuse of previously designed solution segments in a manner that conserves engineering efforts and reduces model size while retaining the flexibility conferred by the use of alternative modes. In this paper, we propose new lower bounds for improving the solution process efficiency and we demonstrate a model-driven decision support system for formulating and solving such scheduling problems. Two types of bounds are derived and analyzed, LP relaxation and parameters-based calculation. Both types consider partial solutions at the customized branch and bound solution tree. The dominance relations among the bounds are determined analytically, and the performances of the dominant bounds are compared empirically. The results demonstrate a 70% runtime reduction compared to previously published methods and bounds.

Predicting queue performance by approximating class-departure variability in tandem queues with downtime events via existing decomposition methods is neither accurate enough nor efficient enough. Analytic approximations, if conducted alone, lack accuracy but attempting to increase accuracy by incorporating simulation to analytic approximation has proved to require significant computation efforts. The aim of this paper is to reduce the latter inefficiency by modeling the Regression-Based Variability Function (RBVF) designed to approximate the between-class effect by exploiting the departure process from a single queue. The new approach predicts performance of n -tandem queues by reducing the focus to two-tandem queues for each traffic intensity level, as well as by modeling different policies of downtimes (e.g. first-come-first-served or priority). Numerical experiments demonstrate that the proposed RBVF delivers both accuracy and efficiency improvements: the relative errors associated with RBVF are about three times smaller than the best existing analytic procedures and the computation efforts associated with RBVF are about five times smaller than existing analytic procedure combined with simulation.

The objective of this research is to study the cost of Net Zero Energy (NZE) communities of different urban scales and densities, while taking into consideration the local climate and the type of buildings in the community. A comprehensive model was developed for this purpose, with which the cost-optimal configuration of renewable energy-related technologies for an NZE community can be identified. To validate the model, data from two case studies that differed in their climate and building types were used. The results of this study contribute to a better understanding of the implications of NZE requirements for urban planning. An increase in the scale of a community was found to reduce energy costs, up to a certain point. Urban density, on the other hand, was found to have a more complex impact on costs, which depends on the local climate of the community and the subsequent energy demand. This underlines the importance of addressing the technological design of energy systems at the initial stage of the urban planning of energy-efficient communities, before the urban density, the unbuilt areas and the building types are set.

It is widely agreed that expanding the Net Zero Energy concept to a community scale brings multiple advantages in terms of design flexibility, cost reduction, clean energy and resource management. However, it is less clear what the scale or the density of the community should be to utilize these advantages in the best possible way. The objective of this work is to study how the Life Cycle Costs of new Net Zero Energy communities are affected by changes in their density and scale. To achieve this objective, a model was developed for identifying the optimal configuration of energy technologies for each specific scale and density. The analysis takes into consideration the local climate and the types of buildings in the community. The results of the implementation of the model show that both urban density and scale have a direct impact on the costs of NZE communities, but in ways that depend on the local climate.

Production shop is a complex task that impacts the ability of an organization to integrate between economic and production performance measurements. The CONWIP (CONstant Work In Process) approach is the simplest pull system to implement. Two-machine transfer line with the exponentially distributed duration of the process, break and repair were formulated and solved as a Markovian-chain. Decomposition was used to iteratively employ this solution in solving K-machine transfer line. These models assume that batch size is fixed. In this study we extend the existing transfer line solution, to deal with 1 unit process batch and t units transfer batch along the line.

This paper describes the development of a set of control and optimization tools that support the implementation of highly innovative energy technologies in the design of Net Zero Energy settlements. The incorporation of novel technologies in building and settlement design involves unique challenges, such as frequent design changes, the risk of cost overruns and the need to synchronize the work of different stakeholders who did not previously collaborate. Three tools were developed and implemented to cope with those challenges. A cost control tool tracks the technology’s initial costs during the design phase, so that any cost overrun can be easily recognized and treated. A change management tool allows the project’s designers to identify, examine and discuss the implications of every proposed change in the design before the change is executed, and prevent deviations from the project’s cost and energy performance requirements. A third tool supports a life cycle cost analysis of each technology and an identification of their optimal configuration. In response to a tight schedule and the uncertainty regarding the users’ exact requirements, an Evolutionary Prototyping methodology was adopted to develop the tools. They were implemented in a project involving the design of Net Zero Energy settlements in four different European countries.

The main motivation of this study is to provide, for the first time, a formulation and solution for a class of production scheduling problems (as in cluster tools) characterized mainly by resource collaboration to perform an operation and while allowing batches and considering alternative production methods. We develop a formulation for the new problem and term it a multiple mode per operation, resource collaboration, and constrained scheduling problem (MRCCSP). Some of the important new characteristics we consider are: multiple products (families); multiple orders (jobs) per family; precedence restrictions among the operations that constitute a job; alternative modes for the performance of an operation (each of which needs a set of collaborating resources) may be defined; complementary and exclusive restrictions between operation-modes; batch production is allowed; and setup times may depend on sequence and batch-size. The objective of the MRCCSP is to minimize makespan. We formulate the MRCCSP as a mixed integer linear programming model, and acknowledging the considerable size of the monolithic formulation required, we prescribe a specific method to achieve size reduction. Finally, a customized branch and bound algorithm for optimally solving this problem is proposed and examined experimentally.

Within the complex and competitive semiconductor manufacturing industry, lot cycle time (CT) remains one of the key performance indicators. Its reduction is of strategic importance as it contributes to cost decreasing, time-to-market shortening, faster fault detection, achieving throughput targets, and improving production-resource scheduling. To reduce CT, we suggest and investigate a data-driven approach that identifies key factors and predicts their impact on CT. In our novel approach, we first identify the most influential factors using conditional mutual information maximization, and then apply the selective naive Bayesian classifier (SNBC) for further selection of a minimal, most discriminative key-factor set for CT prediction. Applied to a data set representing a simulated fab, our SNBC-based approach improves the accuracy of CT prediction in nearly 40% while narrowing the list of factors from 182 to 20. It shows comparable accuracy to those of other machine learning and statistical models, such as a decision tree, a neural network, and multinomial logistic regression. Compared to them, our approach also demonstrates simplicity and interpretability, as well as speedy and efficient model training. This approach could be implemented relatively easily in the fab promoting new insights to the process of wafer fabrication.

Based on a simulated non volatile memory fab, we employ data-mining to identify and quantify the apparent causes of work in process bubbles along the process. The chosen bubble formalization methods proved able to detect the phenomenon and enabled its occurrence frequency to be forecasted. In the chosen environment, bubbles seem to be highly correlated with the utilization patterns of the process segment considered.

We suggest a data-driven methodology to identify key factors of the cycle time (CT) in a semiconductor manufacturing plant and to predict its value. We first extract a data set from a simulated fab and describe each operation in the set using 182 features (factors). Then, we apply conditional mutual information maximization for feature selection and the selective naive Bayesian classifier for further selection and CT prediction. Prediction accuracy of 72.6% is achieved by employing no more than 20 features. Similar results are obtained by neural networks and the C5.0 decision tree.

We quantify the impact of work in process (WIP) bubbles on a semiconductor fab. As a preliminary step, we formalize the concept of WIP bubbles by decomposing them into local events of relatively acute and temporary WIP congestion. The local bubble is empirically identified and its impact on local waiting time distribution is assessed. We then estimate its marginal impact on the overall line waiting time and cost. Finally, a novel visualization tool for the bubble's progression is proposed.

In this paper we study the performance of a branch and bound enumeration procedure in solving a comprehensive mixed-integer linear programming formulation of a resource-sharing and scheduling problem (RSSP). The formulation is monolithic and deterministic. Various independent factors of an RSSP generally influence the size of the problem. An experimental analysis is performed to examine how such factors influence performance. We found that the number of resources, the total number of modes allowed in the problem, and the average number of renewable resources used in the problem have significant influence on the computational time as well as other performance measures. In addition, some intermediate response variables are identified and additional insights regarding the influences of the independent factors on these variables are provided.

Every organization controls its investments in the development and maintenance of internal knowledge (IK) as opposed to outsourcing this effort, namely, consuming external knowledge (EK). A number of factors involved in this decision, such as the IK learning curve, its associated holding cost, value deterioration rate, value of future IK or cost of purchasing EK. This study proposes a dynamic optimal control model for examining the properties of this problem. Optimal control strategies and steady-state conditions are identified for a number of special cases. Some insightful observations are obtained by studying the solution sensitivity to the underlying assumptions.

Inventory management involves determination of shortage policy. It specifies the conditions for losing or backordering a demand. Alternative policies include pure backorder, pure lost sales, and partial backorder (using a single backorder control limit). When the backorder-cost is time dependent it makes sense to modify the backorder-limit over time. Thus, a new form of partial backorder policy (PB2) with two-segment backorder control limits is introduced. The traditional policies mentioned above, are special cases of PB2. Hence, we provide a unified framework for studying different policies that deal with shortage. The PB2 problem is formulated and solved as a discrete time, stochastic constrained control problem. Its performance is numerically compared with the simpler alternative policies. In some cases its cost savings, versus the best of PB and PL, exceeds 15%, and 7% versus a single backorder limit policy. The economical advantage is significant over a wide range of the problem parameters. Scope and purpose This paper develops an expanded framework for modeling shortages in inventory management. It recognizes that optimal backordering strategy may change over time during an “out-of-stock” period. The paper is motivated by experience in the chemical industry in which, the cost of backordering is highly time related. Inventory managers, in this industry, consider to lose sales initially (once they run out of stock) and begin to backorder demand later as they approach the replenishment time. A two-segment partial backorder (r,Q) model is introduced and solved. Pure backorder (PB), pure lost sales (PL), and partial backorder (using a single backorder limit), are all special cases of the proposed model. The problem is formulated and solved as a discrete time, stochastic constrained control problem. Its performance is numerically compared with the simpler alternative policies. In some cases its cost savings, versus the best of PB and PL, exceeds 15%, and 7% versus a single backorder limit policy. The economical advantage is significant over a wide range of the problem parameters. The partial backorder policy we propose is not only different from those in the literature, but it provides new control flexibility.

The Dead Sea Works Ltd extracts more than 2 million ton of potassium a year from the Dead Sea and ships it by surface and marine transportation to markets around the world. Product mix and market distribution changes have made it necessary to redesign the surface logistics system. This system consists of four sites with a total of more than 300,000 ton of roofed storage capacity. The purpose of the study was to suggest changes in the storage capacity of each product at each of the sites. We have designed a model that expresses the expected annual loss for the firm and have solved it under potential market scenarios and management and technological constraints. Sensitivity analyses were then carried out for several uncertain cost and reliability parameters. Based on the best system configuration for each scenario, alternative configurations were designed in detail and compared under potential market scenarios. With a clear understanding of the risks and expected benefits involved, the decision makers then selected the best configuration. The annual net benefit from implementing the proposed solution is expected to be 1.7 million dollars.

Two cases of the discrete time finite horizon technology replacement problem are solved. The first deals with two available machines, one in use and a better one that can be purchased to replace it. The second case considers, in addition, a third machine of future technology that will be available at some random future time. The maintenance level of each used machine is chosen for each period in order to economically control performance deterioration. For solving the model, we first derive an optimal preventive maintenance policy by showing that “bang-bang” (i.e., full or non), non-increasing, maintenance efforts are optimal. Employing the optimal maintenance policy, we reformat the problem and conduct a numerical search in order to derive the replacement policy that will maximize the expected net present profit. Numerical examples demonstrate that management may improve profitability by delaying (but not necessarily foregoing) replacement with an available better machine.

We propose a globally convergent method for the numerical solution of a class of continuous nonlinear knapsack problems arising, e.g., in chemical production service facilities.

This paper deals with the decision making for resource sharing and scheduling for a class of computer-integrated manufacturing cells. Each such cell is characterized by a set of resources and it can be used for the cyclic production of several products. The production (processing) of each product requires a set of operations with sequence constraints among them. Each operation can be performed using alternative modes. Each mode defines the subset of resources needed for the operation and the timing of their use. Resource setup times are sequence-dependent. The problem is to choose the best mode for each operation and, accordingly, to allocate and schedule resources in order to minimize make-span time. A general (feasible) mixed 0–1 LP model is proposed. A customized Branch and Bound type solution algorithm is presented, which fathoms only feasible solutions and finds all optimum solutions.

Previous research on the newsboy problem is based on the assumption that in case of a shortage, unsatisfied demand is lost. Such an assumption is inappropriate for items that have a close substitute. In this paper, we formulate a two-item newsboy problem with substitutability (TINPS). Upper and lower bounds on the optimal order quantities of the two items are derived. Since analytical solutions to the problem are difficult to obtain, a Monte Carlo simulation is used to identify the optimal solution to the TINPS. Order quantities identified by the simulation provide higher expected profit than would have been obtainable without considering substitutability.

This paper presents a two-stage model for industrial robot operation and selection in a multi-product manufacturing system. The quality of the production process is related to robot repeatability and the output rate is related to robot speed. Robot repeatability deteriorates with increased speed. In the second stage, a one-period robot operational model maximises the expected profit of a risk-neutral decision maker, leading to optimal robot speed and production quantity decisions. Using the optimal speeds and quantities of the operational model and taking into account demand uncertainties, the first stage robot selection model is solved.

In this paper, we consider the partial backorder policy used in conjunction with the traditional stochastic (r, Q) inventory system. The partial backorder control is modeled using a control variable, b, which limits the maximum number of backorders allowed to be accumulated in any given cycle. In traditional inventory control policies, the unsatisfied demands are either completely backordered or completely lost. The new control variable b provides an alternative to dealing with shortages, which is different from the two extremes policies: the pure backorder policy (which corresponds to b=∞) and the lost sales policy (b=0). We obtain the expected annual cost of the model and give a procedure to compute the optimal parameters, r∗, b∗ and Q∗. Numerical examples which demonstrate the advantage of implementing the partial backorder policy are presented and the percentage of cost savings depends on the fill rate.

A two-stage decision and control model for the operation of the power station in the Hazbani-Dan Water System (Israel) was developed and implemented. In this system, river water is stored and then either consumed by agricultural farms or released to a hydroelectric power station. Under management policy, agricultural consumption is taken as a constraint. The problem was solved via a two-stage procedure consisting of a long-term model (LTM) and a short-term model (STM). The LTM was presented by Rabinowitz et al. (1988). This paper reports on the development and analysis of the STM, in which operational flexibilities of the power station are taken into consideration, thereby improving the LTM solution. Our main contributions are aimed at: (1) describing the (nontraditional) systematic thinking process which transformed the mathematically expressed problem into an efficiently solvable model; (2) presenting the analysis of the model; and (3) reporting on a practical application of the model in a unique system.

Dyslipidemia remains underdiagnosed and undertreated in patients with coronary artery disease. The Computer-based Clinical Decision Support System provides an opportunity t close these gaps.To study the impact of computerized intervention on secondary prevention of CAD.The CDSS was programmed to automatically detect patients with CAD and to evaluate the availability of an updated lipoprotein profile and treatment with lipid-lowering drugs. The program produced automatic computer-generated monitoring and treatment recommendations. Adjusted primary clinics were randomly assigned to intervention (n=56) or standard care arms (n=56). Reminders were mailed to the primary medical teams in the intervention arm every 4 months updating them with current lipid levels and recommendations for further treatment. Compliance and lipid levels were monitored. The study group comprised all patients with CAD who were alive at least 3 months after hospitalization.Follow-up was available for 7448 patients (median 19.8 months, range 6-36 months). Overall, 51.7% of patients were adequately screened, and 55.7% of patients were compliant with treatment to lower lipid level. In patients with initial low density lipoprotein120 mg/dl, a significant decrease in LDL levels was observed in both arms, but was more pronounced in the intervention arm: 121.9 +/- 34.2 vs. 124.3 +/- 34.6 mg/dl (P0.02). A significantly lower rate of cardiac rehospitalizations was documented in patients who were adequately treated with lipid-lowering drugs, 37% vs. 40.9% (P0.001).This initial assessment of our data represent a real-world snapshot where physicians and CAD patients often do not adhere to clinical guidelines, presenting a major obstacle to implementing effective secondary prevention. Our automatic computerized reminders system substantially facilitates adherence to guidelines and supports wide-range implementation.

The Hazbani-Dan water system supplies gravitational water for both irrigation and hydroelectric energy production. An automatic control system for operating the energy plant while satisfying the irrigation needs was developed and applied. This was accomplished through an interdisciplinary research effort comprised of a feasibility study, formulation and analysis of a two level optimization model, development of forecasting models, and information system analysis. The value of such a control system goes beyond its direct economical benefits. It also provides tools for management decisions involving the evaluation of investments, and the determination of operating rules. This paper consolidates the narrow and technical scope of previous reports by presenting the overall research methodology, the relationships among the different parts, and a unique way of conducting applied research in an academic environment.

This paper suggests a conceptual scheme to represent the knowledge base of Management Science/Operations Research (MS/OR). Such a scheme could be utilized within the framework of a Decision Support System (DSS). Its purpose is to facilitate expansion of knowledge. An illustrative example is provided to indicate the potential of a DSS with the scheme suggested. Finally, some of the issues of development and implementation of the suggested DSS are discussed.

An optimal decision and control model was developed and used for dual purpose allocation of water from a reservoir. The model was constructed for the Hazbani-Dan Water System in the Upper Galilee (Israel). In this system about half of the Dan River water enters and is stored in a reservoir and is then released via a 70″ pipe either to a hydroelectric power station or to agricultural fields for irrigation. The electricity generated is sold to the national electricity utility according to a three-tiered pricing system which is a function of the time in the day and the season. The decision and control model maximizes the expected return on energy production subject to storage capacity and flow limitations. According to the policy of the system management, water demand for irrigation has to be satisfied and so it is taken as a constraint in the model. The following paper develops the model, exploring the feasibility conditions of the model and the uniqueness of the solution. An example containing a sensitivity analysis is given.

To compare enlargement alternatives in the Hazbani-Dan water supply system, we developed an evaluation process that considers organizational, technological, and economical factors. The suggested alternatives are intended to improve system utilization by combining additional water resources and users. We used the criterion of expected annual return to compare the alternatives and a linear programming model to allocate water for different users according to policy priorities. Based on the results, we used a nonlinear programming model to allocate water for hydroelectric energy production. An annual return of about one million dollars is predicted for the most economical alternative versus a return of one hundred thousand dollars prior to enlargement. However, due to managerial factors and lack of reliable data, only two of the four possible physical enlargements were implemented as recommended, with an expected annual return of $422,000.