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For many items, decisions related to supply chain management have not been an easy task. It is then much more complex for perishable items than the other. Besides cost-efficiency, other factors, such as customer expectation and product freshness, have significant contributions to competitive advantages and business’s reputation. In other words, it would be flawed to care about either cost or profit ignoring other objectives. A multi-objective model for such items is, thus necessary. In this regard, this study introduces an integrated network design and inventory optimization model considering a perishable supply chain from a sustainable perspective. The mathematical model is to maximize profit, minimize the freshness loss of products at the point of demand fulfillment, and minimize the solid waste stress of the supply chain. In the result analysis, a numerical case is used to demonstrate the model utilization and the decisionmaking process. Max-Min Operator is to solve a multi-objective model. The model resulting shows tradeoffs among the objectives. Additionally, decision-makers can observe influences of the environment and society along with the economy on supply chain-related decisions, and vice versa. [ABSTRACT FROM AUTHOR]

Even the shortest flight through an urban canyon requires a reliable local path planning algorithm to avoid unknown or unforeseen obstacles. The local path planner must evaluate multiple alternative flight paths and identify the best path if challenged with an obstacle blocking its flight path. Commonly, weighted sums are used for this task. This work shows that weighted Chebyshev distances and factorial achievement scalarizing functions are suitable alternatives to weighted sums if combined with the 3DVFH* local path planning algorithm. Both methods considerably reduce the failure probability of simulated flights in an urban environment, with an additional slight reduction in flight time. The standard 3DVFH* uses a weighted sum and has a failure probability of 73% in the test environments. A factorial achievement scalarizing function which minimizes the worst combination of three out of four objective functions reaches a failure probability of 47%. [ABSTRACT FROM AUTHOR]

Water resources and water-related sectors are increasingly affected by multiple challenges such as climate change and extreme events, issues of ageing infrastructure, natural and qualitative water scarcity, recession, wars, population movements, increased energy and resource demand, etc. In an attempt to balance different goals of water allocation under different constraints, we present a multi-objective optimization model. The model considers various water supply sources (groundwater, surface water, desalinated water, treated wastewater) and water uses (domestic, agricultural, industrial). Water demand, availability, quality parameters, costs, and stakeholder input for the prioritization of the different goals set are synthesized through Goal Programming. [ABSTRACT FROM AUTHOR]

The paper aims to propose a model for production possibilities frontier for commodities of an enterprise. The motivation of the paper is to contribute to one of the Sustainable Development Goals set by the United Nations, which is to reduce carbon footprint towards the net zero emissions by 2050. A Multi Objective Optimization is used in the study, to help managers decide what mix of products to produce in order to minimize the production costs and carbon dioxide emissions with their current resources. Data in the model comes from a case study company to have numerical parameters and test the feasibility and efficiency of the proposed model. The input parameters and all constraints mentioned in the study was transformed into mathematical model and was coded in Linear programming software. The results show a Pareto Optimal mix of products or production possibility frontier in which the optimum solution points are converged within the boundary of their resources. [ABSTRACT FROM AUTHOR]

In this research, we address the issue of outsourcing the clearance operation to a third party, in the context of multi-partner and multi-product. The outsourcing operation is a widespread industrial practice and requires a welldefined process to follow to be successful. Indeed, to choose the best partners (or retailers) from a potential set of candidates, a company must define its selection criteria, as well as the selection method that should be applied. This paper aims to develop a framework for the clearance partner selection process. Using multi-objective optimization and based on the suppliers' evaluation according to the established criteria, appropriate partners are selected, and optimal allocation of unsold quantity is determined. [ABSTRACT FROM AUTHOR]

For the past decades, the supply chain network has been significantly influenced by several major factors. Standing out of those is global warming which causes radical changes in traditional supply chain network. Therefore, environmental vulnerability with legality and economic benefits has made supply chain management to be as an important objective for companies and governments. Sustainability must be built-in supply chain network design (SCND), by striking the right balance between cost-cutting and environmental preservation. The goal of this paper is to develop a green closed-loop supply chain (G-CLSC) network by first, evaluating supplier selection with Multi-criteria decision-making (MCDM). Subsequently, a bi - objective Mixed-Integer Linear Programming (MILP) model is presented for facilities allocating and consolidate closed-loop flow, with two objectives: minimizing operating cost and minimizing CO2 emission. The model is testified by implying on the case of a Vietnamese plastic injection enterprise consisting of facilities spreading all over the country. The simulated result demonstrates the efficiency and validity of the proposed solution model. All possible solutions are measured thoroughly to identify the optimal design for prior environment protection. Ultimately, the solution model finalizes with a forward and a reverse supply chain network. [ABSTRACT FROM AUTHOR]

Disaster planning and management has always been challenging for the relief agencies of the underdeveloped counties, since they usually do not have the well organized and well-funded system in place, like the developed counties. The models developed in the previous literatures either did not address all the relevant logistics issues or are often too complicated to be utilized by the underdeveloped countries. To mitigate the casualties caused by disastrous events, post disaster planning is very important. This research has developed a simple but inclusive multi-objective post disaster logistics planning model to addresses issues like distribution of relief commodity and medical supplies, staffing of medical facilities, evacuation of wounded people to the medical facilities, vehicular and transportation management, unsatisfied demands at affected locations etc., while maintaining service equity. The applicability of the proposed model has been tested via a real-world case study based on the recurrent flash flood problem in the Sylhet district of Bangladesh. A Branch-and-Cut algorithm has been used via CPLEX platform to solve the developed linear integer programming model. Results have been demonstrated both numerically and graphically to aid the decision maker to properly visualize the solutions, which has offered several important managerial implications and insights on the post disaster management. This way the proposed research is expected to help the decision makers to plan an efficient and effective post disaster logistics network to minimize human suffering, wastage of relief goods, and associated operational costs. [ABSTRACT FROM AUTHOR]

Humanitarian logistics management has recently become a trendy buzzword because of the number of natural disasters in recent years. Managing relief & logistics networks properly when disaster strikes are of great importance, as any negligence may result in a severe penalty such as loss of precious lives & public property. The unpredictable and unexpected nature of natural calamities is another concern. In a catastrophic event, decision-making usually involves choosing the best locations for aid distribution, distributing relief goods to different demand nodes, and managing the appropriate routes for transportation of relief goods, medicines, etc. This study proposes a multi-echelon multi-objective logistics model based on a multi-objective approach to finding optimal locations for setting up regional relief distribution centers with the minimum facility set up cost and to see the optimal quantity of goods to be flowed between nodes and stages of the relief supply chain to minimize distribution and supply costs, and the unmet Demand under uncertain conditions. [ABSTRACT FROM AUTHOR]

The paper aims to propose a model for production possibilities frontier for commodities of an enterprise. The motivation of the paper is to contribute to one of the Sustainable Development Goals set by the United Nations, which is to reduce carbon footprint towards the net zero emissions by 2050. A Multi Objective Optimization is used in the study, to help managers decide what mix of products to produce in order to minimize the production costs and carbon dioxide emissions with their current resources. Data in the model comes from a case study company to have numerical parameters and test the feasibility and efficiency of the proposed model. The input parameters and all constraints mentioned in the study was transformed into mathematical model and was coded in Linear programming software. The results show a Pareto Optimal mix of products or production possibility frontier in which the optimum solution points are converged within the boundary of their resources. [ABSTRACT FROM AUTHOR]

Disaster planning and management has always been challenging for the relief agencies of the underdeveloped counties, since they usually do not have the well organized and well-funded system in place, like the developed counties. The models developed in the previous literatures either did not address all the relevant logistics issues or are often too complicated to be utilized by the underdeveloped countries. To mitigate the casualties caused by disastrous events, post disaster planning is very important. This research has developed a simple but inclusive multi-objective post disaster logistics planning model to addresses issues like distribution of relief commodity and medical supplies, staffing of medical facilities, evacuation of wounded people to the medical facilities, vehicular and transportation management, unsatisfied demands at affected locations etc., while maintaining service equity. The applicability of the proposed model has been tested via a real-world case study based on the recurrent flash flood problem in the Sylhet district of Bangladesh. A Branch-and-Cut algorithm has been used via CPLEX platform to solve the developed linear integer programming model. Results have been demonstrated both numerically and graphically to aid the decision maker to properly visualize the solutions, which has offered several important managerial implications and insights on the post disaster management. This way the proposed research is expected to help the decision makers to plan an efficient and effective post disaster logistics network to minimize human suffering, wastage of relief goods, and associated operational costs. [ABSTRACT FROM AUTHOR]

Demand for energy is only expected to grow in the near future due to global population growth which result to a rise in environmental concerns with the diminishing supply of fossil fuels along with the rise of greenhouse gas emissions. Biofuel production from microalgae biomass has been proven to be a viable alternative for fossil fuels; however, challenges are faced regarding its economic sustainability. The integration of processes to yield various high-value bioproducts have been implemented to raise profitability and sustainability. Incorporating a circular economy outlook, recirculation of resource flows is maximized to satisfy economic and environmental sustainability through waste minimization. Previous modelling studies have not looked into the opportunity of integrating continuous recirculation and reuse of resources. In this work, a novel multi-objective optimization model is developed centered on an algal biofuel supply chain that simultaneously optimizes cost and environmental impact, integrates inputs and processes aimed towards a closed loop process flow, and adopts the principle of resource recovery and recirculation. A case study is solved as proof of concept and to illustrate the design methodology, optimal solutions based on economic and environmental performance are analyzed. Scenario analysis is also performed to analyze system behavior under varying conditions. [ABSTRACT FROM AUTHOR]

Reactor core design is inherently a multi-objective problem which spans a large design space, and potentially larger objective space. This process relies on high-fidelity models to probe the design space, and sophisticated computer codes to calculate the important physics occurring in the reactor. In the past, the design space has been reduced by individuals with extensive knowledge of reactor core design; however, this approach is not always available. In this paper, we utilize a set of high-fidelity models to generate a reduced-order model, and couple this with a genetic algorithm to quickly and effectively optimize a preliminary design for a prototypical sodium fast reactor. We also examine augmenting the genetic algorithm with physical programming to generate the fitness function(s) that evaluates the degree to which a core has been optimized. Physical programming is used in two variations of multi-objective optimization and is compared with a traditional weighting scheme to examine the solutions present on the Pareto front. Optimization on the reduced-order model produces a set of solutions on the Pareto front for a designer to examine. The uncertainty for the objective functions examined in the reduced-order model is less than 7% for the given designs, and improves as additional data points are employed. Utilizing a reduced-order model can significantly reduce the computation time and storage to perform preliminary optimization. Physical programming was shown to reduce the objective space when compared with a traditional weighting scheme. It also provides an intuitive and computationally efficient way to produce a Pareto front that meets the designer's objectives. [ABSTRACT FROM AUTHOR]

Recommender systems enable users to discover items of interest from a large set of alternatives. Most recommender systems employ accuracy-oriented algorithms to predict user preferences. Overemphasis on accuracy leads to monotony in the items predicted, resulting in low customer satisfaction. Hence, to improve user experience, it is crucial to inject serendipity into recommendations by discovering users' latent preferences and recommending items that are both relevant and unexpected. However, there is a lack of serendipity-related labelled data and previous serendipitous recommendation algorithms are unable to strike a trade-off between serendipity and accuracy. We address the challenges by presenting a new dataset and designing a novel deep learning-based recommendation algorithm. Our experiments demonstrate that our model improves over state-of-the-art methods, in both recommendation accuracy and serendipity. We outline our plan to conduct a comprehensive user evaluation, through surveys and eye-tracking experiments, to understand how serendipitous recommendations impact user behavior in e-commerce. [ABSTRACT FROM AUTHOR]

Natural disasters are serious challenges for the underdeveloped regions of the world, who usually works with very limited resources. In this research an efficient multi-objective, multi-echelon, multi-commodity pre-disaster planning model has been developed, which can be used by aid management agencies in underdeveloped countries with limited resources. The proposed model minimizes the travelling distances among different key nodes in the logistics network by choosing appropriate locations for setting up regional warehouses with the necessary capacity, while complying with the budgetary constraints, and selects appropriate suppliers from a pool of available suppliers, while ensuring service equity. A scenario-based approach has been used here to ensure that the proposed facilities can accommodate moderate fluctuations in demand in the future. The applicability of the model has been tested via a real-world case study on the recurrent flash flood problem of the Sylhet district in Bangladesh. A Branch-and-Cut algorithm has been used via CPLEX platform to solve the developed linear integer programming model. Results have been demonstrated both numerically and graphically to aid the decision maker to properly visualize the solutions, which has offered several important managerial implications and insights on the disaster management. This way the proposed research intends help the decision makers to plan an efficient and effective humanitarian logistics network and thereby to minimize human suffering, wastage of relief goods, and associated operational costs. [ABSTRACT FROM AUTHOR]

As the number of electric vehicles increases, the access to public charging points along an electric grid can become congested. This study proposes a multi-agent solution to solve the task of electric vehicle allocation on smart grid chargers sensitive to the vehicle's base, destination and battery profile for charging convenience. In particular, we assess robustness against irrational vehicle behavior and unstable power supply. The proposal follows a design science research methodology. We consider an unstable power source and agents with emotional behaviours, such as irrational preconception of stations and individual perceptions experience. Different environments are simulated with an automatic topology engine extraction grounded on real world urban scenarios. This tool may be helpful to aid planning by simulating station distribution in a parameterizable way. The simulation results allow for a preview of the architecture performance at a system wide level considering several scenarios with distinct characteristics. The smart grid consistently manages energy delivery, leaving no cars uncharged. [ABSTRACT FROM AUTHOR]

Fierce competition in the market has forced companies to study their supply chain networks more. Due to increased social awareness and stricter governmental laws and legislation, the green closed-loop supply chain (GCLSC) has been reviewed more recently. The main goal of this study is to propose a multi-objective model for optimization of a comprehensive green closed-loop supply chain network with a multi-period multi-stage network, including the manufacturer, distributor, customer market, collection, recovery, and disposal centers under uncertain demand. To handle the uncertain parameter, we utilized chance constraint fuzzy programming. We considered different objective functions, consisting of maximizing income, minimizing total supply chain cost, and minimizing total CO2 emissions (i.e., CO2 emitted from facility centers and various transportation modes). Aimed at achieving optimal values, we utilized a carbon-pricing approach to transform the problem into a single objective function. A numerical example coupled with a sensitivity analysis has been conducted to validate the proposed model and formulation. The results show the suitability of the model and the formulation. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) are dedicated networks used in applications where environmental information must be collected, such as temperature, humidity, level, flow, pressure, rain, radiation, among others. These kinds of networks are constrained regarding power, bandwidth, number of nodes per area unit, etc. It is desirable that they operate without supervision and can work steadily in time, because they are normally located in difficult or far places. Nonetheless, some of these metrics are conflicting with others, so if one improves, some of the others get worse. So, it is mandatory to know what is the best combination of metrics that in conjunction can fit an application the best. Literature reports works where¬ optimization is used as a mathematical scheme to solve this problem, and two scenarios are provided: first, where a single objective function is proposed regarding one metric, and the other metrics are restricted via constraints, and second, where multi-objective optimization (MOOP) approaches are proposed, but without considering the whole set of significant metrics involved in WSN, so there is not a definitive solution that finds a real optimal set of metrics. System Dynamics (SD) is a computer-aided approach to design and analyze (mostly) social, economic and enterprise systems, that allows proposing a mathematical framework to analyze such complex systems, by using relationships of interdependence, mutual interaction, feedback and causality. This work aims to show a first dynamic hypothesis of a model that considers important metrics of WSN, in order to find a set of equations that serve as objective functions in a MOOP context. By applying this methodology is possible to find some difficult relations between metrics, that are not clearly reported by previous work so far. [ABSTRACT FROM AUTHOR]

Wind energy has become an essential costcompetitive pillar for the energy transition, and the numerous possibilities of a wind power plant's design motivate the use of optimization techniques. This work proposes a multi-objective function to size the cable diameter of an offshore wind power plant considering the corresponding power losses and costs of cables. With the help of Pareto optimality, the best set of cables for a given placement of wind turbines and the substation were chosen. The results were compared with a reference case, where the cables were sized to fulfill the current-carrying capacity. The result showed a more cost-effective design with the proposed optimization method. [ABSTRACT FROM AUTHOR]

Biomass co-firing in coal power plants is an immediate and practical approach to reduce coal usage and pollutant emissions because only minor modifications are required. With direct co-firing, biomass can be used directly as secondary fuel in power plants to partially displace coal. Although it requires minimal investments, it can lead to equipment corrosion from unconventional fuel properties of the biomass-coal blend. With indirect co-firing, the risk of damage is minimized by separately processing biomass. The solid biochar by-product can be used as soil fertilizer to achieve further reductions in GHG emissions through carbon sequestration. However, as this calls for a separate biomass energy conversion plant, its investment cost is higher. Moreover, this system faces uncertainties from the inherent variability in biomass quality. This must be accounted for because mixing fuels results in the blending of their properties. In this work, a robust optimization model is proposed to design cost and environmentally effective biomass co-firing networks that decides on appropriate co-firing configurations and fuel blends. A case study is solved to demonstrate validity. Results of Monte Carlo simulation show that the robust optimal network configuration is relatively immune to uncertainty realizations as compared with the optimum identified with deterministic models. [ABSTRACT FROM AUTHOR]

With the increase in automated decision making using predictive analytics, the aspect of fairness of the resulting predictions for specific groups is increasingly considered in research and practice. Currently, the actual trade-off to achieve fairness, or a certain level of fairness, is not well understood, other than that an increase in fairness typically decreases other predictive analytics metrics such as accuracy. To enable a systematic evaluation of potential trade-offs between fairness and other metrics, a framework for exploring Algorithmic Fairness is proposed. Using a combination of multi-objective optimization and Pareto fronts, the framework allows for the exploration of fairnessperformance trade-offs and enables the systematic comparison of different algorithmic techniques to increase fairness. A case study compares several fairness metrics and different algorithmic techniques, provides insight into trade-offs found between metrics, and shows how the framework can be leveraged to find a 'best' level of fairness for a given scenario. [ABSTRACT FROM AUTHOR]

Copyright of Congreso Internacional de Investigacion Academia Journals is the property of PDHTech, LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Transportation problem is an integral part of this competitive world. Minimizing transportation cost of a product from sources to destinations is the main objective of traditional transportation problems. In the present context single objective transportation problem fails to meet the managerial decision making requirements which demands the multi objective transportation problems. Many real life problems can be formulated as multi objective transportation problem, and their solution requires consideration of conflicting objectives. Naturally, these problems have a number of potentially Pareto-optimal solutions. Multi-objective transportation problem refers to a special class of vector minimum linear programming problem, which usually involves multiple, conflicting, and incommensurable objective functions. Solving these complexities, the decision maker has to develop a utility function. In this paper, we develop a utility function to attain the targeted goal for a multi objective transportation problem. This utility function is developed using fuzzy membership functions considering deviations of objective function from the goal value where deviation variable has chosen as fuzzy type. Moreover, fuzzy max-min operator has been included for taking limiting value of the objective functions. Performance of the utility function to achieve proper goal is evaluated and justified by solving numerical examples using fuzzy goal programming. [ABSTRACT FROM AUTHOR]

The new organization method, represented by the mixed operation of multi-marshalling trains, can adapt to the characteristics of the uneven distribution of passenger flow, but the research on this aspect is still not perfect enough. This paper introduced the passenger sharing rate and congestion penalty coefficient with different train formations. On this basis, this paper established an optimization model with the minimum passenger cost and operation cost as objective, and operation frequency and passenger demand as constraint. The ideal point method is used to solve this model. Compared with the fixed marshalling operation model, the overall cost of this scheme saves 9.24% and 4.43% respectively. This result not only validates the validity of the model, but also illustrate the advantages of the multiple train formations scheme. [ABSTRACT FROM AUTHOR]

In this paper, we established a half open multi-objective optimization model for the vehicle routing problem of B2C (business-to-customer) E-Commerce firms. To minimize the current Transport distance as well as the disparity between the excepted shipments and the transport capacity in the next distribution, we applied the concept of dominated solution and Pareto solutions to the standard particle swarm optimization and proposed a MOPSO (multi-objective particle swarm optimization) algorithm to support the model. Besides, we also obtained the optimization solution of MOPSO algorithm based on data randomly generated through the System, which verified the validity of the model. [ABSTRACT FROM AUTHOR]

The nurse scheduling problem is among the most widely studied problem in literature. Nurse scheduling is concerned about allocating nurses to conduct specific tasks. The various objectives and constraints of this problem that changes from country to country and from hospital to hospital makes it very hard to solve. Furthermore, lack of resources makes this scheduling task very challenging. Many hospitals are still executing nurses scheduling manually, which is indeed a cumbersome and time consuming task. This, however, may lead to high chances and risks of not fulfilling all rules set by the hospital. Therefore, this article presents a goal programming framework model to scheduling nurses working at the emergency department with the objective to increase fairness in shifts distribution. The experimental results highlights the importance of developing heuristic or meta-heuristic approaches to solve such NP-hard problem. [ABSTRACT FROM AUTHOR]

The reprocessing of tailings is gaining importance both for economic and environmental reasons. In the case of abandoned mining waste disposals, the reprocessing of tailings constitutes an option that can be considered to fit the paradigms of the actual mineral industry challenges: circular economy and sustainability. The present study focuses on the tailings of an old embankment (Cabeço do Pião) of the tungsten Panasqueira mine, in central Portugal. Although Panasqueira mine is still in operation today, it had its production peak during the period of World War II, when tungsten was in great demand. This fact contributed to the disposal of residues with high levels of tungsten for today standards, but also of base metals such as copper and zinc; rare metals, that at the time had no economic value, are also occurring. A project for reprocessing the tailings poses several different objectives and criteria that general are conflicting. This work aims to develop an integrated decision tool to assess and manage the technological, economic, environmental and social criteria that drive the design of Cabeço do Pião tailings reprocessing. Stochastic optimization methods, namely genetic algorithms are used as they allow simultaneous parametric and structural multi-objective optimization. The technical aspect of tailings reprocessing will be covered by multi-objective parametric optimization (W and Zn grades and recoveries) based on mathematical models and processing laboratory results for flotation and leaching. Then, economic, environmental and social constraints will be added to the different technical configurations included in the multi-objective (net smelter return or net operating profit) structural optimization process. [ABSTRACT FROM AUTHOR]

Copyright of CISTI (Iberian Conference on Information Systems & Technologies / Conferência Ibérica de Sistemas e Tecnologias de Informação) Proceedings is the property of Conferencia Iberica de Sistemas Tecnologia de Informacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Temperature measurement by acoustic is a new kind of temperature visualization measurement technology. It is very important to develop three-dimensional temperature field reconstruction algorithm in acoustic application. In order to get the three-dimensional temperature field distribution, a three-dimensional temperature field reconstruction algorithm based on multi-objective optimization is proposed in this paper. A multi-objective optimization model is set up considering least square criterion, smoothness criterion and maximum entropy criterion. Numerical experiments show that the algorithm of image quality is improved obviously compared with that of least squares algorithm. [ABSTRACT FROM AUTHOR]

The work is devoted to the development of a domestic catalyst of ethylene acetoxylation for the synthesis of vinyl acetate is relevant. Non-stationary process of vinyl acetate synthesis from ethylene, oxygen and acetic acid is being implemented in a tubular reactor by passing the gas mixture of the initial reagents through the catalyst bed. It has been established that using hydrothermal treatment of silica gel in an autoclave at varying temperature and processing time it is possible to obtain the samples with different porous structure in a wide range of its change. The catalyst carriers that varied considerably in physical parameters were obtained. |Mathematical model of the catalyst carrier of the process of ethylene acetoxylation for synthesis of vinyl acetate take into account the physical parameters of the catalyst carrier and chemical composition of the catalyst was created. The problem of optimization of the physical parameters of the catalyst carrier; the problem of multi-objective constrained optimization of chemical composition of the catalyst was solved. This optimization allows maximizing the selectivity and the activity of the catalyst and minimizing the content of gold and palladium. These problems have been solved through nonlinear programming methods. [ABSTRACT FROM AUTHOR]

We investigate the optimal tea purchasing and blending problem at a global FMCG company. The company purchases tea leaves from various markets around the world and processes them in its global network of blending facilities. Tea leaves have different attributes such as taste, color and scent that vary depending on the producer and the season, and can be purchased at different price levels subject to market conditions. Leaves are mixed in blending facilities in appropriate quantities to achieve desired attributes of tea blend products. We formulate the optimal tea purchasing and blending problem as a multi-objective optimization problem. We also describe our experience in building a cloud-based decision support system based on our optimization model, which is used by the company on a weekly basis. [ABSTRACT FROM AUTHOR]

According to the fundamental theorems of welfare economics, any competitive equilibrium is Pareto efficient. Unfortunately, competitive equilibrium prices only exist under strong assumptions such as perfectly divisible goods and convex preferences. In many real-world markets, participants have non-convex preferences and the allocation problem needs to consider complex constraints. Electricity markets are a prime example, but similar problems appear in many real-world markets, which has led to a growing literature in market design. [ABSTRACT FROM AUTHOR]

This article addresses the optimization of the quality of an insulating material that is based on wood fibres. In a context where several conflicting objectives must be satisfied simultaneously in the design process, meta-heuristic approaches provide efficient methods for optimization. Multi-objective particle swarm optimization (MOPSO) has been chosen here to solve this complex problem in which physical properties such as thermal conductivity and thickness recovery, that are conflicting, are modelled through heterogeneous variables and nonlinear mathematical models. This is an ongoing work; Influence graph and the first mathematical model are presented in this paper while the preliminary optimization results will be presented during the ESAFROM conference. [ABSTRACT FROM AUTHOR]

Cogeneration systems are known to be efficient power systems for their ability to reduce pollution. Their integration into a grid requires simultaneous consideration of the economic and environmental challenges. Thus, an optimal cogeneration power are adopted to face such challenges. This work presents a novelty in selectinga suitable solution using heuristic optimization method. Its aim is to optimize the cogeneration capacity to be installed according to the economic and environmental concerns. This novelty is based on the sensitivity and data analysis method, namely, Multiple Linear Regression (MLR). This later establishes a compromise between power, economy, and pollution, which leads to find asuitable cogeneration power, and further, to be integrated into a grid. The data exploited were the results of the Genetic Algorithm (GA) multi-objective optimization. Moreover, the impact of the utility's subsidy on the selected power is shown. [ABSTRACT FROM AUTHOR]

Numerical computation of the three dimensional problem of the freezing interface propagation during the cryosurgery coupled with the multi-objective optimization methods is used in order to improve the efficiency and safety of the cryosurgery operations performing. Prostate cancer treatment and cutaneous cryosurgery are considered. The heat transfer in soft tissue during the thermal exposure to low temperature is described by the Pennes bioheat model and is coupled with an enthalpy method for blurred phase change computations. The finite volume method combined with the control volume approximation of the heat fluxes is applied for the cryosurgery numerical modeling on the tumor tissue of a quite arbitrary shape. The flux relaxation approach is used for the stability improvement of the explicit finite difference schemes. The method of the additional heating elements mounting is studied as an approach to control the cellular necrosis front propagation. Whereas the undestucted tumor tissue and destucted healthy tissue volumes are considered as objective functions, the locations of additional heating elements in cutaneous cryosurgery and cryotips in prostate cancer cryotreatment are considered as objective variables in multi-objective problem. The quasi-gradient method is proposed for the searching of the Pareto front segments as the multi-objective optimization problem solutions. [ABSTRACT FROM AUTHOR]

In the deepwater oil-gas exploitation, it is a fast non-weld connection that is the key for subsea structures. Paper lakes the connection between pipelines as an example to design a NSPC. In the works, the design processes, such as formula of contact pressure on the sealing face, critical contact pressure for 8 inch NSPC, CPV, MCP, PFEM created by ANSYS, and optimal design based on MOGA, etc., were discussed and analyzed in details. Four types of experiments of optimized connectors and non-optimized ones are carried out to test the performance in seal and resisting external load. The NSPC is also applied to the connection of other subsea oil-gas production equipment, which provides a valid method for deep water connection. [ABSTRACT FROM AUTHOR]

Current regulations on the Energy Efficiency Design Index (EEDI) have no clear description of the computation method for the index associated with high performance ships. As a supplement, this paper aims to bridge the gap by developing the calculation formula for calculating the EEDI for these ships. Load capacity, ship speed and EEDI in the proposed EEDI formula are significantly affected by the ship's main dimensions. This leads to the need of multi-objective optimizations. To meet the need, the genetic algorithm was used to obtain the pareto frontier. Fifteen groups of optimized results were determined for further decision-making. The final optimal model that determines the weight of different indicators was established by using the rigid least square method. The optimum scheme showed an increase in the load capacity and speed by 16.41% and 0.36%, respectively. In addition, the EEDI was reduced by 14.42% compared to the target ship. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EA) have been systematically developed to solve mono-objective, multi-objective and many-objective optimization problems. In recent works connected with the Genetic Algorithms (GAs) in the design optimization of Electrical Machinery, it has been observed that GAs locate the global optimum region faster than the conventional direct search optimization techniques. In this paper a study of the NSGA-II algorithm is presented alongside with the optimized design results, obtained in the research work. [ABSTRACT FROM AUTHOR]

Past studies have witnessed the wide application of assisted history matching for the calibration of dynamic reservoir models. Although the proposed algorithms have the potential to improve the history matching process in some synthetic cases, most of them have failed or have partially succeeded when applied to real, complex reservoirs. Thus far, identifying the most efficient optimization strategy for history matching has remained a challenging topic for research. In this paper a sequential approach is adopted whereby a reservoir model is replaced by a proxy model and multi-objective optimization algorithms are applied on misfit functions which was defined by combination of the proxy models and historical data. The proposed approach was tested on a case study involving a benchmark synthetic reservoir model with 14 years of production data. The data was freely provided by Imperial College London. The effectiveness of using individual optimization algorithms were quantified by using normalized root mean square error. The proposed approach is found to be efficient, robust and flexible. [ABSTRACT FROM AUTHOR]

One of the most important components of a Plug-In Hybrid Electric Vehicle (PHEV) is the battery pack. A large battery pack will allow the car to travel in charge-sustaining mode for a longer period of time, thereby reducing fuel consumption. However, a large pack will result in a high capital and operating cost. In this paper, the optimal number of batteries for the PHEV is determined. The vehicle model was developed using Autonomie and the optimization algorithm was carried out using the actual vehicle model. Due to the large computational time of the problem, the optimization was also executed using an empirical model. Comparisons are provided between the two approaches in terms of accuracy and computational time. [ABSTRACT FROM AUTHOR]

This article discusses research which proposed a computational tool based on evolutionary algorithms for the synthesis and optimization of submarine pipeline routes. Topics discussed include the reformulation of the modeling of the optimization problem and case studies demonstrating the influence of the engineering criteria, on-bottom stability (OBS) and vortex induced vibrations (VIV) on the optimal route.