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作者： Eren Ozbay Bilkent University

学位级别：硕士

In some cases, natural disasters happen successively (e. g. a tsunami following an earthquake) in close proximity of each other, even if they are not correlated. This study locates shelter sites and allocates the affected population to the established set of shelters by considering the aftershock(s) following the initial earthquake, via a three-stage stochastic mixed-integer programming model. In each stage, before the uncertainty, which is the number of affected people, in the corresponding stage is resolved, shelters are established, and after the uncertainty is resolved, affected population is allocated to the established set of shelters. To manage the inherent risk related to the uncertainty, conditional value-at-risk is utilized as a risk measure in allocation of victims to the established set of shelters. Computational results on the Istanbul dataset are presented to emphasize the necessity of considering secondary disaster(s), along with a heuristic method to improve the solution times and qualities. During these computational analyses, it is observed that the original single-objective model poses some obstacles in parameter selection. As in humanitarian operations, choosing parameters may cause conflict of interests and hence may be criticized, a multi-objective framework is developed with various formulations. Some generalizations regarding the performance and applicability of the developed formulations are discussed and finally, another heuristic for the multi-objective formulation is presented to tackle the curse of dimensionality and improve the solution times.

关键词： Shelter Site Location Secondary Disasters multi-Stage Stochastic programming Conditional Value-at-Risk multi objective programming

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multi objective stochastic microgrid scheduling incorporating dynamic voltage restorer

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2017年第Dec.期93卷 316-327页

作者： Jirdehi, Mehdi Ahmadi Tabar, Vahid Sohrabi Hemmati, Reza Siano, Pierluigi Kermanshah Univ Technol Dept Elect Engn Kermanshah Iran Univ Salerno Dept Ind Engn Salerno Italy

This paper focus on optimal scheduling of microgrid including thermal and electrical loads, renewable energy sources (solar and wind), combined heat and power (CHP), conventional energy sources (boiler and micro turbine), energy storage systems (thermal and electrical storages), and series flexible alternating current transmission system (FACTS) devices. Dynamic Voltage Restorer (DVR) is included in the line between the main network and the microgrid in order to achieve a higher power transfer to the upstream grid. In the proposed method, wind speed, solar radiation, and loads are modelled as uncertain parameters based on a stochastic approach. The problem is modelled as a linear, mixed integer, constrained, and multi objective optimization one aiming at minimizing cost and pollution at the same time. Also, a sensitivity analysis is proposed for studying the sensitive parameters in microgrid management. The proposed multi objective and stochastic problem is solved by using the augmented Epsilon-constraint method. All results and calculations are obtained by using GAMS24.1.3/CPLEX12.5.1. Finally, in order to confirm the results of the proposed method, final results are compared to the genetic algorithm method. Simulation results demonstrate the viability and effectiveness of the proposed scheduling method for microgrid. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Augmented Epsilon-constraint method Dynamic voltage restorer Microgrid programming multi objective programming Stochastic programming

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Road to robust prediction of choices in deterministic MCDM

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第1期259卷 229-235页

作者： Pajala, Tommi Korhonen, Pekka Wallenius, Jyrki Aalto Univ Sch Business Dept Informat & Serv Econ POB 21220 Aalto 00076 Finland

We compare five different prediction methods (linear estimated weights, AHP weights, equal weights, logistic regression, and a lexicographic method) in their success rate for predicting preferences in pairwise choices. Students were asked to make pairwise comparisons between student apartments on four criteria: size, rent, travel time to the university and travel time to a (hobby) location of their choice. First ten choices were used to set up the estimation model, and subsequent ten choices are used for prediction. We find that the linear estimation method has the highest prediction success rate. Furthermore, the probability of predicting a choice correctly differs only slightly (by 0.1) between linear consistent and inconsistent subjects, ie. subjects whose preferences were consistent or inconsistent with a linear value function. This shows that in the absence of other preference information, a linear value function is suitable for prediction purposes. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multi objective programming Preference prediction multiple criteria decision making Linear value function Bayesian model

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A NEW MODEL FOR PROBABILISTIC multi-PERIOD multi-objective PROJECT SELECTION PROBLEM 24

A NEW MODEL FOR PROBABILISTIC MULTI-PERIOD MULTI-OBJECTIVE P...

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24th International Conference on Production Research (ICPR)

作者： Khalili-Damghani, K. Poortarigh, M. Pakgohar, A. Islamic Azad Univ Tehran South Branch Dept Ind Engn Tehran Iran Econ Sci Univ Tehran Iran Sheffield Business Sch Dept Finance Accounting & Business Syst Sheffield S Yorkshire England

ISBN: (纸本)9781605955070

The project selection problem is considered as one of the most imperative decisions for investor organizations. Due to non-deterministic nature of some criteria in the real world projects in this paper, a new model for project selection problem is proposed in which some parameters are assumed probabilistic. This model is formulated as a non-linear, multi-objective, multi-period, zero-one programming model. Then the epsilon constraint method and an algorithm are applied to check the Pareto front and to find optimal solutions. A case study is conducted to illustrate the applicability and effectiveness of the approach, with the results presented and analysed. Since the proposed model is more compatible with real world problems, the results are more tangible and trustable compared with deterministic cases. Implications of the proposed approach are discussed and suggestions for further work are outlined.

关键词： Project selection multi objective programming chance constraint epsilon constraint method

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Modelo multiobjetivo de análise envoltória de dados combinado com desenvolvimento de funções empíricas e otimização via simulação Monte Carlo

Modelo multiobjetivo de análise envoltória de dados combin...

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作者： Figueiredo, Marcelo Vilela Universidade Estadual Paulista

O controle de qualidade é um dos principais pilares para um bom rendimento de uma linha produtiva, visando garantir maior eficiência, eficácia e redução de custos de produção. A identificação de causas de defeitos e o controle das mesmas é uma atividade relativamente complexa, devido à infinidade de variáveis presentes em determinados processos. Na produção de itens à base de aço fundido, objetiva-se reduzir defeitos de fundição (rechupes, trincas, problemas dimensionais, entre outros), os quais podem ser ocasionados por diversas variáveis de processo, tais como: composição química do aço, temperatura de vazamento e propriedades mecânicas. Em virtude disso, o presente trabalho foi desenvolvido em uma indústria siderúrgica de grande porte, a qual atua na produção de componentes ferroviários e industriais. Por meio de sua extensa base de dados, foram avaliadas as eficiências dos produtos produzidos, sendo os mesmos denominados DMU (Decision Making Units). Para tal foi aplicada a BiO-MCDEA (Bi objective Data Envelopment Analysis) em sete DMUs produzidas à base de aço fundido em função de 38 variáveis de processos. Nesta aplicação foram evidenciadas as variáveis de processos (input/output) influentes na determinação da eficiência das DMUs. Uma vez obtidos tais resultados, foram desenvolvidas funções empíricas para as variáveis respostas em função das variáveis de processos influentes por meio de regressão não-linear múltipla. Por fim foi realizada a Otimização via Simulação Monte Carlo de forma a determinar com quais valores se deve trabalhar com cada input para a otimização das funções empíricas. Os resultados obtidos foram satisfatórios, sendo bem condizentes com a realidade da empresa e a abordagem aplicada por meio da combinação de diferentes ferramentas se mostrou aderente à realidade estudada, e também *** control is one of the pillars to guaranty a good yield on a production line, aiming to reach better efficiency, effectiveness and reduction of

关键词： Programação multiobjetivo BiO-MCDEA Simulação Monte Carlo Regressão não-linear múltipla Otimização multi objective programming Monte Carlo Simulation Non-linear multiple regression Optimization Dissertação de mestrado

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Bi-objective optimization of three echelon supply chain involving truck selection and loading using NSGA-II with heuristics algorithm

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APPLIED SOFT COMPUTING 2016年 38卷 978-987页

作者： Chan, Felix T. S. Jha, Aditya Tiwari, Manoj K. Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India Indian Inst Technol Dept Ind & Syst Engn Kharagpur 721302 W Bengal India

This paper models a three echelon supply chain distribution problem considering multiple time periods, multi-products and uncertain demands. To take the problem closer to reality we consider multiple truck types and focus on the truck selection and loading sub-problem. Truck selection is important because the quantity of goods to be transported varies regularly and also because different trucks have different hiring cost, mileage and speed. Truck loading is important when considering the optimal loading pattern of products having different shapes and sizes on trucks, which themselves have distinct loading capacities. The two objectives considered here are the cost and responsiveness of the supply chain. The distribution problem is solved using the non-dominated sorting genetic algorithm (NSGA-II). However, the genetic algorithms compromise the optimality of the sub-problems while optimizing the entire system. But the optimality of truck selection and loading sub-problem is non-compromisable in nature. Hence a heuristic algorithm is used innovatively along with the NSGA-II to produce much better solutions. To make our model more realistic, the distribution chain is modelled as a push-pull based supply chain having multiple time periods and using demand aggregation over time. Using a separate algorithm also gives the advantage of utilizing the difference in nature of the push and pull part of the supply chain by giving every individual truck different objectives. Real life like data is generated and the optimality gap between the heuristic and non-heuristic approach is calculated. A clear improvement in objectives can be seen while using the heuristic approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Genetic algorithm Supply chain management multi objective programming Heuristic Transportation Logistics

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A novel fuzzy goal programming approach with preemtive structure for optimal investment decisions

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015年第2期28卷 633-645页

作者： Keskin, Ridvan Kocadagli, Ozan Cinemre, Nalan Celal Bayar Univ Fac Econ & Adm Sci Dept Econometr Merkez Turkey Mimar Sinan Fine Arts Univ Dept Stat Istanbul Turkey

In this article, a novel portfolio selection model is proposed. This model is essentially based on the fuzzy goal programming with preemptive structure. In order to construct this model;the portfolio risk, the return levels and the beta coefficient introduced in the Capital Asset Pricing Model are considered as the fuzzy goals, and then a preemptive priority is defined among them. In accordance with the market moving trends, the different portfolio selection models are constituted for different types of investor behaviors. This model not only takes into account the different investor strategies with respect to the market moving trends, but provides also a reasonable diversification for a portfolio in terms of the risk - return tradeoff together with beta coefficient. In the application sections, the two different periods having the upward and the downward moving trends in the Istanbul Stock Exchange National 30 Index are handled separately, then the optimal portfolios are determined using the proposed portfolio selection model accordance with different investment strategies. Finally, the optimal portfolios are compared in terms of their return performances based on the selling prices in the test periods.

关键词： multi objective programming fuzzy goal programming preemptive priority portfolio selection model capital asset pricing model

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Research on the model of creative flat folding table

Research on the model of creative flat folding table

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2016 3rd International Conference on Materials Engineering,Manufacturing Technology and Control(ICMEMTC 2016)

作者： Yanbo Wang North China Electric Power University

In the subject one, we firstly number the legs of the table. According to the Pythagorean theorem, we can calculate the length of the table legs. Then describe the dynamic folding process of table by the change condition of angle between table leg and the ground, also we can understand the change of this angle during the process of folding;then we will calculate the distance between steel bar and the top of the table leg under the condition of normal status of and when it is under the usage respectively. Then we can solve the upper and lower boundary of the wooden slot of the table legs, as well as the length of slot;the establishment of rectangular coordinate system shall take the straight line where is the reinforce located as X axis, the vertical direction as the Z axis, the first wood reinforced position as the origin of coordinates. Representing the coordinates of each end of the batten with coordinate, then connecting each spot by using matlab, we can get the image of edge line of the table foot.

关键词： multi objective programming pressure bar stability dynamic simulation curve equation

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Application of a fuzzy service level constraint for solving a multi-objective location-routing problem for the industrial hazardous wastes

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015年第5期28卷 2003-2013页

作者： Ghezavati, Vahidreza Morakabatchian, Shabnam Islamic Azad Univ Sch Ind Engn South Tehran Branch Tehran Iran

In this paper, a multi objective location-routing model is developed and solved for a real case related to Petrochemical Special Economic Zone (PETZONE) located in Khuzestan (Iran). Decision makers can decide on optimum locations of suitable treatment, recycling and disposal centers, and also the routes for transporting of different types of industrial wastes between these centers by the help of the model. There are considered three criteria in the mathematical model which minimizing total costs of transportation and establishment of the centers and total transportation and site risks related to the population exposure along transportation routes and around treatment and disposal centers. During the optimization process, waste collection system is improved by considering fuzzy customer satisfaction level. Finally, the model is computed and solved by Lingo software to find efficient solutions.

关键词： multi objective programming location-routing problem fuzzy time windows industrial hazardous waste management capacitated vehicle location-routing problem

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Linearization approach to multi objective set covering problem with imprecise nonlinear fractional objectives

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OPSEARCH 2015年第3期52卷 597-615页

作者： Upmanyu, Mudita Saxena, Ratnesh R. Univ Delhi Dept Math Delhi India Univ Delhi Deen Dayal Upadhyaya Coll Dept Math Delhi India

The Set Covering Problem is one of most important NP-complete 0-1 integer programming problems as it serves as a model for many real world problems, like crew scheduling problems, facility location problems, vehicle routing etc. This paper develops an enumerative technique for solving the multiobjective set covering problem with fuzzy nonlinear fractional objectives, under certain convexity conditions. To mirror real life situations in the covering problem we have taken the objective functions in a nonlinear fractional form. In addition to that, the coefficients of the objective functions are of a fuzzy nature, as in most cases the decision maker is unable to provide information of a precise nature. The algorithm provided in this paper aims to not only solve this covering problem to obtain a set of efficient cover solutions, but also to provide the decision maker with a fuzzy solution for the same. It is based on a linearization technique with the subsequent application of the cutting plane approach, but it employs a more generalized and much deeper form of the Dantzig cut. An example to illustrate the technique is presented, and a few applications of this problem are also described.

关键词： Set covering problem Fuzzy objective functions multi objective programming Fractional programming

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