Solution approaches for the multiobjective stochastic programming

Abdelaziz, Fouad Ben

doi:10.1016/j.ejor.2011.03.033

Cited by 97 publications

(44 citation statements)

References 35 publications

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“…Z r (x), r = 1) and solve it as a single objective MP problem subject to the constraints. Let x (1) be the ideal solution. Then select the second objective function and find the ideal solution as x (2) , continue the process R times for R different objective functions.…”

Section: Fuzzy Programming Methodsmentioning

confidence: 99%

Solving multi-objective fuzzy probabilistic programming problem

Acharya

Ranarahu

Dash

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

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Most of the real world decision making problems involve uncertainty, which arise due to incomplete information or linguistic information on data. Stochastic programming and fuzzy programming are two powerful techniques to solve such type of problems. Fuzzy stochastic programming is concerned with optimization problems in which some or all parameters are treated as fuzzy random variables in order to capture randomness and fuzziness under one roof. A method for solving multi-objective fuzzy probabilistic programming problem is proposed in this paper. The uncertain parameters are considered as fuzzy log-normal random variables. Since the existing methods are not enough to solve fuzzy probabilistic programming problem directly, therefore the mathematical programming model is transformed to an equivalent multi-objective crisp model. Finally, a fuzzy programming technique is used to solve the multi-objective crisp model. The resulting model is then solved by standard non-linear programming methods. In order to illustrate the methodology a numerical example is provided.

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Section: Fuzzy Programming Methodsmentioning

confidence: 99%

Solving multi-objective fuzzy probabilistic programming problem

Acharya

Ranarahu

Dash

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

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“…The multiobjective stochastic programming is not mathematically well defined [38,[2][3]. So either multiobjective transformation or stochastic transformation must be used.…”

Section: Stochastic Transformationmentioning

confidence: 99%

Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution

Hamadameen

Zainuddin

2014

AIP Conference Proceedings

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This study deals with multiobjective fuzzy stochastic linear programming problems with uncertainty probability distribution which are defined as fuzzy assertions by ambiguous experts. The problem formulation has been presented and the two solutions strategies are; the fuzzy transformation via ranking function and the stochastic transformation when α− cut technique and linguistic hedges are used in the uncertainty probability distribution. The development of Sen's method is employed to find a compromise solution, supported by illustrative numerical example.

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“…Focusing on the uncertainty issues, a great number of stochastic optimization models are applied in formulating and resolving complex decision-making problems in management science, and the basic idea to resolve a stochastic optimization problem is to convert the original problem into several deterministic optimization problems [43]. Scenario-based solution method is an effective and efficient approach to resolve stochastic optimization problem due to its simplicity and applicability [44,45].…”

Section: Multi-criteria Scenario-based Solution Methodsmentioning

confidence: 99%

A Stochastic Programming Approach with Improved Multi-Criteria Scenario-Based Solution Method for Sustainable Reverse Logistics Design of Waste Electrical and Electronic Equipment (WEEE)

Solvang

2016

Sustainability

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Today, the increased public concern about sustainable development and more stringent environmental regulations have become important driving forces for value recovery from end-of-life and end-of use products through reverse logistics. Waste electrical and electronic equipment (WEEE) contains both valuable components that need to be recycled and hazardous substances that have to be properly treated or disposed of, so the design of a reverse logistics system for sustainable treatment of WEEE is of paramount importance. This paper presents a stochastic mixed integer programming model for designing and planning a generic multi-source, multi-echelon, capacitated, and sustainable reverse logistics network for WEEE management under uncertainty. The model takes into account both economic efficiency and environmental impacts in decision-making, and the environmental impacts are evaluated in terms of carbon emissions. A multi-criteria two-stage scenario-based solution method is employed and further developed in this study for generating the optimal solution for the stochastic optimization problem. The proposed model and solution method are validated through a numerical experiment and sensitivity analyses presented later in this paper, and an analysis of the results is also given to provide a deep managerial insight into the application of the proposed stochastic optimization model.

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Solution approaches for the multiobjective stochastic programming

Cited by 97 publications

References 35 publications

Solving multi-objective fuzzy probabilistic programming problem

Solving multi-objective fuzzy probabilistic programming problem

Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution

A Stochastic Programming Approach with Improved Multi-Criteria Scenario-Based Solution Method for Sustainable Reverse Logistics Design of Waste Electrical and Electronic Equipment (WEEE)

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