University of Tehran Press Interdisciplinary Journal of Management Studies 2981-0795 10 3 2017 07 01 Evaluating the Effectiveness of Integrated Benders Decomposition Algorithm and Epsilon Constraint Method for Multi-Objective Facility Location Problem under Demand Uncertainty ارزیابی اثربخشی الگوریتم یکپارچه تجزیه بندرز و روش اپسیلون-محدودیت برای مساله موقعیت یابی چند هدفه تحت شرایط عدم قطعیت تقاضا 551 576 63115 10.22059/ijms.2017.229298.672575 EN Iman Rahimi Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University Putra Malaysia, Malaysia Sai Hong Tang Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University Putra Malaysia, Malaysia Abdollah Ahmadi Australian Energy Research Institute and the School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2032, Australia Siti Azfanizam Binti Ahmad Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University Putra Malaysia, Malaysia Lai Soon Lee Department of Mathematics, Faculty of Science, 43400 UPM Serdang, Selangor Malaysia, Adel M. Sharaf Department of Electrical and Computer Engineering, University of New Brunswick, P.O. Box 4400-UNB, Fredericton, NB, Canada E3B 5A3 Journal Article 2017 03 06 One of the most challenging issues in multi-objective problems is finding Pareto optimal points. This paper describes an algorithm based on Benders Decomposition Algorithm (BDA) which tries to find Pareto solutions. For this aim, a multi-objective facility location allocation model is proposed. In this case, an integrated BDA and epsilon constraint method are proposed and it is shown that how Pareto points in multi-objective facility location model can be found. Results are compared with the classic form of BDA and the weighted sum method for demand uncertainty and deterministic demands. To do this, Monte Carlo method with uniform function is used, then the stability of the proposed method towards demand uncertainty is shown. In order to evaluate the proposed algorithm, some performance metrics including the number of Pareto points, mean ideal points, and maximum spread are used, then the t-test analysis is done which points out that there is a significant difference between aforementioned algorithms. https://ijms.ut.ac.ir/article_63115_babf90c6636ea4a0b85e945f6a608fb0.pdf