Facility layout problem under uncertainty
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Abstract
The facility layout problem has usually been treated as a deterministic problemand uncertainty regarding the problem parameters has seldom been addressed. In thisstudy, the aim is to investigate dierent ways of dealing with uncertainty to design afacility layout which attains robust and ecient performance under all possible scenarios.For this purpose, seven mathematical models based on the Quadratic AssignmentProblem (QAP) formulation have been developed. These formulations cover alternativemethodologies existing in stochastic and robust optimization literature such as minimizingmaximum cost, expected cost, maximum regret and p-robustness as well as newapproaches that combine them in dierent ways. The proposed models are solved usingGenetic Algorithms (GA) incorporating operators and local improvement schemes thatare specially selected and adapted for the models. As two of the models involve multipleobjective functions, a Multi-Objective Evolutionary Algorithm (MOEA) has alsobeen developed. Extensive numerical analysis enables us to compare the performanceof these approaches in terms of robustness metrics and to gain important insights intoways of treating the uncertainty issue in facility layout problem.
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