Volume 50, Number 4-5, October-December 2016
Special issue - Advanced Optimization Approaches and Modern OR-Applications
|Page(s)||869 - 890|
|Published online||03 November 2016|
A novel meta-heuristic algorithm for multi-objective dynamic facility layout problem
1 Planning Directorate, National
Iranian Oil Products Distribution Company, Qazvin, Iran.
2 Department of Industrial Engineering, Alzahra University, Deh Vank, Tehran, Iran.
Accepted: 30 August 2016
This paper proposes an integrated approach for dynamic facility layout problem considering the material handling equipment (MHE). The objectives of this problem are minimization of the fixed costs of MHE, minimization of material handling cost (MHC) and minimization of machine rearrangement costs (MRC). To be more realistic, MHE fixed costs, MRC and MHC, which might be of different importance to decision maker, are considered separately in three objective functions. An integrated model is proposed which is able to simultaneously select the MHE along with the arranging and re-arranging facilities. The model belongs to the class of multi-objective nonlinear mathematical programming models. Considering the NP-hard nature of the model and inspiring the existing water flow like algorithm, a novel Pareto-based meta-heuristic algorithm called multi-objective water flow like algorithm (MOWFA)is developed to solve the problem. We comprehensively discuss the parameter tuning of the algorithms utilizing Taguchi method. Finally, the performance of the proposed MOWFA is evaluated against two well-known meta-heuristic algorithms called non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA). Computational results indicate the greater efficiency of the algorithm compared to the two addressed algorithms for solving the given multi-objective problem.
Mathematics Subject Classification: 90B50 / 90B80
Key words: Multi-objective dynamic facility layout problem / water flow like algorithm / manufacturing facility / material handling / Taguchi method
© EDP Sciences, ROADEF, SMAI 2016
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