Solving a location-allocation problem by a fuzzy self-adaptive NSGA-II

Document Type : Research Paper

Authors

1 Industrial Engineering department, South branch of Azad University, Tehran, Iran

2 School of Industrial Engineering, College of Engineering, University of Tehran,Tehran, Iran

Abstract

This paper proposes a modified non-dominated sorting genetic algorithm (NSGA-II) for a bi-objective location-allocation model. The purpose is to define the best places and capacity of the distribution centers as well as to allocate consumers, in such a way that uncertain consumers demands are satisfied. The objectives of the mixed-integer non-linear programming (MINLP) model are to (1) minimize the total cost of the network and (2) maximize the utilization of distribution centers. To solve the problem, a fuzzy modified NSGA-II with local search is proposed. To illustrate the results, computational experiments are generated and solved. The experimental results demonstrate that the performance metrics of the fuzzy modified NSGA-II is better than the original NSGA-II.

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Main Subjects


Ahmadi, G., Torabi, S.A. and Tavakkoli-Moghaddam‎, R. (2016). A bi-objective location-inventory model with capacitated transportation and lateral transshipments, Int. J. of Production Research, 54(7), 2035-2056.
Cicek, C.T., Gultekin, H. and Tavli, B. (2019). The location-allocation problem of drone base stations. Computers & Operations Research, 111, 155-176.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. John Wiley & Sons.
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.
Kahag, M.R., Akhavan Niaki, S.T., Seifbarghy, M. and Zabihi, S. (2019). Bi-objective optimization of multi-server intermodal hub-location-allocation problem in congested systems: modeling and solution. Journal of Industrial Engineering International, 15(2), 221-248.
Kaya, O. and Urek, B. (2016). A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain. Computers & Operations Research, 65, 93-103.
Mehrabad, M.S., Aazami, A. and Goli, A. (2017). A location-allocation model in the multi-level supply chain with multi-objective evolutionary approach. Journal of Industrial and Systems Engineering, 10(3), 140-160.
Mousavi, S., Alikar, N., Niaki, S. and Bahreininejad, A. (2015). Optimizing a location allocation-inventory problem in a two-echelon supply chain network: A modified fruit fly optimization algorithm. Computers & Ind. Eng., 87, 543-560.
Nobari, A., Kheirkhah, A. and Esmaeili, M. (2019). Considering chain to chain competition in forward and reverse logistics of a dynamic and integrated supply chain network design problem. Journal of Industrial and Systems Engineering,12(1), 147-166.
Puga, M. and Tancrez, J. (2016). A heuristic algorithm for solving large location–inventory problems with demand uncertainty. European Journal of Operational Research, 259(2), 413-423.
Sadati, A., Tavakkoli-Moghaddam, R., Naderi, B. and Mohammadi, M. (2019). A bi-objective model for a scheduling problem of unrelated parallel batch processing machines with fuzzy parameters by two fuzzy multi-objective meta-heuristics. Iranian Journal of Fuzzy Systems, 16(4), 21-40.
Sadeghi, J., Sadeghi, S. and Niaki, S.T.A. (2014). A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: An NSGA-II with tuned parameters. Computers & Operations Research, 41, 53-64.
Salehi, H., Tavakkoli-Moghaddam, R. and Nasiri, G. (2015). A multi-objective location-allocation problem with lateral transshipment between distribution centres. International J. of Logistics Systems and Management, 22(4), 464-482.
Sardou, I. and Ameli, M. (2016). A fuzzy-based non-dominated sorting genetic algorithm-II for joint energy and reserves market clearing. Soft Computing, 20(3), 1161-1177.
Soolaki, M. and Arkat, J. (2018). Supply chain design considering cellular structure and alternative processing routings. Journal of Industrial and Systems Engineering, 11(1), 97-112.
Srinivas, N. and Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), pp. 221-248.
Yadegari, E., Alem-Tabriz, A. and Zandieh, M. (2019). A Memetic Algorithm with a Novel Neighborhood Search and Modified Solution Representation for Closed-loop Supply Chain Network Design. Computers & Industrial Engineering, 128, 418-436.
Zahiri, B., Jula, P. and Tavakkoli-Moghaddam, R. (2018). Design of a pharmaceutical supply chain network under uncertainty considering perishability and substitutability of products. Information Sciences, 423, 257-283.
Zhen, L., Wu, Y., Wang, S., Hu, Y. and Yi, W. (2018). Capacitated closed-loop supply chain network design under uncertainty. Advanced Engineering Informatics, 38, 306-315.
Zheng, X., Yin, M. and Zhang. Y. (2019). Integrated optimization of location, inventory and routing in supply chain network design. Transportation Research Part B, 121, 1-20.