Volume 56, Number 5, September-October 2022
|3561 - 3579
|19 October 2022
A congested capacitated location problem with continuous network demand
University of Haute-Alsace, IRIMAS UR 7499, 68100 Mulhouse, France
2 Alliance Manchester Business School, University of Manchester, Manchester M15 6PB, UK
3 University of Haute-Alsace IRIMAS UR 7499, 68100 Mulhouse, France
* Corresponding author: email@example.com
Accepted: 24 September 2022
This paper presents a multi-objective mixed-integer non-linear programming model for a congested multiple-server discrete facility location problem with uniformly distributed demands along the network edges. Regarding the capacity of each facility and the maximum waiting time threshold, the developed model aims to determine the number and locations of established facilities along with their corresponding number of assigned servers such that the traveling distance, the waiting time, the total cost, and the number of lost sales (uncovered customers) are minimized simultaneously. Also, this paper proposes modified versions of some of the existing heuristics and metaheuristic algorithms currently used to solve NP-hard location problems. Here, the memetic algorithm along with its modified version called the stochastic memetic algorithm, as well as the modified add and modified drop heuristics are used as the solution methods. Computational results and comparisons demonstrate that although the results obtained from the developed stochastic memetic algorithm are slightly better, the applied memetic algorithm could be considered as the most efficient approach in finding reasonable solutions with less required CPU times.
Mathematics Subject Classification: 90B80 / 90C27 / 90C11
Key words: Capacitated facility location problem / queuing theory / continuous network demand / heuristics and metaheuristic algorithms
© The authors. Published by EDP Sciences, ROADEF, SMAI 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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