Modeling and solving a bi-objective joint replenishment-location problem under incremental discount: MOHSA and NSGA-II

Seyed Hamid Reza Pasandideh (), Seyed Taghi Akhavan Niaki () and Reza Abdollahi ()
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Seyed Hamid Reza Pasandideh: Kharazmi University
Seyed Taghi Akhavan Niaki: Sharif University of Technology
Reza Abdollahi: Islamic Azad University, Qazvin Branch

Operational Research, 2020, vol. 20, issue 4, No 19, 2365-2396

Abstract: Abstract In this paper, the joint replenishment-location problem of some distribution centers (DCs) with a centralized decision maker who is responsible for ordering and dispatching shipments of a single product is modeled. The warehouse spaces of the DCs are limited and the product is sold under an incremental discount policy. The model seeks to minimize the total cost of the supply chain under the joint replenishment policy along with minimizing the cost of locating the DCs in potential sites as the first objective. The second objective is to minimize the warehouse space of all DCs using the revisable approach. As the proposed model is a bi-objective integer non-linear optimization problem (NP-Hard) and cannot be solved by an exact method in a reasonable computational time, a multi-objective harmony search algorithm is developed to solve it. Since there is no benchmark available in the literature, the non-dominated sorting genetic algorithm II is utilized as well to validate the results obtained. As the performance of a meta-heuristic algorithm is largely influenced by the choice of its parameters, the response surface methodology is employed to tune the parameters. Several numerical illustrations are provided at the end to not only demonstrate the application of the proposed methodology, but also to analyze and compare the performances of the two solution algorithms in terms of several multi-objective measures.

Keywords: Joint replenishment problem; Location; Revisable approach; Incremental discount; Multi-objective harmony search; Non-dominated sorting genetic algorithm (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12351-018-0423-0

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