Title: Location-inventory-reliability optimisation problem in a multi-objective multi-period three-level supply chain network with stochastic demand

Authors: Farid Abdi; Hiwa Farughi; Heibatolah Sadeghi; Jamal Arkat

Addresses: University of Kurdistan, Pasdaran St., Sanandaj, 66177-15175, Iran ' Faculty of Industrial Engineering, University of Kurdistan, Pasdaran St., Sanandaj, 66177-15175, Iran ' Faculty of Industrial Engineering, University of Kurdistan, Pasdaran St., Sanandaj, 66177-15175, Iran ' Faculty of Industrial Engineering, University of Kurdistan, Pasdaran St., Sanandaj, 66177-15175, Iran

Abstract: One of the efficient methods of improving the reliability of factories is allocating appropriate redundant components that play an important role in responding to customers' demands, timely delivery and cost reduction. In this study, the issue of simultaneous optimisation of facility location-inventory-redundancy allocation has been investigated. In this regard, a multiple-period three-level problem has been taken into account. It has been assumed that demand for each retailer is stochastic and follows the normal distribution. In order to deal with the fluctuations of demand, the risk pooling effect has been applied. For this purpose, an integer nonlinear programming model has been proposed to optimise the cost of the supply chain as well as its reliability. Since facility location-inventory and redundancy allocation are categorised as NP-hard problems, non-dominated sorting genetic algorithm (NSGA-II) and archived multi-objective simulated annealing (AMOSA) algorithms have been developed for solving the aforementioned problem. Finally, their results have been evaluated by using comparison metrics of multi-objective algorithms. [Submitted: 9 September 2021; Accepted: 9 March 2022]

Keywords: supply chain management; location-inventory model; redundancy allocation; stochastic demand; risk pooling; non-dominated sorting genetic algorithm II; NSGA-II; archive multi-objective simulated annealing; Taguchi methods.

DOI: 10.1504/EJIE.2023.131743

European Journal of Industrial Engineering, 2023 Vol.17 No.4, pp.479 - 528

Received: 09 Sep 2021
Accepted: 09 Mar 2022
Published online: 30 Jun 2023 *

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