Two effective metaheuristic algorithms for solving a stochastic optimization model of a multi-echelon supply chain

Production distribution network (PDN) planning problems in multi echelon status are commonly complex with dynamic relationships that cause several uncertainties in different parameters of the network. In this paper, we formulate a multi echelon PDN to deliver products to customers with uncertain dem...

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Bibliographic Details
Published in:Applied soft computing Vol. 76; pp. 545 - 563
Main Authors: Khalifehzadeh, Sasan, Fakhrzad, M.B., Zare Mehrjerdi, Yahia, Hosseini_Nasab, Hasan
Format: Journal Article
Language:English
Published: Elsevier B.V 01.03.2019
Subjects:
Allowable shortage
Multi transportation systems
Ranking Genetic Algorithm (RGA)
Selective Firefly Algorithm (SFA)
Stochastic delivery lead time
Stochastic demand
Allowable shortage
Stochastic delivery lead time
Selective Firefly Algorithm (SFA)
Multi transportation systems
Ranking Genetic Algorithm (RGA)
Stochastic demand
ISSN:1568-4946, 1872-9681
Online Access:Get full text
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Summary:Production distribution network (PDN) planning problems in multi echelon status are commonly complex with dynamic relationships that cause several uncertainties in different parameters of the network. In this paper, we formulate a multi echelon PDN to deliver products to customers with uncertain demand in the least time with uncertain delivery lead time. The proposed network is including multi supplier, producer, potential depot, retailer and inland and outland customer in multi time period horizon. A stochastic multi objective model with maximizing total profit of the system and minimizing total delivery lead time is designed. We apply chance constraints approach to cover the uncertainty of the model and introduce two heuristic methods named selective firefly algorithm (SFA) and ranking genetic algorithm (RGA) in order to solve several sized especially real world instances. Finally, the performance of two proposed algorithms is examined with solving several sized instances. The results indicate average improvement 6.12% and 8.93% with applying SFA and RGA, respectively. [Display omitted] •A multi echelon supply chain network under uncertain environment is proposed.•The customers’ demands are assumed stochastic with a known normal distribution.•Delivery lead time is assumed stochastic with a known normal distribution.•A MILP model is designed to maximize profit and minimize lead time of the network.•Two novel metaheuristic algorithms based on FA and GA (SFA and RGA) are proposed.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2018.12.018