Title: Genetic algorithm for inventory positioning problem with general acyclic supply chain networks

Authors: Dali Jiang; Haitao Li; Tinghong Yang; De Li

Addresses: Institute of Modern Logistics, Logistical Engineering University, Chongqing 401311, China ' School of Economics, Tianjin University of Commerce, Tianjin 300134, China; Department of Logistics and Operations Management, College of Business Administration, University of Missouri – St. Louis, One University Blvd, St. Louis, MO 63121, USA ' Institute of Modern Logistics, Logistical Engineering University, Chongqing 401311, China ' Institute of Modern Logistics, Logistical Engineering University, Chongqing 401311, China

Abstract: Inventory positioning, also known as safety stock placement, in supply chain networks is an important optimisation problem that has various applications in supply chain design and configuration. In this paper, we develop a new genetic algorithm (GA) for this NP-hard problem. Our new GA features custom designed procedure to generate feasible individuals by exploiting the problem structure. It also implements a multi-start strategy to enhance solution quality. Computational results show that our GA is able to offer near optimal solutions in reasonable computational time. [Received 4 October 2014; Revised 19 October 2015; Revised 14 January 2016; Accepted 18 January 2016]

Keywords: inventory positioning; safety stock placement; general acyclic networks; genetic algorithms; GAs; supply chain design; supply chain management; SCM; supply networks; optimisation; supply chain configuration.

DOI: 10.1504/EJIE.2016.076385

European Journal of Industrial Engineering, 2016 Vol.10 No.3, pp.367 - 384

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 05 May 2016 *

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