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

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]

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