Authors: Hokey Min
Addresses: Department of Management, College of Business Administration, Bowling Green State University, BAA 3008C, Bowling Green, Ohio 43403, USA
Abstract: As a subfield of artificial intelligence, genetic algorithm (GA) was introduced in the 1970s to tackle various types (both continuous and discrete) of combinatorial decision problems facing many business enterprises. These problems include routine but complex managerial challenges associated with supply chain activities of sourcing, making, selling, and delivering goods and services. With the emergence of supply chain principles in today's business world, GA has increased its role in improving managerial decision-making processes and subsequently enhancing supply chain efficiency by avoiding the sub-optimisation of problem solutions. Despite its application potentials, we have seen the limited use of GA for supply chain management. To make the best use of GA for supply chain management, this paper introduces the theoretical underpinning of GA and then explains how effectively it works for solving difficult supply chain problems. In so doing, this paper reviews the past record of success in GA applications to supply chain fields and then identifies the most promising areas of supply chain management in which to apply GA.
Keywords: genetic algorithms; supply chain management; SCM; metaheuristics; supply chain modelling.
International Journal of Services and Operations Management, 2015 Vol.22 No.2, pp.143 - 164
Received: 07 Apr 2014
Accepted: 15 May 2014
Published online: 31 Aug 2015 *