Genetic algorithm for supply chain modelling: basic concepts and applications

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.