Collaborative supply chain network using embedded genetic algorithms

The aim of this paper is to propose a genetic algorithms approach to develop a collaborative supply chain network, i.e. a supply chain network with genetic algorithms embedded (GA-SCN), so as to increase the efficiency and effectiveness of a supply chain network. The methodologies of the GA-SCN are illustrated through a case study of a supply chain network of a Hong Kong lamp manufacturing company involving 10 entities, whose roles range from suppliers, purchasers, designers and manufacturers, to sales and distributors. A GA-SCN is developed according to the information provided by the company, the performance results in the case study are discussed, and the concepts of network analysis are then introduced to analyze the equivalence structure of the developed GA-SCN. The genetic algorithms approach is a suitable approach for developing an efficient and effective supply chain network in terms of shortening the processing time and reducing operating time in the network: the processing time and operating cost are reduced by around 45% and 35% per order, respectively, in the case study. This paper is the first known study to apply genetic algorithms for the development of a collaborative supply chain network to increase the competitiveness of a supply chain.