Developing a hybrid genetic algorithm solution to optimise multi–echelon supply chains. (13th January 2014)

• Record Type: Journal Article
• Authors: Mahmoud, Haitham A.; Hassan, Mohammed H.; Nasr, Emad S. Abouel
• Is Part Of: International journal of collaborative enterprise. Volume 3:Number 4(2013)
• Page Start: 287
• Page End: 313
• Publication Date: 2014-01-13
• Subjects: dynamic supply chains -- forward logistics networks -- genetic algorithms -- pattern search -- hGAs -- hybrid GAs -- experimental study -- optimisation -- multi–echelon supply chains -- supply chain management -- SCM -- dynamic modelling -- mixed integer linear programming -- MILP; 338.805
• Languages: English
• ISSNs: 1740-2085

Abstract

The design of dynamic supply chain (DSC) models has received a great attention in the last two decades. In this paper, a forward supply chain network (SCN) model is developed in which both dynamic facility locations and dynamic distributed quantities of materials and products are assumed. The problem is formulated mathematically using mixed integer linear programming (MILP). The solution methodology adopted is the hybrid genetic algorithm (hGA) comprising genetic algorithm (GA) and pattern search (PS) optimisation techniques. Two experimental studies are conducted to analyse the developed SCN model and the developed hGA. The results of the hGA analysis show that the accuracy of the proposed hGA is improved in the cases of large number of nodes of the SC's echelons, small number of manufactured products and large quantities of products' demands.