Modelling JIT supply chains by simulation and hybrid genetic variable neighbourhood search algorithm

Abstract

The objective of this study is investigating multi-stage supply chain systems which is controlled using a kanban system so as to analyse and optimise it. In this paper, a new optimisation approach based on simulation analysis and metaheuristic methods to optimise a multi-echelon supply chain in a JIT production context is proposed. A hybrid of simulation modelling, genetic algorithm (GA), and variable neighbourhood search (VNS) to solve the above mentioned problem was proposed. In this research paper, GA-VNS is used iteratively to optimise the supply chain. Furthermore, the performance of the GA and VNS compared with their GA-VNS counterpart based on their relative error, and it is illustrated that the GA-VNS has ability to solve NP-hard problems in the area of complicated simulation optimisation models, especially where there is no prior knowledge of the behaviour of the system.