Optimization of the supply chain network: Simulation, Taguchi, and Psychoclonal algorithm embedded approach

Abstract

In today’s market increased level of competitiveness and uneven fall of the final demands are pushing enterprises to make an effort for optimization of their process management. It involves collaboration in multiple dimensions viz. information sharing, capacity planning, and reliability among players. One of the most important dimensions of the supply chain network is to determine its optimal operating conditions incurring minimum total costs. However, this is even a tough job due to the complexities involved in the dynamic interaction among multiple facilities and locations. In order to resolve these complexities and to identify the optimal operating condition we have proposed a hybrid approach incorporating simulation, Taguchi method, robust multiple non-linear regression analysis and the Psychoclonal algorithm. The Psychoclonal algorithm is an evolutionary algorithm that inherits its traits from Maslow need hierarchy theory and the Artificial Immune System (AIS). The results obtained using the proposed hybrid approach is compared with those found out by replacing Psychoclonal algorithm with the Artificial Immune System (AIS) and Response Surface Methodology (RSM), respectively. This research makes it possible for the firms to understand the intricacies of the dynamics and interdependency among the various factors involved in the supply chain. It provides guidelines to the manufacturers for the selection of appropriate plant capacity and also proposes a justified strategy for delayed differentiation.