An Evolutionary Clustering-Based Optimization to Minimize Total Weighted Completion Time Variance in a Multiple Machine Manufacturing System

Abstract

This paper discusses the clustering as a new paradigm of optimization and devises an integration of clustering and an evolutionary algorithm, neighborhood search algorithm (NSA), for a multiple machine system with the case of reducible processing times. After the problem is formulated mathematically, evolutionary clustering search (ECS) is devised to reach the near-optimal solutions. It is a way of detecting interesting search areas based on clustering. In this approach, an iterative clustering carried out integrated to evolutionary mechanism NSA to identify which subspace is promising, and then the search strategy becomes more aggressive in detected areas. It is interesting to find out such subspaces as soon as possible to increase the algorithm’s efficiency by changing the search strategy over possible promising regions. Once relevant search regions are discovered by clustering they can be treated with special intensification by the NSA algorithm. Furthermore, different neighborhood mechanisms are designed to be embedded within the main NSA algorithm so as to enhance its performance. The applicability of proposed model and performance of NSA approach are demonstrated via computational experiments.