The 2012 International Symposium on Semiconductor Manufacturing Intelligence

During the past several years, there have been a significant number of researches conducted in the area of semiconductor final test scheduling problems (SFTSP). As specific example of simultaneous multiple resources scheduling problem (SMRSP), intelligent manufacturing planning and scheduling based on meta-heuristic methods, such as Genetic Algorithm (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO), have become the common tools for finding satisfactory solutions within reasonable computational times in real settings. However, limited researches were aiming at analyze the effects of interdependent relations during group decision-making activities. Moreover for complex and large problems, local constraints and objectives from each managerial entity, and their contributions towards the global objectives cannot be effectively represented in a single model. In this paper, we propose a novel Cooperative Estimation of Distribution Algorithm (CEDA) to overcome the challenges mentioned before. The CEDA is established based on divide-and-conquer strategy and a co-evolutionary framework. Considerable experiments have been conducted and the results confirmed that CEDA outperforms recent research results for scheduling problems in FMS (Flexible Manufacturing Systems).