An Evolutionary Approach to Optimizing Production Planning of Flexible Manufacturing Systems

• Main Authors: Ching-Yu Chen, 陳敬瑜
• Other Authors: SHINN-YING HO
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/84998547275644337589

碩士 === 逢甲大學 === 資訊工程所 === 92 === Process plans in a flexible manufacturing system (FMS) differs from that in a conventional job shop because each operation of a job may be performed by any one of several machines. The routing flexibility is a feature that distinguishes FMS process plans from a classic general job shop problem. In the general job shop process plans problem , the researchers only concerns about the job sequence on each machine ; however, routing and sequencing need to be decided simultaneously in the FMS environment. This thesis proposes an efficient method to design optimal Flexible manufacturing systems using an Multi-objective Genetic Algorithm (MOGA). The FMS can deal with a large number of multimedia files and has four objectives: to minimize the total flow time , to minimize of the deviations of machine workload, to minimization of greatest machine workload and to minimization of tool costs. We use the MOGA which can efficiently solve large multi-objective parameter optimization problems to obtain a high-quality set of non-dominated solutions. The non-dominated solutions can provide the decision maker valuable information to design optimal FMS according to preference and suggestions to extend the existing devices. The experimental results show that MOGA performs better than an existing multi-objective evolutionary algorithm SPEA for solving the optimization problem and the four objectives of the optimal FMS can be achieved.