Selecting Machines and Buffer Size in Complex Production System Using Genetic Algorithms
Recently, many production systems that have complicated structures such as parallel, reworks, feed-forward, etc. are widely used in high volume industries. This article formulates a new optimal design problem of a complex production system (CPS). The type of the machines and the buffer size are included to achieve a greater production rate of the CPS. We attempt to find the nearest optimal design of CPS that can maximize production efficiency by optimizing the following decision variables: buffer size between each pair of machines and, machine types. Machines are selected from a list of products available in the market. The buffers are characterized by their size. The machines are characterized by their failure rate, repair rate and processing time. For the performance evaluation, a decomposition approach is used as an evaluative method. The optimal design problem is formulated as a combinatorial optimization one where the decision variables are buffers and types of machines. To solve this problem, genetic algorithm (GA) is presented. In order to achieve the most efficient use of GA, a multiple vectors distribution method (MVDM) is used for the genes arrangement in individuals. Numerical examples show that after a number of operations based on the GA, the nearest optimal design of CPS can be found.
Complex Production System Buffers Genetic Algorithm and Throughput Evaluation
Abu Qudeiri JABER Rizauddin RAMLI YAMAMOTO Hidehiko
Gifu University, Yanagido, Gifu Shi, Japan
国际会议
The Fifth InternationalSymposium on Management of Technology(ISMOT07)(第五届技术与创新管理国际研讨会)
杭州
英文
1143-1147
2007-06-01（万方平台首次上网日期，不代表论文的发表时间）