Master-Slave Genetic Algorithm for Flow Shop Scheduling with Resource Flexibility
This paper aims to investigate the improvements in manufacturing efficiency. This can be realized by broadening the scope of the production scheduling which includes both the sequencing jobs and processing-time control through the deployment of the flexible resource. This study assumes an environment in which a set of jobs must be scheduled in a flow shop, where each manufacturing cell consists of a single machine. There the processing time of each operation depends on the amount of resource allocated to the machine. This study is expected to solve the static version of flow-shop flexible-resource scheduling (SFSFR) problem with genetic algorithm to minimize the weight sum of earliness and tardiness. We suggest a master-slave genetic algorithm (MSGA) that can solve the resource allocation and job sequencing together in order to avoid the defect of two-stage method, and the heuristic algorithm of shifting job completed before due date by insertion of idle time is embedded into genetic algorithm to optimize the solutions. At last, the adaptive genetic operator is applied to increase convergence rate and improve search capability. Experimental results show that the proposed master-slave genetic algorithm performed better than other related algorithms.
static flow-shop flexible-resource scheduling master-slave genetic algorithm earliness and tardiness adaptive genetic operator
JinFenghe FuYaping
Northeast Dianli University Economic and Management Institute Jilin, China
国际会议
成都
英文
341-346
2010-07-09（万方平台首次上网日期，不代表论文的发表时间）