作业车间调度
流水车间调度
水准点(测量)
计算机科学
数学优化
工作车间
动态优先级调度
单调速率调度
调度(生产过程)
数学
大地测量学
地铁列车时刻表
操作系统
地理
作者
Nilsen Kundakcı,Osman Kulak
标识
DOI:10.1016/j.cie.2016.03.011
摘要
Job shop scheduling has been the focus of a substantial amount of research over the last decade and most of these approaches are formulated and designed to address the static job shop scheduling problem. Dynamic events such as random job arrivals, machine breakdowns and changes in processing time, which are inevitable occurrences in production environment, are ignored in static job shop scheduling problem. As dynamic job shop scheduling problem is known NP-hard combinatorial optimization, this paper introduces efficient hybrid Genetic Algorithm (GA) methodologies for minimizing makespan in this kind of problem. Various benchmark problems including the number of jobs, the number of machines, and different dynamic events are generated and detailed numerical experiments are carried out to evaluate the performance of proposed methodologies. The numerical results indicate that the proposed methods produce superior solutions for well-known benchmark problems compared to those reported in the literature.
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