An improved Genetic Algorithm with Local Search for solving the DJS']JSSP with new dynamic events

被引：0

作者：

Ben Ali, Kaouther ^{[1
]}

Telmoudi, Achraf Jabeur ^{[2
]}

Gattoufi, Said ^{[1
]}

机构：

[1] Inst Super Gest Tunis, Tunis, Tunisia

[2] Univ Sousse, Sousse, Tunisia

来源：

2018 IEEE 23RD INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA) | 2018年

关键词：

Genetic algorithm; Local search; Dynamic job shop scheduling problem; Makespan; Event-driven rescheduling; SHOP SCHEDULING PROBLEM;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper addresses an improved Genetic Algorithm (GA) combined with local search technique to solve the dynamic job shop scheduling problem (DJSSP) with new job arrivals and change in processing time. The objective function is the minimization of the makespan known to be one of the performance criterion used to optimize manufacturing system requirements. To enhance the scheduling process, a rescheduling strategy is used to solve dynamic disturbances. Various problems including the number of jobs, the number of machines and the number of new job arrivals are compared with a collection of state of the art Dispatching Rules(DRs) and other metrics. Obtained results are satisfactory for rescheduling of new job arrivals, change in processing time and makespan minimization.

引用

页码：1137 / 1144

页数：8

共 31 条

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