A novel hybrid election campaign optimisation algorithm for multi-objective flexible job-shop scheduling problem

被引：0

作者：

Wang, Shuting ^{[1
]}

Liu, Chuanjiang ^{[1
]}

Pei, Dawei ^{[2
]}

Wang, Jinjiang ^{[2
]}

机构：

[1] School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan

[2] Qinghai No. 1 Machine Tool Co, Ltd., Qinghai Province

来源：

Wang, S. (wangst@mail.hust.edu.cn) | 2013年 / Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland卷 / 07期

关键词：

election campaign optimisation; flexible scheduling; job shop scheduling; multi-objective optimisation; tabu search;

D O I：

10.1504/IJMSI.2013.055113

中图分类号：

学科分类号：

摘要：

Flexible job-shop problem (FJSP) is an extension of the job shop problem that allows an operation to be processed by any machine from a given set along different routes. This paper presents a novel hybrid election campaign optimisation (ECO) algorithm combining with tabu search (TS) algorithm for solving the multi-objective FJSP to minimise the makespan, the total workload of all machines, and the workload of the busiest machine. ECO, as a new meta-heuristic, which integrates local search and global search scheme possesses high global search efficiency, TS, as a traditional meta-heuristic which possesses high local search ability. Through reasonably hybridising these two optimisation algorithms, an effective hybrid approach (ECO+TS), which makes full advantages of ECO and TS has been proposed for the multi-objective FJSP. The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP. Copyright © 2013 Inderscience Enterprises Ltd.

引用

页码：160 / 170

页数：10

共 10 条

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