A High Performance Search Algorithm for Job-Shop Scheduling Problem

被引：6

作者：

Wang, Shao-Juan ^{[1
]}

Tsai, Chun-Wei ^{[2
]}

Chiang, Ming-Chao ^{[1
]}

机构：

[1] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan

[2] Natl Chung Hsing Univ, Dept Comp Sci & Engn, Taichung 40227, Taiwan

来源：

9TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS (EUSPN-2018) / 8TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE (ICTH-2018) | 2018年 / 141卷

关键词：

Metaheuristic Algorithm; Job-Shop Scheduling Problem; Search Economics; GENETIC ALGORITHM;

D O I：

10.1016/j.procs.2018.10.157

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

A novel metaheuristic algorithm, called search economics for job-shop scheduling problem (SEJSP), is presented to solve the job-shop scheduling problem (JSP) in this paper. The most distinguishing feature of the proposed algorithm is that it attempts to guide the search process to the regions that are promising, based on the expected value that is influenced by the performance of samples and candidate solutions and the developed rate of regions. Since SEJSP inherits the characteristics of search economics, it can also avoid the search from falling into local optima at early iterations. To evaluate the performance and effectiveness of the proposed algorithm and, this study compares SEJSP with other heuristics. The experimental results demonstrate that the proposed algorithm outperforms all the other heuristic algorithms compared in this study. (C) 2018 The Authors. Published by Elsevier Ltd.

引用

页码：119 / 126

页数：8

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