Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem

被引：64

作者：

Shahzad, Atif ^{[1
]}

Mebarki, Nasser ^{[1
]}

机构：

[1] Inst Rech Commun & Cybernet Nantes, F-44321 Nantes, France

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2012年 / 25卷 / 06期

关键词：

Scheduling; Simulation; Job-shop; Data mining; Tabu search; GENETIC ALGORITHM; MACHINE; RULES; INDUCTION;

D O I：

10.1016/j.engappai.2012.04.001

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A data mining based approach to discover previously unknown priority dispatching rules for job shop scheduling problem is presented. This approach is based on seeking the knowledge that is assumed to be embedded in the efficient solutions provided by the optimization module built using tabu search. The objective is to discover the scheduling concepts using data mining and hence to obtain a set of rules capable of approximating the efficient solutions for a job shop scheduling problem (JSSP). A data mining based scheduling framework is presented and implemented for a job shop problem with maximum lateness as the scheduling objective. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：1173 / 1181

页数：9