A hybrid heuristic to solve the parallel machines job-shop scheduling problem

被引：23

作者：

Rossi, Andrea ^{[1
]}

Boschi, Elena ^{[2
]}

机构：

[1] Univ Pisa, Dept Mech Nucl & Prod Engn, I-56126 Pisa, Italy

[2] Univ Pisa, Dept Oncol Transplants & Adv Technol Med, I-56124 Pisa, Italy

来源：

ADVANCES IN ENGINEERING SOFTWARE | 2009年 / 40卷 / 02期

关键词：

Hybrid systems; Ant colony optimization; Genetic algorithms; Parallel machines; Statistical analysis; ALGORITHMS;

D O I：

10.1016/j.advengsoft.2008.03.020

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper presents an advanced software system for solving the flexible manufacturing systems (FMS) scheduling in a job-shop environment with routing flexibility, where the assignment of operations to identical parallel machines has to be managed. in addition to the traditional sequencing problem. Two of the most promising heuristics from nature for a wide class of combinatorial optimization problems, genetic algorithms (CA) and ant colony optimization (ACO), share data structures and co-evolve in parallel in order to improve the performance of the constituent algorithms. A modular approach is also adopted in order to obtain an easy scalable parallel evolutionary-ant colony framework. The performance of the proposed framework on properly designed benchmark problems is compared with effective CA and ACO approaches taken as algorithm components. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：118 / 127

页数：10

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