A multi-population genetic algorithm for transportation scheduling

被引：40

作者：

Zegordi, S. H. ^{[1
]}

Nia, M. A. Beheshti ^{[1
]}

机构：

[1] Tarbiat Modares Univ, Dept Ind Engn, Tehran 14115143, Iran

来源：

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW | 2009年 / 45卷 / 06期

关键词：

Scheduling; Supply chain management; Genetic algorithm; Transportation; Tardiness; SUPPLY CHAIN OPTIMIZATION; MINIMIZING TOTAL TARDINESS; JOB DELIVERY COORDINATION; READY-MIXED CONCRETE; PARALLEL MACHINES; SELECTION; INDUSTRY; MANAGEMENT; LOGISTICS; SYSTEM;

D O I：

10.1016/j.tre.2009.05.002

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This study considers the integration of production and transportation scheduling in a two-stage supply chain environment. The objective function minimizes the total tardiness and total deviations of assigned work loads of suppliers from their quotas. After modeling the problem as a mixed integer programming problem, a genetic algorithm with three populations, namely, a multi-society genetic algorithm (MSGA), is proposed for solving it. MSGA is compared with the optimum solutions for small problems and a heuristic and a random search approach for larger problems. Additionally, an MSGA is compared with a generic genetic algorithm. The experimental results show the superiority of the MSGA. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：946 / 959

页数：14

共 53 条

[41] Planning and scheduling in a multi-site environment [J].