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.
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收藏
页码：946 / 959
页数：14
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