A slope scaling/Lagrangean perturbation heuristic with long-term memory for multicommodity capacitated fixed-charge network design

被引:60
作者
Crainic, TG
Gendron, B
Hernu, G
机构
[1] Univ Quebec, Dept Management & Technol, Ecole Sci Gest, Montreal, PQ H3C 3P8, Canada
[2] Univ Montreal, Ctr Rech Transports, Montreal, PQ H3C 3J7, Canada
[3] Univ Montreal, Dept Informat & Rech Operat, Montreal, PQ H3C 3J7, Canada
基金
加拿大自然科学与工程研究理事会; 加拿大创新基金会;
关键词
slope scaling; Lagrangean heuristic; long-term memory; multicommodity capacitated fixed-charge network design;
D O I
10.1023/B:HEUR.0000045323.83583.bd
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper describes a slope scaling heuristic for solving the multicomodity capacitated fixed-charge network design problem. The heuristic integrates a Lagrangean perturbation scheme and intensification/diversification mechanisms based on a long-term memory. Although the impact of the Lagrangean perturbation mechanism on the performance of the method is minor, the intensification/diversification components of the algorithm are essential for the approach to achieve good performance. The computational results on a large set of randomly generated instances from the literature show that the proposed method is competitive with the best known heuristic approaches for the problem. Moreover, it generally provides better solutions on larger, more difficult, instances.
引用
收藏
页码:525 / 545
页数:21
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