Locomotive optimization using artificial intelligence approach

被引：0

作者：

Ziarati, K ^{[1
]}

Chizari, H ^{[1
]}

Nezhad, AM ^{[1
]}

机构：

[1] Shiraz Univ, Sch Engn, Shiraz, Iran

来源：

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY | 2005年 / 29卷 / B1期

关键词：

railway; network flows; transportation; genetic algorithms; locomotive assignment; artificial intelligence;

D O I：

暂无

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The problem of assigning locomotives to trains consists of determining the number of locomotives of different types that provide sufficient power to pull trains on fixed schedules. The objective is to minimize the fixed and operational locomotive costs. The locomotive assignment problem is defined for cyclic and non cyclic problems. In this paper, an approach using a genetic algorithm and a neural network algorithm is presented to find a cyclic solution on a one-week horizon, while satisfying the power demands of all trains. This system was tested using realistic data from the Canadian National (C.N.) North America Company with about 1600 trains and 1300 locomotives.

引用

页码：93 / 105

页数：13

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