Models and algorithm for stochastic shortest path problem

被引：43
|
作者：
Ji, XY [1 ]
机构：
[1] Tsinghua Univ, Dept Math Sci, Beijing 100084, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2005年 / 170卷 / 01期
基金：
高等学校博士学科点专项科研基金; 中国国家自然科学基金;
关键词：
shortest path problem; genetic algorithm; hybrid intelligent algorithms; stochastic simulation;
D O I：
10.1016/j.amc.2004.12.015
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we study the shortest path problem with stochastic arc length. According to different decision criteria, we originally propose the concepts of expected shortest path, a-shortest path and the most shortest path, and present three new types of models: expected value model, chance-con strained programming and dependent-chance programming. In order to solve these models, a hybrid intelligent algorithm integrating stochastic simulation and genetic algorithm is developed and some numerical examples are given to illustrate its effectiveness. (c) 2005 Elsevier Inc. All rights reserved.
引用
收藏
页码：503 / 514
页数：12
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