Models and algorithm for stochastic shortest path problem

Abstract

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, α-shortest path and the most shortest path, and present three new types of models: expected value model, chance-constrained 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.