Optimal path in dynamic and stochastic networks

In transportation, the path finding problem is usually defined as the shortest path (SP) problem in deterministic terms (distance, deterministic cost, etc.). However, in real life situation, many information are uncertainty (travel time, travel cost or combination of criteria). In this paper we present a formulation for the multi-objective paths problem in dynamic and stochastic networks. In order to solve this problem a procedure integrating stochastic simulation and genetic algorithm proposed by Chen in [15] is applied and some numerical examples are given to validate our idea.