Finding multi-objective paths in stochastic networks: a simulation-based genetic algorithm approach

Path finding is a fundamental research topic in transportation due to its wide applications in transportation planning and intelligent transportation system (ITS). In transportation, the path finding problem is usually defined as the shortest path (SP) problem in terms of distance, time, cost, or a combination of criteria under a deterministic environment. However, in real life situations, the environment is often uncertain. In this paper, we develop a simulation-based genetic algorithm to find multi-objective paths in stochastic networks. Numerical experiments are presented to demonstrate the algorithm feasibility.