Chance-constrained multi-objective approach for hazardous materials routing and scheduling under demand and service time uncertainty

The transportation of hazardous materials (hazmat) is a challenging problem that often requires a trade-off between conflicting objectives. In practice, the complexity of the problem is exacerbated due to the lack of sufficient and reliable historical data. In this research, a stochastic multi-objective optimization model for hazardous materials (hazmat) vehicle routing and scheduling problem is developed. The goal is to find optimal links and routes to obtain a trade-off between the safe and fast distribution of hazmat through a transport network under customers' demand and service time uncertainty. We utilized a hybrid game theory based compromise programming to develop a solution algorithm to determine the Pareto-optimal solutions, which are based on the total travel distance and total risk imposed on the transportation process. Computational results of a realistic numerical case study demonstrate the effectiveness of the proposed model and the solution algorithm in obtaining Pareto-optimal solutions.