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

• Published in: Journal of multi-criteria decision analysis 2020-09, Vol.27 (5-6), p.318-336
• Main Authors: Moghaddam, Kamran S., Azadian, Farshid
• Format: Article
• Language: English
• Subjects: chance‐constrained programming; demand uncertainty; hazardous material; Hazardous materials; multi‐objective optimization; service time uncertainty; vehicle routing and scheduling problem
• ISSN: 1057-9214; 1099-1360
• DOI: 10.1002/mcda.1718

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.