A genetic-based approach for vehicle routing problem with fuzzy alpha-cut constraints

Abstract In today’s business environment, logistics and supply chain management are especially important for the timely delivery of materials and goods. Delivery must not only be fast, it also needs to be performed within a specific time frame. Therefore, this study examines a vehicle routing problem (VRP) that considers multiple goals and allows vehicles to reach their destinations within a time window with a crashed traveling time. Two objectives are considered, the minimization of total cost and the maximization of customer satisfaction. Firstly, a fuzzy multi-objective linear programming (FMOLP) model with fuzzy alpha-cut constraints is proposed for multi-objective optimization, and then an improved genetic algorithm (IGA) is constructed to solve large-scale problems. The performances of the proposed methods are evaluated through several case studies. Design of experiments and sensitivity analysis are applied to evaluate the performance and robustness of IGA. The results show that both the FMOLP and IGA are effective, and the IGA can efficiently obtain a near-optimal solution.