Multiobjective Dynamic Vehicle Routing Problem With Fuzzy Travel Times and Customers’ Satisfaction in Supply Chain Management

This paper presents a multiobjective dynamic vehicle routing problem with fuzzy travel times and customers´ satisfaction level. In this model, all the required data are not known in advance and a set of dynamic requests (real time requests) arrives over time. The dispatcher does not have any information of these requests until they arrive. Moreover, the travel times which in reality and in urban areas fluctuate due to a variety of factors, such as accident, traffic conditions, and weather conditions, are modeled as fuzzy travel times. In addition, the customers´ satisfaction level is involved in the routing of vehicles by using the concept of fuzzy time windows. This paper uses a direct interpretation of the proposed model as a multiobjective problem where the total required fleet size, overall total traveling distance, and waiting time imposed on vehicles are minimized and the overall customers´ preferences for service are maximized. The dynamic solving strategy is proposed based on the genetic algorithm and three basic modules and its performance are evaluated in different steps on various test problems generalized from a set of static instances in the literature. The computational experiments on data sets illustrate the efficiency and effectiveness of the proposed approach.