Automotive Science and Engineering

Fleet routing is one of the basic solutions to meet the good demand of customers in which decisions are made based on the limitations of product supply warehouses, time limits for sending orders, variety of products and the capacity of fleet vehicles. Although valuable efforts have been made so far in modeling and solving the fleet routing problem, there is still a need for new solutions to further make the model more realistic. In most research, the goal is to reach the shortest distance to supply the desired products. Time window restrictions are also applied with the aim of reducing product delivery time. In this paper, issues such as customers' need for multiple products, limited warehouses in terms of the type and number of products that can be offered, and also the uncertainty about handling a customer's request or the possibility of canceling a customer order are considered. We used the random model method to deal with the uncertainty of customer demand. A fuzzy clustering method was also proposed for customer grouping. The final model is an integer linear optimization model that is solved with the powerful tools of Mosek and Yalmip. Based on the simulation results, it was identified to what extent possible and accidental changes in customer behavior could affect shipping costs. It was also determined based on these results that the effective parameters in product distribution, such as vehicle speed, can be effective in the face of uncertainty in customer demand.