The receding horizon control (RHC) combining with the various intelligent algorithms is a common method for the dynamic vehicle routing problem (DVRP). However, the traditional RHC only considers the objects within each time window while making route plan, and can’t make adjustment according to the situations of the objects near the window. In order to deal with this problem, a fuzzy receding horizon control strategy (FRHC) is proposed. By combining the RHC and the membership function theory, the relationship between objects and time window is redefined. And the travel routes are planned by the genetic algorithm (GA) for each fuzzy time window. Finally, ten instances are selected from the DVRP standard test library to verify the proposed strategy. The experimental results show that when comparing with the RHC strategy, the FRHC can reduce the distance, the waiting time of all customers and the number of waiting customers dramatically. The FRHC combines with the GA (FRHC-GA) method is also reasonable and effective.

Introduction

The Vehicle Routing Problem (VRP) is a classical NP-hard problem in the field of operations research, is always a hot topic [1]–[۶]. It arms to design an optimal route for a number of vehicles in serving a set of customers. The vehicles serve each customer in an orderly manner to get the plan with the shortest distance or the shortest waiting time under some constraints. The VRP is mainly divided into two categories according to its characteristics: the Static VRP (SVRP) and the Dynamic VRP (DVRP). The main feature of the SVRP is that all the information of the environment such as the customer demands and travel costs is known and unchanged. However, this assumption is rarely true in real life, where the environment is often changing over time, e.g. a new customer request arrives while the vehicles are on their routes. In such a dynamic environment, the theories and the solution methods of the SVRP are no longer applicable. The DVRP is first proposed by Psaraftis [7], [8]. The main difference between the DVRP and the SVRP is that the information of customers (e.g. demand, address, service time, etc.) may change with time. To solve DVRP, many scholars have proposed various optimization algorithms [9]–[۲۵]. These approaches can be roughly divided into three categories. (1) The original travel route is generated at the beginning of the system. The system will modify the original travel route when the dynamic information generates [10].