Multi-objective vehicle routing problem with time windows based on improved simulated annealing algorithm

Abstract

Aiming at the vehicle routing problem with time window constraints, this paper discusses the vehicle routing optimization problem among multiple warehouses, multiple products and multiple customers. In this paper, a multi-objective model of vehicle path optimization based on the improved simulated annealing algorithm is established by adding memory function and setting monotonous heating up and other real-time optimization strategies. The optimization objectives include total transportation cost, total driving distance and total driving time. In order to prove the effectiveness of the proposed model, the optimization results of standard genetic algorithm, random search and the algorithm in this paper are compared on the randomly generated dataset. The simulation results show that the algorithm designed in this paper is fast and efficient in all four cases.

Challenges and Opportunities for Applying Meta-Heuristic Methods in Vehicle Routing Problems: A Review

“…(a) Vehicle Routing Problem with Time Windows (VRPTW): Each customer has a time window when they can receive service [9,10]; (b) Capacitated Vehicle Routing Problem (CVRP): Each vehicle has a limited capacity to transport goods [11]; (c) VRP with Pickup and Delivery (VRPPD): Vehicles not only deliver goods but also pick up goods from other customers during the journey [12,13]; (d) Time-Dependent VRP (TDVRP): Considers time as an important factor in determining the optimal route for delivery [1]; (e) Multiple Depot VRP (MDVRP): Several depots or distribution centers are used as the starting and ending points of delivery routes [14].…”

“…As a relatively easy-to-modify method, SA has also been used in several studies. For example, one study aimed to solve the problem of vehicle routing with limited time windows, which involved many warehouses, many products, and many customers [10]. SA was also used to complete a VRPTW, and the results were compared with agent-based simulations [24].…”