Nature of real-world multi-objective vehicle routing with evolutionary algorithms

Castro-Gutierrez, Juan; Landa-Silva, Dario; Moreno Perez, Jose

Abstract

The Vehicle Routing Problem with Time Windows VRPTW) is an important logistics problem which in the realworld appears to be multi-objective. Most research in this area has been carried out using classic datasets designed for the single-objective case, like the well-known Solomon's problem instances. Some unrealistic assumptions are usually made when using these datasets in the multi-objective case (e.g. assuming that one unit of travel time corresponds to one unit of travel distance). Additionally, there is no common VRPTW multiobjective oriented framework to compare the performance of algorithms because different implementations in the literature tackle different sets of objectives. In this work, we investigate the conflicting (or not) nature of various objectives in the VRPTW and show that some of the classic test instances are not suitable for conducting a proper multi-objective study. The insights of this study have led us to generate some problem instances using d ata from a real-world distribution company. Experiments in these new dataset using a standard evolutionary algorithm NSGA-II) show stronger evidence of multi-objective features. Our contribution focuses on achieving a better understanding about the multi-objective nature of the VRPTW, in particular the conflicting relationships between 5 objectives: number of vehicles, total travel distance, makespan, total waiting time, and total delay time.

Citation

Castro-Gutierrez, J., Landa-Silva, D., & Moreno Perez, J. (2011). Nature of real-world multi-objective vehicle routing with evolutionary algorithms.

• Conference Name: Proceedings of the 2011 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011)
• End Date: Oct 12, 2011
• Publication Date: Oct 1, 2011
• Deposit Date: Apr 4, 2016
• Publicly Available Date: Apr 4, 2016
• Peer Reviewed: Peer Reviewed
• Keywords: vehicle routing, multiobjective optimization, evolutionary algorithms, problem formulation, test problems
• Public URL: https://nottingham-repository.worktribe.com/output/1009615
• Publisher URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6083675
• Additional Information: Copyright IEEE 2011.; doi: 10.1109/ICSMC.2011.6083675.