A general variable neighborhood search for solving the multi-objective open vehicle routing problem
- 202 Downloads
The multi-objective open vehicle routing problem (MO-OVRP) is a variant of the classic vehicle routing problem in which routes are not required to return to the depot after completing their service and where more than one objective is optimized. This work is intended to solve a more realistic and general version of the problem by considering three different objective functions. MO-OVRP seeks solutions that minimize the total number of routes, the total travel cost, and the longest route. For this purpose, we present a general variable neighborhood search algorithm to approximate the efficient set. The performance of the proposal is supported by an extensive computational experimentation which includes the comparison with the well-known multi-objective genetic algorithm NSGA-II.
KeywordsGeneral variable neighborhood search NSGA-II Open vehicle routing problem Sweep algorithm Local search Multi-objective optimization
J. M. Colmenar and J. Sánchez-Oro are supported by the Spanish Ministry of “Economía y Competitividad”, Grant Refs. TIN2015-65460-C2-2-P and TIN2014-54806-R. A.D. López-Sánchez acknowledge support from the Spanish Ministry of Science and Innovation through Projects ECO2013-47129-C4-1-R and ECO2016-76567-C4-1-R.
- Eiben, A.E., Smith, J.E.: Introduction to evolutionary computing (2003)Google Scholar
- Toth, P., Vigo, D. (eds.): The Vehicle Routing Problem. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (2002)Google Scholar
- We, Z., Tingxin, S., Fei, H., Xi, L.: Multiobjective Vehicle Routing Problem with Route Balance Based on Genetic Algorithm. Discrete Dynamics in Nature and Society 2013(Article ID 325686), 9 (2013)Google Scholar
- Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. PhD Thesis, Swiss Federal Institute of Technology Zurich (1999)Google Scholar
- Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms—A comparative case study, pp. 292–301. Springer, Berlin, Heidelberg (1998)Google Scholar