Title: New enhanced differential evolution algorithms for solving multi-depot vehicle routing problem with multiple pickup and delivery requests

Authors: Siwaporn Kunnapapdeelert; Voratas Kachitvichyanukul

Addresses: School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, KlongLuang, Pathumtani 12120, Thailand ' School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, KlongLuang, Pathumtani 12120, Thailand

Abstract: This paper presents four new enhanced DE-based algorithms for solving multi-depot vehicle routing problem with multiple pickup and delivery requests (GVRP-MDMPDR). Two enhanced DE algorithms are based on subgrouping idea and two are based on the idea of strategy switching. The four enhanced algorithms are: subgrouping mutation differential evolution (SGMDE), subgrouping crossover differential evolution (SGCDE), switching mutation differential evolution (SWMDE), and switching crossover differential evolution (SWCDE). All enhanced algorithms are compared to the classical DE algorithm for solving GVRP-MDMPDR by using published benchmark test problems. The results show that algorithms SGMDE, SGCDE and SWCDE outperform classical DE algorithm. Furthermore, it found that the enhancement in the crossover process is more effective for improving DE performance than enhancement in the mutation process. The combination of the solution representations SD1 and SD3 with the DE-based algorithms also found new best known solutions in 25 out of 30 test problem instances.

Keywords: differential evolution; generalise multi-depot vehicle routing problem with multiple pickup and delivery requests; subgrouping; switching.

DOI: 10.1504/IJSOM.2018.095562

International Journal of Services and Operations Management, 2018 Vol.31 No.3, pp.370 - 395

Received: 25 Jul 2016
Accepted: 09 Dec 2016
Published online: 11 Oct 2018 *

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