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

Author

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  • Siwaporn Kunnapapdeelert
  • Voratas Kachitvichyanukul

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.

Suggested Citation

  • Siwaporn Kunnapapdeelert & Voratas Kachitvichyanukul, 2018. "New enhanced differential evolution algorithms for solving multi-depot vehicle routing problem with multiple pickup and delivery requests," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 31(3), pages 370-395.
    • Handle: RePEc:ids:ijsoma:v:31:y:2018:i:3:p:370-395
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