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Single depot multiple traveling salesman problem (MTSP) is widely studied in the literature. However, multi-depot multiple traveling salesman problem (MD-MTSP) has not gained much attention. Only a few approaches exist in the literature for MD-MTSP. In this paper, we have proposed two hybrid evolutionary approaches for MD-MTSP. Our first approach is based on grouping genetic algorithm (GGA), whereas the other approach utilizes discrete differential evolution (DDE). Chromosome encoding and variation operators in these two approaches are designed considering the characteristics of MD-MTSP. The solutions obtained through variation operators in these approaches are improved further through a local search. We have compared the performance of our approaches with the best approach available in the literature on standard benchmark instances. In addition, we have reported the performance of our approaches on some large instances also. Computational results show the effectiveness of our two approaches in solving MD-MTSP as these two consistently outperform the existing best approach.

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Hybrid Evolutionary Approaches with Tailor-Made Variation Operators for Multi-depot Multiple Traveling Salesman Problem

  • 会议论文

  • 发表日期：

    2026年

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  • Sanjeev Chauhan,
  • Alok Singh,
  • Rammohan Mallipeddi

摘要

Single depot multiple traveling salesman problem (MTSP) is widely studied in the literature. However, multi-depot multiple traveling salesman problem (MD-MTSP) has not gained much attention. Only a few approaches exist in the literature for MD-MTSP. In this paper, we have proposed two hybrid evolutionary approaches for MD-MTSP. Our first approach is based on grouping genetic algorithm (GGA), whereas the other approach utilizes discrete differential evolution (DDE). Chromosome encoding and variation operators in these two approaches are designed considering the characteristics of MD-MTSP. The solutions obtained through variation operators in these approaches are improved further through a local search. We have compared the performance of our approaches with the best approach available in the literature on standard benchmark instances. In addition, we have reported the performance of our approaches on some large instances also. Computational results show the effectiveness of our two approaches in solving MD-MTSP as these two consistently outperform the existing best approach.