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The two-region multi-depot pickup and delivery problem.

Adria Soriano1, Margaretha Gansterer1, Richard F Hartl1

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This study introduces a new logistics problem for efficient planning in multi-depot networks. An adaptive large neighborhood search algorithm effectively solves complex routing and delivery decisions.

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Area of Science:

  • Operations Research
  • Logistics Management
  • Transportation Science

Background:

  • Logistics networks are increasingly complex, requiring efficient planning for cost savings.
  • The two-region multi-depot pickup and delivery problem addresses challenges in geographically diverse distribution networks.
  • Inter-regional transportation necessitates specialized strategies due to inefficiencies in direct routing.

Purpose of the Study:

  • To define and address the two-region multi-depot pickup and delivery problem.
  • To develop an efficient algorithm for solving complex logistics decisions.
  • To improve cost-efficiency in large distribution networks through optimized planning.

Main Methods:

  • Problem decomposition into three interconnected subproblems.
  • Development of an adaptive large neighborhood search (ALNS) algorithm.
  • Tailoring ALNS operators to specific subproblem decisions for enhanced efficiency.

Main Results:

  • The proposed ALNS algorithm outperforms alternative sequential ALNS approaches.
  • Operators designed for subproblems demonstrate high efficiency on primal problems.
  • Extensive computational studies validate the effectiveness of the developed approach.

Conclusions:

  • The adaptive large neighborhood search is a powerful tool for complex distribution network problems.
  • The proposed method offers significant improvements over sequential problem-solving strategies.
  • Efficient planning in evolving logistics networks is achievable through advanced algorithmic approaches.