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Collaborative multicenter logistics delivery network optimization with resource sharing.

Shejun Deng1, Yingying Yuan2, Yong Wang3

  • 1College of Civil Science and Engineering, Yangzhou University, Yangzhou, China.

Plos One
|November 23, 2020
PubMed
Summary
This summary is machine-generated.

Collaborative resource sharing in multicenter logistics networks optimizes resource use. This approach reduces operational costs and vehicle numbers, enhancing overall logistics efficiency.

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Logistics Optimization

Background:

  • Logistics facility collaboration enhances resource utilization through sharing.
  • Multicenter logistics delivery networks (CMCLDN) benefit from coordinated resource allocation.
  • Existing models often lack efficient mechanisms for collaborative resource sharing.

Purpose of the Study:

  • To propose and test resource sharing schemes for the collaborative multicenter logistics delivery network based on resource sharing (CMCLDN-RS) problem.
  • To develop a bi-objective optimization model minimizing total cost and vehicle numbers.
  • To ensure fair allocation of cost savings among collaborative participants.

Main Methods:

  • Developed a bi-objective optimization model for CMCLDN-RS considering resource sharing schemes and service periods.
  • Devised an adaptive grid particle swarm optimization (AGPSO) algorithm with customer clustering for Pareto optimal solutions.
  • Implemented an elite iteration and selective endowment mechanism within AGPSO for enhanced search capabilities.

Main Results:

  • The AGPSO algorithm demonstrated superior computational efficiency compared to NSGA-II and MOEA.
  • The proposed resource sharing mechanism effectively reduced total operational costs and the number of vehicles.
  • A profit allocation model was established to ensure equitable distribution of cost savings.

Conclusions:

  • Collaborative resource sharing is a viable strategy for improving logistics network efficiency.
  • The AGPSO algorithm provides an effective and efficient solution for CMCLDN-RS.
  • Empirical results confirm significant operational cost and vehicle reductions in a real-world case study.