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Hybrid Tabu-Grey wolf optimizer algorithm for enhancing fresh cold-chain logistics distribution.

Hao Zhang1, Jianing Yan1, Liling Wang1

  • 1School of Business, Beijing Technology and Business University, Beijing, China.

Plos One
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

A new hybrid Tabu-Grey wolf optimizer (TGWO) algorithm optimizes fresh product cold chain logistics, reducing costs and improving efficiency. This approach enhances vehicle scheduling and product quality in complex supply chains.

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Artificial Intelligence

Background:

  • Growing demand for fresh products necessitates efficient cold chain logistics.
  • Current systems face challenges like temperature control and delivery delays, causing product loss and increased costs.
  • Optimization is crucial for maintaining product quality and reducing operational expenses in fresh product distribution.

Purpose of the Study:

  • To introduce a novel hybrid Tabu-Grey wolf optimizer (TGWO) algorithm for optimizing cold chain logistics distribution of fresh products.
  • To minimize total distribution costs and establish efficient vehicle scheduling schemes under various constraints.
  • To enhance the efficiency and solution quality of cold chain logistics optimization.

Main Methods:

  • Development of a hybrid Tabu-Grey wolf optimizer (TGWO) algorithm, combining Tabu Search (TS) for exploration and Grey Wolf Optimizer (GWO) for exploitation.
  • Implementation of TGWO to optimize vehicle scheduling and minimize distribution costs in cold chain logistics.
  • Experimental validation and comparative analysis against Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and original GWO.

Main Results:

  • The TGWO algorithm demonstrated superior efficiency and solution quality compared to PSO, WOA, and GWO in optimizing cold chain logistics.
  • Experiments and case studies validated the effectiveness of the proposed hybrid approach.
  • TGWO successfully minimized distribution costs and improved vehicle scheduling schemes.

Conclusions:

  • Hybrid optimization techniques, like TGWO, are effective in complex supply chain networks and dynamic markets.
  • Implementing TGWO can significantly improve distribution efficiency, reduce costs, and maintain product quality in cold chain logistics.
  • Future research should explore new search operators and diverse real-life scenarios to enhance TGWO's applicability.