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Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty.

Yuhua Zhu1, Xiang Fan1, Chuanzhong Yin1

  • 1College of Transport and Communications, Shanghai Maritime University, Shanghai, China.

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|January 29, 2024
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Summary
This summary is machine-generated.

This study introduces a new model for selecting low-carbon logistics center sites, addressing demand uncertainty to reduce costs and carbon emissions. The approach optimizes location planning for a more sustainable and efficient logistics industry.

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Area of Science:

  • Operations Research
  • Environmental Science
  • Supply Chain Management

Background:

  • China's "double carbon" goal necessitates advancements in low-carbon logistics.
  • Current logistics center site selection models often overlook demand uncertainty, leading to resource inefficiency.
  • Optimizing logistics center location is crucial for reducing carbon emissions and operational costs.

Purpose of the Study:

  • To develop a multi-objective site selection model for low-carbon logistics centers that incorporates demand uncertainty.
  • To minimize both logistics costs and carbon emissions simultaneously.
  • To provide a robust methodology for sustainable logistics infrastructure planning.

Main Methods:

  • A multi-objective site selection model based on stochastic programming theory was formulated.
  • Demand uncertainty was treated as a stochastic constraint, transformed into a 0-1 mixed integer multi-objective planning model.
  • The Aptenodytes Forsteri Optimization (AFO) algorithm, combined with the Ideal Point Method, was employed for solving the model.

Main Results:

  • The proposed model and AFO algorithm effectively addressed the low-carbon logistics center site selection problem under demand uncertainty.
  • Compared to deterministic approaches, the model demonstrated reductions in logistics costs (average 6.2%) and carbon emissions (average 3.6%).
  • Under demand uncertainty (0.9 confidence level), both logistics costs and carbon emissions were significantly decreased.

Conclusions:

  • The study offers a novel approach to low-carbon logistics location problems, specifically accounting for demand uncertainty.
  • The findings support the transformation of the logistics industry towards greater low-carbon efficiency.
  • The integrated optimization model and AFO algorithm provide a valuable tool for sustainable logistics planning.