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An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem.

Longlong Leng1, Yanwei Zhao2, Jingling Zhang3

  • 1Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, China. cyxlll@zjut.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a multiobjective regional low-carbon location-routing problem (MORLCLRP) to minimize costs and emissions. A novel multiobjective hyper-heuristic approach (MOHH) effectively finds Pareto-optimal solutions for complex logistics challenges.

Keywords:
carbon emissionfuel consumptionmultiobjective hyperheuristicsmultiobjective optimizationregional low-carbon location-routing problem

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Area of Science:

  • Operations Research
  • Logistics Management
  • Environmental Sustainability

Background:

  • The location-routing problem (LRP) is a complex combinatorial optimization challenge.
  • Minimizing environmental impact and operational costs in logistics is increasingly critical.
  • Existing LRP models often do not sufficiently address multiobjective, low-carbon, and real-world constraints.

Purpose of the Study:

  • To address the multiobjective regional low-carbon LRP (MORLCLRP), balancing service duration, client waiting time, and total costs including carbon emissions.
  • To develop and evaluate a novel multiobjective hyper-heuristic approach (MOHH) for solving the MORLCLRP.
  • To analyze the impact of various domain-specific parameters on the performance of the proposed solution.

Main Methods:

  • Formulation of a multiobjective mixed integer programming model for the MORLCLRP.
  • Development of a general framework, the multiobjective hyper-heuristic approach (MOHH), integrating high-level heuristics (selection strategies, acceptance criteria) and low-level heuristics (multiobjective evolutionary algorithms).
  • Extensive computational testing and comparative analysis against eight domain-tailored MOEAs.

Main Results:

  • The proposed MOHH approach generated high-quality Pareto sets for most tested instances.
  • Comparative analyses demonstrated the effectiveness of the MOHH against specialized algorithms.
  • Empirical assessments revealed the influence of fleet composition, client/depot distribution, and zone area on key performance indicators.

Conclusions:

  • The MOHH provides an effective framework for solving the complex MORLCLRP.
  • The study offers valuable management insights into optimizing low-carbon logistics operations.
  • The research highlights the importance of considering multiple objectives and practical constraints in sustainable routing problems.