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Shared bikes distribution vehicle routing problem with split delivery considering carbon emission.

Guanghui Chen1, Huicong Li2, Bing Su1

  • 1School of Economics and Management, Xi'an Technological University, Xi'an, China.

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Summary

Optimizing shared bike distribution involves balancing delivery costs and environmental impact. This study introduces a model and algorithm to minimize carbon emissions and delivery expenses by splitting demand and considering vehicle load limits.

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Area of Science:

  • Operations Research
  • Environmental Science
  • Transportation Logistics

Background:

  • Shared bike distribution differs from traditional logistics due to high demand at specific stations (e.g., transit hubs, business districts).
  • Meeting peak demand often requires batch distribution, splitting deliveries from distribution centers to demand stations.
  • Delivery vehicles contribute to carbon emissions, necessitating environmental considerations in route selection.

Purpose of the Study:

  • To establish a model for shared bike distribution vehicle route selection that incorporates carbon emissions.
  • To minimize the combined costs of carbon emissions and delivery operations.
  • To address demand splitting at stations and vehicle load limitations.

Main Methods:

  • Developed a mathematical model for distribution vehicle route selection, optimizing for total cost (emission + delivery).
  • Designed a Genetic Algorithm (GA) as an approximation algorithm to solve the established model.
  • Analyzed the time complexity and approximation ratio bounds of the GA.

Main Results:

  • The proposed model effectively integrates carbon emission costs into shared bike distribution route selection.
  • The GA demonstrated good performance with an approximation ratio of 3.52 in an empirical study.
  • The model and algorithm were validated using real-world shared bike station data from Xi'an, China.

Conclusions:

  • The study provides a theoretical framework for optimizing shared bike logistics with a focus on environmental sustainability.
  • Decision-makers can utilize the model and algorithm to improve the efficiency and reduce the carbon footprint of shared bike delivery operations.
  • Demand splitting and vehicle load constraints are critical factors in efficient shared bike distribution planning.