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An optimization model for fleet sizing and empty pallet allocation considering CO2 emissions.

Jianwei Ren1,2,3, Chunhua Chen4,5, Jian Gao1

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This study proposes an optimization model for fleet sizing and empty pallet allocation to maximize profits and minimize carbon dioxide (CO2) emissions for pallet rental companies. The model helps managers make efficient vehicle configuration decisions.

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Area of Science:

  • Operations Research
  • Environmental Management
  • Logistics and Supply Chain Management

Background:

  • Pallet rental services are increasingly common, requiring efficient logistics for transporting and returning goods.
  • Effective vehicle fleet configuration is crucial for optimizing empty pallet allocation, impacting efficiency and costs.
  • Minimizing carbon dioxide (CO2) emissions is a growing concern in logistics operations.

Purpose of the Study:

  • To develop an optimization model for fleet sizing and empty pallet allocation in pallet rental services.
  • To simultaneously maximize company profits and minimize vehicle carbon dioxide (CO2) emissions.
  • To provide a scientifically sound method for managers to configure vehicle fleets.

Main Methods:

  • Utilizing mixed-integer programming and stochastic programming for model development.
  • Employing a particle swarm optimization algorithm with inertia weight (IPSO) to solve the optimization model.
  • Conducting numerical examples to validate the proposed model and algorithm.

Main Results:

  • The proposed model effectively balances fleet sizing and empty pallet allocation decisions.
  • Carbon dioxide (CO2) emissions significantly influence fleet sizing and empty pallet allocation strategies.
  • Economic factors (price, rental fees, idle costs) of low-emission vehicles can deter their adoption.

Conclusions:

  • The optimization model provides a robust framework for enhancing operational efficiency and environmental sustainability in pallet rental services.
  • Fleet configuration decisions must consider both economic objectives and environmental impact.
  • Further research may explore the integration of more sophisticated environmental metrics and real-time operational data.