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Entropy as an Objective Function of Optimization Multimodal Transportations.

Oleg Bazaluk1, Sergiy Kotenko2, Vitalii Nitsenko3,4

  • 1Belt and Road Initiative Institute for Chinese-European Studies, Guangdong University of Petrochemical Technology, Maoming 525000, China.

Entropy (Basel, Switzerland)
|August 27, 2021
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Summary
This summary is machine-generated.

This study introduces an entropy method for optimizing multimodal transport routes in real-time, considering various risks. It enables dynamic route adjustments and predicts transport node load redistribution for efficient logistics.

Keywords:
determination and stochastic parametersentropy methodfuzzymathematical modelmultimodal transportationstransportation risksvarious criteria optimization

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Area of Science:

  • Operations Research
  • Logistics Management
  • Risk Analysis

Background:

  • Multimodal transport systems face complex risks from deterministic, stochastic, and fuzzy sources.
  • Real-time optimization and forecasting are crucial for managing dynamic changes in transportation networks.
  • Existing models may not adequately address the multifaceted nature of risks in logistics.

Purpose of the Study:

  • To develop a mathematical model for real-time optimal route selection in multimodal transport under complex risk conditions.
  • To enable dynamic route adjustments to mitigate unacceptable risk increases.
  • To forecast the redistribution of load across transport nodes and estimate dynamic changes in turnover.

Main Methods:

  • Application of the entropy method for risk assessment and optimization.
  • Development of a mathematical model for real-time decision-making in route selection.
  • Analysis and forecasting of cargo turnover using historical data and risk factors.

Main Results:

  • The entropy method effectively handles multimodal transport optimization with combined risk types.
  • The proposed model allows for optimal, real-time route changes to manage risks.
  • Accurate prediction of transport node loading and cargo turnover dynamics is achieved.

Conclusions:

  • The entropy method provides a robust framework for optimizing multimodal transport under uncertainty.
  • The developed mathematical model enhances logistical efficiency and risk management capabilities.
  • The study demonstrates practical application through a case study of Ukrainian seaports.