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Summary

This study introduces a hybrid Genetic Algorithm (GA) and Long Short-Term Memory (LSTM) network to optimize supply chain sustainability. The approach significantly reduces emissions and enhances operational efficiency while ensuring regulatory compliance.

Keywords:
Carbon disclosure projectEmissions optimizationGenetic algorithmLong short-term memoryResource efficiencySustainability

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Area of Science:

  • Supply Chain Management
  • Environmental Science
  • Artificial Intelligence

Background:

  • Increasing pressure on businesses to adopt sustainable practices and reduce carbon footprints.
  • Need for efficient operational strategies within supply chains to balance environmental and economic goals.

Purpose of the Study:

  • To develop a novel hybrid approach combining Genetic Algorithms (GA) and Long Short-Term Memory (LSTM) networks for optimizing supply chain sustainability.
  • To create a cost-effective, scalable solution for reducing emissions, improving efficiency, and ensuring regulatory compliance.

Main Methods:

  • Utilized publicly available Carbon Disclosure Project (CDP)-reported data for emissions prediction and resource allocation.
  • Employed LSTM networks for forecasting emission trends based on historical data.
  • Applied GA for optimizing supply chain processes, including transportation and energy consumption, through multi-objective optimization.

Main Results:

  • Achieved a 23.67% reduction in total supply chain emissions, with notable improvements in indirect emissions.
  • Enhanced operational efficiency by 10.98%.
  • Ensured 100% compliance with environmental regulations, eliminating penalties.

Conclusions:

  • The hybrid GA-LSTM framework effectively optimizes supply chain sustainability, offering a practical, data-driven solution.
  • The approach provides valuable insights for businesses aiming to meet sustainability targets and improve performance.
  • The system is scalable for both large corporations and SMEs, promoting widespread adoption of sustainable practices.