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<p>This study presents a novel framework for addressing the Capacitated Vehicle Routing Problem (CVRP) in supply chain management, emphasizing a multidimensional optimization approach that sets it apart from previous research. The proposed method features an improved Moth Flame Optimization algorithm, integrated with Opposition-Based Learning and Tournament Selection, to address limitations in solution diversity and quality. Its performance is benchmarked against state-of-the-art algorithms using 23 benchmark test functions. The model integrates three key objectives: delivery time, transportation cost, and CO₂ emissions, providing a balanced trade-off evaluation that surpasses traditional single- or dual-objective models. Its practical applicability is validated through real-world logistics scenarios, showcasing its potential to enhance operational efficiency and reduce environmental impact. This comprehensive solution bridges the gap between theoretical advancements and practical logistics challenges, offering a sustainable and flexible approach to supply chain optimization.</p>

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Enhanced IMFOM framework for multi-objective CVRP: a time–cost-CO2 emission trade-off

  • 期刊论文

  • 发表日期：

    2026年1月7日

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  • Vu Hong Son Pham,
  • Van Nam Nguyen,
  • Nghiep Trinh Nguyen Dang

摘要

This study presents a novel framework for addressing the Capacitated Vehicle Routing Problem (CVRP) in supply chain management, emphasizing a multidimensional optimization approach that sets it apart from previous research. The proposed method features an improved Moth Flame Optimization algorithm, integrated with Opposition-Based Learning and Tournament Selection, to address limitations in solution diversity and quality. Its performance is benchmarked against state-of-the-art algorithms using 23 benchmark test functions. The model integrates three key objectives: delivery time, transportation cost, and CO₂ emissions, providing a balanced trade-off evaluation that surpasses traditional single- or dual-objective models. Its practical applicability is validated through real-world logistics scenarios, showcasing its potential to enhance operational efficiency and reduce environmental impact. This comprehensive solution bridges the gap between theoretical advancements and practical logistics challenges, offering a sustainable and flexible approach to supply chain optimization.