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<p>Decarbonizing transportation fleets is a multifaceted challenge that involves optimizing long-term fleet composition while carefully balancing three competing objectives: minimizing operational costs, lowering carbon emissions, and consistently fulfilling annual transportation demand. Most existing methods address these goals in isolation, limiting their practical applicability. This study proposes a hybrid evolutionary optimization framework inspired by NSGA-II, which integrates TOPSIS-based preference modeling to generate fleet plans that are cost-efficient, low-emission, and operationally viable across a multi-year planning horizon. This hybrid model leverages TOPSIS scores during both initialization and fitness evaluation phases of the evolutionary optimization (EO), enabling the generation of Pareto-optimal fleet compositions that minimize cost and emissions while satisfying year-wise demand constraints. The framework also incorporates an iterative planning mechanism that reuses viable fleet assets across years, reducing redundant acquisitions and promoting capital efficiency. Compared to the standard EO framework, the proposed method achieved a 28.37% reduction in total cost and a 39.95% reduction in emissions, with both improvements statistically significant (p &lt; 0.005). A parallelized implementation also reduced runtime by 36.2%, demonstrating scalability without compromising optimization outcomes. This framework offers a practical tool for sustainable and cost-effective fleet planning. GitHub: <a href="https://github.com/yashyaks/FleetStructureOptimization">https://github.com/yashyaks/FleetStructureOptimization</a>.</p>

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A multi-objective optimization framework for sustainable fleet structure planning using TOPSIS integrated evolutionary optimization

  • 期刊论文

  • 发表日期：

    2026年7月18日

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  • Hiya Jain,
  • Yash Thakar,
  • Shruti Jain,
  • Kanchan Dabre

摘要

Decarbonizing transportation fleets is a multifaceted challenge that involves optimizing long-term fleet composition while carefully balancing three competing objectives: minimizing operational costs, lowering carbon emissions, and consistently fulfilling annual transportation demand. Most existing methods address these goals in isolation, limiting their practical applicability. This study proposes a hybrid evolutionary optimization framework inspired by NSGA-II, which integrates TOPSIS-based preference modeling to generate fleet plans that are cost-efficient, low-emission, and operationally viable across a multi-year planning horizon. This hybrid model leverages TOPSIS scores during both initialization and fitness evaluation phases of the evolutionary optimization (EO), enabling the generation of Pareto-optimal fleet compositions that minimize cost and emissions while satisfying year-wise demand constraints. The framework also incorporates an iterative planning mechanism that reuses viable fleet assets across years, reducing redundant acquisitions and promoting capital efficiency. Compared to the standard EO framework, the proposed method achieved a 28.37% reduction in total cost and a 39.95% reduction in emissions, with both improvements statistically significant (p < 0.005). A parallelized implementation also reduced runtime by 36.2%, demonstrating scalability without compromising optimization outcomes. This framework offers a practical tool for sustainable and cost-effective fleet planning. GitHub: https://github.com/yashyaks/FleetStructureOptimization.