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<p>Resilient and sustainable supply chain transportation is critical in today’s globalized economy. This study introduces a stochastic-capacity supply chain (SCSC) model that integrates transport damage and carbon emission constraints, addressing two key challenges faced by modern logistics networks. By considering the variability in arc capacities due to vehicle breakdowns, route disruptions, and other stochastic factors, the model captures the degradation of transportation flow, both in terms of reduced capacity and the accumulation of carbon emissions. SCSC reliability is the probability that the supply chain meets demand and carbon emission limits despite uncertainties. A simulation-based algorithm, which combines Monte Carlo sampling with linear programming formulations, is proposed to efficiently estimate this reliability measure without resorting to exhaustive enumeration of all possible network states. The methodology is validated through a real-world case study of a citrus distribution network, where various demand levels and carbon restrictions are examined. Sensitivity analyses reveal trade-offs between these two objectives. We can demonstrate the impact of adjusting carbon emission factors on overall supply chain performance, providing valuable insights for decision-makers aiming to balance environmental objectives with operational efficiency.</p>

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Performance estimation of a supply chain transportation with transport damage and carbon emissions

  • 期刊论文

  • 发表日期：

    2025年7月29日

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  • Ding-Hsiang Huang

摘要

Resilient and sustainable supply chain transportation is critical in today’s globalized economy. This study introduces a stochastic-capacity supply chain (SCSC) model that integrates transport damage and carbon emission constraints, addressing two key challenges faced by modern logistics networks. By considering the variability in arc capacities due to vehicle breakdowns, route disruptions, and other stochastic factors, the model captures the degradation of transportation flow, both in terms of reduced capacity and the accumulation of carbon emissions. SCSC reliability is the probability that the supply chain meets demand and carbon emission limits despite uncertainties. A simulation-based algorithm, which combines Monte Carlo sampling with linear programming formulations, is proposed to efficiently estimate this reliability measure without resorting to exhaustive enumeration of all possible network states. The methodology is validated through a real-world case study of a citrus distribution network, where various demand levels and carbon restrictions are examined. Sensitivity analyses reveal trade-offs between these two objectives. We can demonstrate the impact of adjusting carbon emission factors on overall supply chain performance, providing valuable insights for decision-makers aiming to balance environmental objectives with operational efficiency.