集装箱搬运车辆的排放感知优化：哈米纳-科特卡港案例研究
Emission-Aware Optimization of Container Handling Vehicles: A HaminaKotka Port Case Study
- Lappeenranta-Lahti University of Technology（拉彭兰塔-拉赫蒂理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对港口陆侧运营脱碳需求，提出基于线性规划的数据驱动车队调度框架，通过重新分配车辆运行时间，在不减少总工作量的前提下实现总CO2eq降低约31%、总能耗降低9.4%。
AI中文摘要：
智慧港口日益依赖物联网驱动的数字化，将异构运营流程转变为可测量、可优化的工作流。为此，本文首先简要回顾了用于港口数字化转型的物联网使能技术，重点强调传感、连接和数据集成能力如何支持港口环境中的实时决策。受《欧洲绿色协议》和可持续发展目标（SDGs）中对运营脱碳日益重视的推动，我们随后提出了一个面向芬兰哈米纳-科特卡港（穆萨洛码头）陆侧港口运营的数据驱动车队调度框架。该调度框架重新分配车辆运行时间，以在保持相同总工作量的同时最小化总CO2eq排放。研究使用了来自77辆港口运营车辆的两年数据集，在月度和年度聚合水平上进行分析，以推导车辆特定的强度指标（每运行小时的CO2eq）并识别重新分配机会。我们将调度任务建模为一个线性规划工作量分配问题，该问题在保持总所需小时数固定的情况下最小化分配小时的加权和，同时强制执行车队级别的CO2eq和能源使用上限，并尊重每辆车的运行限制。优化使用SciPy的linprog中的HiGHS线性规划求解器进行求解。结果表明，将利用率从高排放车辆转移到更高效的车辆上，可在不减少总运行小时数的情况下实现显著的系统级改进：优化后的调度实现了总CO2eq约31%的减少，同时总能耗减少9.4%。
英文摘要：
Smart ports increasingly depend on IoT-enabled digitalization to turn heterogeneous operational processes into measurable, optimizable workflows. Accordingly, this paper first provides a concise review of IoT-enabler technologies for port digital transformation, emphasizing how sensing, connectivity, and data integration capabilities support real-time decision-making in port environments. Motivated by the growing emphasis on operational decarbonization reflected in the European Green Deal and the Sustainable Development Goals (SDGs), we then present a data-driven fleet scheduling framework for landside port operations at the Port of HaminaKotka (Mussalo terminal), Finland. The scheduling framework reallocates vehicle operating hours to minimize total CO2eq emissions while preserving the same aggregate workload. The study uses a two-year dataset from 77 port-operating vehicles, analyzed at monthly and annual aggregation levels, to derive vehicle-specific intensity metrics (CO2eq per operating hour) and identify reallocation opportunities. We formulate the scheduling task as a linear programming workload allocation problem that minimizes the weighted sum of assigned hours while keeping total required hours fixed, enforcing fleet-level caps on CO2eq and energy use, and respecting per-vehicle operating limits. The optimization is solved using the HiGHS linear programming solver via SciPy's linprog. Results show that shifting utilization away from high-intensity vehicles and toward more efficient units yields substantial system-level improvements with no reduction in total operating hours: the optimized schedule achieves an approximately 31% reduction in total CO2eq and a simultaneous 9.4% reduction in total energy consumption.
