Emission-Aware Optimization of Container Handling Vehicles: A HaminaKotka Port Case Study

Abstract

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.