Stockyard allocation in dry bulk ports considering resource consumption reduction of spraying operations

Abstract

Stockyard allocation is a crucial segment of operational decision-making in dry bulk ports (DBPs). The stockyard allocation plan determines the storage position and duration of each stockpile to avoid operational delays in stockyards. Spraying operations, a unique operation in DBPs, are significantly influenced by stockyard allocation plans. Port operators regularly conduct spraying operations to prevent dust diffusion during the storage of dry bulk materials in stockyards. The spraying operation system consumes substantial electrical energy to transport the water to the designated material pile and spray large amounts of water onto its surface. Due to the layout constraints of pipelines and spraying nozzles, different stockyard allocation plans lead the varying consumptions of electrical energy and water resources for spraying operations. However, previous studies on the stockyard allocation problem frequently ignore the impacts of the stockyard allocation plan on the resource consumption of spraying operations. To fill this gap, this paper proposes a stockyard allocation model that uniquely considers the resource consumption of spraying operations to balance operation efficiency and resource consumption in stockyards from a global perspective. With the goal of minimizing the total cost, including operation delay penalties in stockyards and the electricity and water costs of spraying operations, a series of comprehensive experiments was conducted based on practical data collected from a major DBP in China under varying stockpile densities and stockyard efficiency properties. The results clearly show significant differences in the stockyard allocation plan and the total cost resulting from considering and disregarding the resource consumption of spraying operations in the stockyard allocation decision-making process. With only a 3.09% increase in average delay time in stockyards, the proposed model can reduce the total cost by 19.26%, the electricity cost by 54.06% and the water cost reduction by 35.09%. Meanwhile, the carbon emissions are reduced 75 tons on average for spraying operations and the Whale Optimization Algorithm (WOA) performs well on large-scale instances. The proposed model can avoid unnecessary resource consumption of spraying operations with acceptable operation delay penalties in stockyards.