Integrated Scheduling of Handling and Spraying Operations in Smart Coal Ports: A MAPPO-Driven Adaptive Micro-Evolutionary Algorithm Framework

This study explores the integrated scheduling optimization of coal port operations, addressing the dual challenges of handling efficiency and resource conservation by coordinating equipment scheduling with stockyard spraying operations. Through a systematic analysis of operational processes in coal ports, a mixed-integer linear programming (MILP) model is developed to achieve global optimization while explicitly quantifying water and electricity consumption in spraying operations. To address this complex problem, we propose a novel hybrid algorithm that integrates a micro-evolutionary algorithm (MEA) framework with multi-agent proximal policy optimization (MAPPO), enabling adaptive decision-making for large-scale real-time scheduling. Three specialized agents for crossover, mutation, and neighborhood search achieve collaborative optimization by observing population features as states, selecting evolutionary operators as actions, and receiving composite rewards based on both population improvement and individual contributions. This strategy facilitates adaptive operator selection and optimal evolutionary direction derivation, collectively guiding population evolution toward high-quality solutions. Extensive experiments on ten scaled instances of a real-world coal port confirm the proposed algorithm’s superior performance. Compared with four other standard algorithms, it consistently yields higher hypervolume (HV) values and lower inverted generational distance (IGD) metrics, which collectively demonstrate stronger convergence capability and higher solution quality.