Efficiently Optimizing Crane Scheduling: A Rule-Based Approach for Dynamic and Uncertain Environments

In logistics and industrial operations, optimizing crane scheduling is crucial for efficiency and resource allocation. Given the dynamic nature of crane scheduling environments, influenced by factors such as fluctuating order volumes and varying crane availability, robust scheduling solutions are imperative. This paper introduces a rule-based approach designed to optimize crane scheduling within a simulated dynamic and uncertain environment, focusing on real-time responsiveness, efficient task allocation, and continuous operational efficiency. Our approach was developed while participating in the “Crane Scheduling” track of the “Dynamic Stacking Optimization in Uncertain Environments” (DynStack) competition, held with the Genetic and Evolutionary Computation Conference (GECCO) 2024. Our experimental results underscore the efficacy of our approach in achieving zero delivery errors, maximizing the handling of blocks, and minimizing parking durations, hereby promoting cost-effectiveness and environmental sustainability in crane operations. As of July 17, 2024, our approach has secured first place in the “Crane Scheduling” competition.