Space resource allocation of dry bulk terminal yard based on logic-based Benders decomposition algorithm

This paper investigates the dual-objective space resource allocation problem for a dry bulk port yard responsible for both imports and exports. The study considers factors such as order operation time, operation sequencing, machine scheduling, and space allocation within the yard during the planning period. The dual-objective are minimizing order delay time and reducing the space configuration cost of the storage yard, which includes stacker-reclaimer movement costs, material mixing costs, and relocation costs. To effectively address this problem, a logic-based Benders decomposition algorithm is proposed. This approach decomposes the model into two sub-models based on the storage yard's operational process: task-machine-material pad and material padmaterial slot. The primary model determines task scheduling, machine assignments, and a rough material layout, while the secondary model refines the material layout based on the output of the primary model. Extensive case studies demonstrate that the proposed logic-based Benders decomposition algorithm generally outperforms the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm in terms of solution quality, and both of them can solve the result in a short time.