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Congestion arises due to the significant influx of containers and ships from various global locations, primarily resulting from inadequate resource allocation or inaccurate resource configuration. This paper examines the optimal configuration of quay cranes and prime movers, required for a vessel to efficiently complete unloading and loading operations. Through simulation, the model generates performance metrics such as the average container waiting time, average utilisation of quay cranes and prime movers separately, and the number of containers handled within an observed time interval. The efficiency of handling equipment configurations is evaluated using the Charnes-Cooper-Rhodes data envelopment analysis (CCR DEA) model and the bi-objective multi-criteria data envelopment analysis (BiO-MCDEA). The optimal handling equipment configuration is then determined using the super efficiency of both models. The model includes quay crane number, prime mover number and average container waiting time as inputs and the other three simulation performance measures and Gross Moves Per Hour (GMPH) for quay cranes and prime movers as outputs. Three quay cranes with fifteen prime movers are the best unloading and loading configuration for super efficiency CCR DEA. Alternatively, super efficiency BiO-MCDEA recommends two quay cranes with eighteen prime movers. The obtained results provide valuable insights for decision makers to enhance terminal productivity and optimise handling equipment efficiency
Aznam et al. (Wed,) studied this question.