Outfitting pallet picking involves the retrieval of items from their storage sites in shipbuilding enterprises. A major issue in manual pallet picking operations is the transformation of outfitting pallets into picking batches (pallet batching). Considering the influence to the subsequent distribution and production processes, a mathematical model for the batching and picking problem of outfitting pallets is formulated with the objective of minimizing the total tardiness of all pallets. According to the characteristics of outfitting pallet picking operations, an Improved Genetic Algorithm (IGA) is proposed. A reversal operator is specially introduced to increase the local search ability of the standard genetic algorithm and speed up the evolution. Benchmarked against the solutions produced by the Earliest Due Date (EDD) rule, the performance of IGA is studied under different picking operations with different workloads and tightness of due dates. A series of numerical experiments are carried out to verify the researches. The results clearly show that IGA is competitive since it improves the solutions by 68.5%, on average, relative to the EDD.
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