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Metaheuristics for Order Picking Optimisation: A Comparison Among Three Swarm-Intelligence Algorithms

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Technological and Industrial Applications Associated With Industry 4.0

Abstract

Nowadays, the Order Picking Problem (OPP) represents the most costly and time-consuming operation of warehouse management, with an average ranging from 50 to 75% of the total warehouse management cost. So, OPP is being analysed to improve logistics operations in companies. The OPP consists of dispatching a set of products, allocated in specific places in a warehouse, based in a group of customer orders. In most traditional warehouses, the optimisation methods of order picking operations are associated with time, whose model is based on the Traveling Salesperson Problem (TSP). The TSP is considered as an NP-Hard problem; thus, the development of metaheuristics approaches is justified. This chapter presents a comparison among three different optimisation metaheuristic approaches that solve the OPP. An analysis is used to evaluate and compare ant colony optimisation, elephant herding optimisation, and the bat algorithm. This study considers the number of picking aisles, the number of extra cross aisles, the number of items in the order, and the standard deviation in both the x and y axis of the product distribution in the warehouse.

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Olmos, J., Florencia, R., García, V., González, M.V., Rivera, G., Sánchez-Solís, P. (2022). Metaheuristics for Order Picking Optimisation: A Comparison Among Three Swarm-Intelligence Algorithms. In: Ochoa-Zezzatti, A., Oliva, D., Hassanien, A.E. (eds) Technological and Industrial Applications Associated With Industry 4.0 . Studies in Systems, Decision and Control, vol 347 . Springer, Cham. https://doi.org/10.1007/978-3-030-68663-5_13

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  • DOI: https://doi.org/10.1007/978-3-030-68663-5_13

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