Order batching and order picking with 3D positioning of the articles: solution through a hybrid evolutionary algorithm

Fabio M. Miguel; Mariano Frutos; Máximo Méndez; Fernando Tohmé; Fabio M. Miguel; Mariano Frutos; Máximo Méndez; Fernando Tohmé

doi:10.3934/mbe.2022259

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 6: 5546-5563. doi: 10.3934/mbe.2022259

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Order batching and order picking with 3D positioning of the articles: solution through a hybrid evolutionary algorithm

1.
Universidad Nacional de Río Negro, Sede Alto Valle y Valle Medio, Argentina
2.
Engineering Department, Universidad Nacional del Sur, IIESS UNS CONICET, Argentina
3.
Universidad de Las Palmas de Gran Canaria (ULPGC), Instituto Universitario SIANI, Spain
4.
Economics Department, Universidad Nacional del Sur, INMABB UNS CONICET, Argentina

Academic Editor: Yang Kuang

Received: 29 December 2021 Revised: 02 March 2022 Accepted: 22 March 2022 Published: 28 March 2022

A critical factor in the logistic management of firms is the degree of efficiency of the operations in distribution centers. Of particular interest is the pick-up process, since it is the costliest operation, amounting to 50 and up to 75% of the total cost of the activities in storage facilities. In this paper we jointly address the order batching problem (OBP) and the order picking problem (OPP). The former problem amounts to find optimal batches of goods to be picked up, by restructuring incoming orders by either splitting up large orders or combining small orders into larger ones that can then be picked in a single picking tour. The OPP, in turn, involves identifying optimal sequences of visits to the storage positions in which the goods to be included in each batch are stored. We seek to design a plan that minimizes the total operational cost of the pick-up process, proportional to the displacement times around the storage area as well as to all the time spent in pick-ups and finishing up orders to be punctually delivered. Earliness or tardiness will induce inefficiency costs, be it because of the excessive use of space or breaches of contracts with customers. Tsai, Liou and Huang in 2008 have generated 2D and 3D instances. In previous works we have addressed the 2D ones, achieving very good results. Here we focus on 3D instances (the articles are placed at different levels in the storage center), which involve a higher complexity. This contributes to improve the performance of the hybrid evolutionary algorithm (HEA) applied in our previous works.
- order batching,
- order picking,
- evolutionary algorithm,
- local search,
- optimization
Citation: Fabio M. Miguel, Mariano Frutos, Máximo Méndez, Fernando Tohmé. Order batching and order picking with 3D positioning of the articles: solution through a hybrid evolutionary algorithm[J]. Mathematical Biosciences and Engineering, 2022, 19(6): 5546-5563. doi: 10.3934/mbe.2022259

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Abstract

A critical factor in the logistic management of firms is the degree of efficiency of the operations in distribution centers. Of particular interest is the pick-up process, since it is the costliest operation, amounting to 50 and up to 75% of the total cost of the activities in storage facilities. In this paper we jointly address the order batching problem (OBP) and the order picking problem (OPP). The former problem amounts to find optimal batches of goods to be picked up, by restructuring incoming orders by either splitting up large orders or combining small orders into larger ones that can then be picked in a single picking tour. The OPP, in turn, involves identifying optimal sequences of visits to the storage positions in which the goods to be included in each batch are stored. We seek to design a plan that minimizes the total operational cost of the pick-up process, proportional to the displacement times around the storage area as well as to all the time spent in pick-ups and finishing up orders to be punctually delivered. Earliness or tardiness will induce inefficiency costs, be it because of the excessive use of space or breaches of contracts with customers. Tsai, Liou and Huang in 2008 have generated 2D and 3D instances. In previous works we have addressed the 2D ones, achieving very good results. Here we focus on 3D instances (the articles are placed at different levels in the storage center), which involve a higher complexity. This contributes to improve the performance of the hybrid evolutionary algorithm (HEA) applied in our previous works.

References

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