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Genetic algorithms applied to integration and optimization of billing and picking processes

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Abstract

This article intends to provide a computational tool that integrates and provides optimized solutions to two interdependent problems called Optimized Billing Sequencing (OBS) and Optimized Picking Sequence (OPS). These problems are addressed separately by the existing literature and refer respectively to the optimization of billing and picking processes in a typical warehouse with low-level picker-to-parts system. Integration literature is, therefore, limited and there is a demand for more robust OBS/OPS optimization methods. This approach will deal with practical dilemmas that have not been addressed by researchers yet to propose an extension to the OBS model by Pinto et al. (J Intell Manuf 29(2):405–422, 2018) along with a specific variation of the Order Batching and Sequencing Problem. The premise is to prove to managers the possibility of making more consistent decisions about the trade-off between the level of customer service and the warehouse efficiency. The proposed tool is formulated by the integration of two Genetic Algorithms called GA-OBS and GA-OPS where GA-OBS maximizes the order portfolio billing and generates the picking order to the OPS, whereas GA-OPS comprises the iteration of batch and routing algorithms to minimize picking total time and cost to the OPS. Experiments with problems with different complexity levels showed that the proposed tool produces solutions of satisfactory quality to OBS/OPS. The approach proposed fills a gap in the literature and makes innovative contributions to the development of more suitable optimization methods to the reality of warehouses.

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Notes

  1. Refers to the planning, retrieving and transporting SKUs mix from their locations in the WA to the P/D area where they are checked, packaged and shipped to fulfill internal or external customers’ orders of organization (De Koster et al. 2007; Bottani et al. 2012).

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Acknowledgements

This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) - Brazil under Grant Number 306075/2017-2 and 430137/2018-4 and by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Brazil.

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  1. Production Engineering Department, São Carlos School of Engineering, University of São Paulo, Av. Trabalhador São Carlense, 400, São Carlos, SP, CEP: 13566-590, Brazil

    Anderson Rogério Faia Pinto & Marcelo Seido Nagano

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  1. Anderson Rogério Faia Pinto
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  2. Marcelo Seido Nagano
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Correspondence to Marcelo Seido Nagano.

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Pinto, A.R.F., Nagano, M.S. Genetic algorithms applied to integration and optimization of billing and picking processes. J Intell Manuf 31, 641–659 (2020). https://doi.org/10.1007/s10845-019-01470-3

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