Skip to main content
Springer Nature Link
Log in

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

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

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).

References

  • Albareda-Sambola, M., Alonso-Ayuso, A., Molina, E., & De Blas, C. S. (2009). Variable neighborhood search for order batching in a warehouse. Asia-Pacific Journal of Operational Research,26(05), 655–683.

    Google Scholar 

  • Azadnia, A. H., Taheri, S., Ghadimi, P., Mat Saman, M. Z., & Wong, K. Y. (2013). Order batching in warehouses by minimizing total tardiness: A hybrid approach of weighted association rule mining and genetic algorithms. The Scientific World Journal, 2013, 1–13.

    Google Scholar 

  • Bandyopadhyay, S., & Bhattacharya, R. (2014). Solving a tri-objective supply chain problem with modified NSGA-II algorithm. Journal of Manufacturing Systems,33(1), 41–50.

    Google Scholar 

  • Bartholdi III, J. J., & Hackman, S. T. (2014). Warehouse & distribution science: release 0.96. Atlanta, GA: The Supply Chain and Logistics Institute, School of Industrial and Systems Engineering, Georgia Institute of Technology. http://www2.isye.gatech.edu/~jjb/wh/book/editions/wh-sci-0.96.pdf. Accessed January, 2018.

  • Bertrand, J. W. M., & Fransoo, J. C. (2002). Modelling and simulation: Operations management research methodologies using quantitative modeling. International Journal of Operations and Production Management,22(2), 241–264.

    Google Scholar 

  • Bottani, E., Cecconi, M., Vignali, G., & Montanari, R. (2012). Optimisation of storage allocation in order picking operations through a genetic algorithm. International Journal of Logistics Research and Applications,15(2), 127–146.

    Google Scholar 

  • Bozer, Y. A., & Kile, J. W. (2008). Order batching in walk-and-pick order picking systems. International Journal of Production Research,46(7), 1887–1909.

    Google Scholar 

  • Cergibozan, Ç., & Tasan, A. S. (2019). Order batching operations: An overview of classification, solution techniques, and future research. Journal of Intelligent Manufacturing,30(1), 335–349.

    Google Scholar 

  • Chabot, T., Lahyani, R., Coelho, L. C., & Renaud, J. (2017). Order picking problems under weight, fragility and category constraints. International Journal of Production Research,55(21), 6361–6379.

    Google Scholar 

  • Chang, F. L., Liu, Z. X., Zheng, X., & Liu, D. D. (2007). Research on order picking optimization problem of automated warehouse. Systems Engineering-Theory & Practice,27(2), 139–143.

    Google Scholar 

  • Chen, M. C., & Wu, H. P. (2005). An association-based clustering approach to order batching considering customer demand patterns. Omega,33(4), 333–343.

    Google Scholar 

  • Chen, T. L., Cheng, C. Y., Chen, Y. Y., & Chan, L. K. (2015). An efficient hybrid algorithm for integrated order batching, sequencing and routing problem. International Journal of Production Economics,159, 158–167.

    Google Scholar 

  • Chien, C., Kim, K. H., Liu, B., & Gen, M. (2012). Advanced decision and intelligence technologies for manufacturing and logistics. Journal of Intelligent Manufacturing,23(6), 2133–2135.

    Google Scholar 

  • De Jong, K. (1988). Learning with genetic algorithms: An overview. Machine Learning,3(2–3), 121–138.

    Google Scholar 

  • De Koster, R. B. M., Johnson, A. L., & Roy, D. (2017). Warehouse design and management. International Journal of Production Research,55(21), 6327–6330.

    Google Scholar 

  • De Koster, R. B. M., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research,182(2), 481–501.

    Google Scholar 

  • Diabat, A. (2014). Hybrid algorithm for a vendor managed inventory system in a two-echelon supply chain. European Journal of Operational Research,238(1), 114–121.

    Google Scholar 

  • Diabat, A., & Deskoores, R. M. (2016). A hybrid genetic algorithm based heuristic for an integrated supply chain problem. Journal of Manufacturing Systems,38, 172–180.

    Google Scholar 

  • Elsayed, E. A., & Lee, M. K. (1996). Order processing in automated storage/retrieval systems with due dates. IIE Transactions,28(7), 567–578.

    Google Scholar 

  • Elsayed, E. A., Lee, M. K., Kim, S., & Scherer, E. (1993). Sequencing and batching procedures for minimizing earliness and tardiness penalty of order retrievals. The International Journal of Production Research,31(3), 727–738.

    Google Scholar 

  • Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2014). A new genetic algorithm for solving optimization problems. Engineering Applications of Artificial Intelligence, 27, 57–69.

    Google Scholar 

  • Gademann, N., & Van de Velde, S. (2005). Order batching to minimize total travel time in a parallel-aisle warehouse. IIE Transactions,37(1), 63–75.

    Google Scholar 

  • Gen, M., Cheng, R., & Lin, L. (2008). Network models and optimization: Multiobjective genetic algorithms approach. London: Springer.

    Google Scholar 

  • Ghiami, Y., Williams, T., & Wu, Y. (2013). A two-echelon inventory model for a deteriorating item with stock-dependent demand, partial backlogging and capacity constraints. European Journal of Operational Research,231(3), 587–597.

    Google Scholar 

  • Gibson, D. R., & Sharp, G. P. (1992). Order batching procedures. European Journal of Operational Research,58(1), 57–67.

    Google Scholar 

  • Gils, T. V., Ramaekers, K., Caris, A., & De Koster, R. B. M. (2018). Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review. European Journal of Operational Research,267(1), 1–15.

    Google Scholar 

  • Goldberg, D. E. & Lingle, R. (1985, July). Alleles, loci, and the traveling salesman problem. In: Grefenstette J. J. (Eds.), Proceedings of the first international conference on genetic algorithms and their applications—ICGA 1985 (pp. 154–159). Pittsburgh, PA: Lawrence Erlbaum Associates. https://books.google.com/. Accessed January 10, 2018.

  • Grosse, E. H., Christoph, H., Glock, C. H., & Neumann, W. P. (2017). Human factors in order picking: A content analysis of the literature. Journal International Journal of Production Research,55(5), 1260–1276.

    Google Scholar 

  • Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research,177(1), 1–21.

    Google Scholar 

  • Gu, J., Goetschalckx, M., & McGinnis, L. F. (2010). Research on warehouse design and performance evaluation: A comprehensive review. European Journal of Operational Research,203(3), 539–549.

    Google Scholar 

  • Hansen, P., & Mladenović, N. (2001). Variable neighborhood search: Principles and applications. European Journal of Operational Research,130(3), 449–467.

    Google Scholar 

  • Haupt, Randy L., & Haupt, Sue E. (2004). Practical genetic algorithms (2nd ed.). New York: Wiley.

    Google Scholar 

  • Helsgaun, K. (2000). An effective implementation of the Lin–Kernighan traveling salesman heuristic. European Journal of Operational Research,126(1), 106–130.

    Google Scholar 

  • Henn, S. (2015). Order batching and sequencing for the minimization of the total tardiness in picker-to-part warehouses. Flexible Services and Manufacturing Journal,27(1), 86–114.

    Google Scholar 

  • Henn, S., Koch, S., Doerner, K. F., Strauss, C., & Wäscher, G. (2010). Metaheuristics for the order batching problem in manual order picking systems. BuR-Business Research,3(1), 82–105.

    Google Scholar 

  • Henn, S., Koch, S., & Wäscher, G. (2012). Order batching in order picking warehouses: A survey of solution approaches. In R. Manzini (Ed.), Warehousing in the global supply chain: Advanced models, tools and applications for storage systems (pp. 105–137). London: Springer.

    Google Scholar 

  • Henn, S., & Schmid, V. (2013). Metaheuristics for order batching and sequencing in manual order picking systems. Computers & Industrial Engineering,66(2), 338–351.

    Google Scholar 

  • Henn, S., & Wäscher, G. (2012). Tabu search heuristics for the order batching problem in manual order picking systems. European Journal of Operational Research,222(3), 484–494.

    Google Scholar 

  • Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.

    Google Scholar 

  • Hsu, C. M., Chen, K. Y., & Chen, M. C. (2005). Batching orders in warehouses by minimizing travel distance with genetic algorithms. Computers in Industry,56(2), 169–178.

    Google Scholar 

  • İnkaya, T., & Akansel, M. (2017). Coordinated scheduling of the transfer lots in an assembly-type supply chain: A genetic algorithm approach. Journal of Intelligent Manufacturing,28(4), 1005–1015.

    Google Scholar 

  • Kulak, O., Sahin, Y., & Taner, M. E. (2012). Joint order batching and picker routing in single and multiple-cross-aisle warehouses using cluster-based tabu search algorithms. Flexible Services and Manufacturing Journal,24(1), 52–80.

    Google Scholar 

  • Kumar, R. S., Tiwari, M., & Goswami, A. (2016). Two-echelon fuzzy stochastic supply chain for the manufacturer-buyer integrated production-inventory system. Journal of Intelligent Manufacturing,27(4), 875–888.

    Google Scholar 

  • Ledari, A. M., Pasandideh, S. H. R., & Koupaei, M. N. (2018). A new newsvendor policy model for dual-sourcing supply chains by considering disruption risk and special order. Journal of Intelligent Manufacturing,25(6), 1367–1376.

    Google Scholar 

  • Li, J., Huang, R., & Dai, J. B. (2017). Joint optimisation of order batching and picker routing in the online retailer’s warehouse in China. International Journal of Production Research, 55(2), 447–461.

    Google Scholar 

  • Marchet, G., Melacini, M., & Perotti, S. (2015). Investigating order picking system adoption: A case-study-based approach. International Journal of Logistics Research and Applications,18(1), 82–98.

    Google Scholar 

  • Matthews, J., & Visagie, S. (2013). Order sequencing on a unidirectional cyclical picking line. European Journal of Operational Research,231(1), 79–87.

    Google Scholar 

  • Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers & Operations Research,24(11), 1097–1100.

    Google Scholar 

  • Mousavi, S. M., Bahreininejad, A., Musa, S. N., & Yusof, F. (2017). A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. Journal of Intelligent Manufacturing,28(1), 191–206.

    Google Scholar 

  • Mousavi, S. M., Hajipour, V., Niaki, S. T. A., & Alikar, N. (2013). Optimizing multi-item multi-period inventory control system with discounted cash flow and inflation: Two calibrated meta-heuristic algorithms. Applied Mathematical Modelling,37(4), 2241–2256.

    Google Scholar 

  • Park, K., & Kyung, G. (2014). Optimization of total inventory cost and order fill rate in a supply chain using PSO. The International Journal of Advanced Manufacturing Technology,70(9–12), 1533–1541.

    Google Scholar 

  • Petersen, C. G. (1995). Routeing and storage policy interaction in order picking operations. Decision Sciences Institute Proceedings,3, 1614–1616.

    Google Scholar 

  • Petersen, C. G., & Aase, G. (2004). A comparison of picking, storage, and routing policies in manual order picking. International Journal of Production Economics,92(1), 11–19.

    Google Scholar 

  • Pinto, A. R. F., Crepaldi, A. F., & Nagano, M. S. (2018). A genetic algorithm applied to pick sequencing for billing. Journal of Intelligent Manufacturing,29(2), 405–422.

    Google Scholar 

  • Rim, S. C., & Park, I. S. (2008). Order picking plan to maximize the order fill rate. Computers & Industrial Engineering,55(3), 557–566.

    Google Scholar 

  • Roodbergen, K. J., & de Koster, R. (2001). Routing order pickers in a warehouse with a middle aisle. European Journal of Operational Research,133(1), 32–43.

    Google Scholar 

  • Scholz, A., Schubert, D., & Wäscher, G. (2017). Order picking with multiple pickers and due dates—Simultaneous solution of order batching, batch assignment and sequencing, and picker routing problems. European Journal of Operational Research,263(2), 461–478.

    Google Scholar 

  • Scholz, A., & Wäscher, G. (2017). Order batching and picker routing in manual order picking systems: the benefits of integrated routing. Central European Journal of Operations Research,25(2), 491–520.

    Google Scholar 

  • Seyedrezaei, M., Najafi, S. E., Aghajani, A., & Valami, H. B. (2012). Designing a genetic algorithm to optimize fulfilled orders in order picking planning problem with probabilistic demand. International Journal,1(2), 40–57.

    Google Scholar 

  • Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. A. (2010). Facilities planning. New York: Wiley.

    Google Scholar 

  • Tsai, C. Y., Liou, J. J., & Huang, T. M. (2008). Using a multiple-GA method to solve the batch picking problem: Considering travel distance and order due time. International Journal of Production Research,46(22), 6533–6555.

    Google Scholar 

  • Van Nieuwenhuyse, I., & de Koster, R. B. (2009). Evaluating order throughput time in 2-block warehouses with time window batching. International Journal of Production Economics,121(2), 654–664.

    Google Scholar 

  • Won, J., & Olafsson, S. (2005). Joint order batching and order picking in warehouse operations. International Journal of Production Research,43(7), 1427–1442.

    Google Scholar 

  • Xu, X., Liu, T., Li, K., & Dong, W. (2014). Evaluating order throughput time with variable time window batching. International Journal of Production Research,52(8), 2232–2242.

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  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

Authors
  1. Anderson Rogério Faia Pinto
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Marcelo Seido Nagano
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Marcelo Seido Nagano.

Additional information

Publisher note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-019-01470-3

Keywords