1. Proceedings
  2. I3M
  3. 2022
  4. MAS
  5. 10.46354/i3m.2022.mas.017

Optimizing the order picking process via simulation: a case study

  • a Salvatore Alfano,
  • b Anna Simona Brettone,
  • c Maria Carmela Groccia, 
  • d Roberto Sia, 
  • e Antonio Francesco Vita 
  • a,b,c,d,e PAC Logistics Srl, Via del Rame, Perugia, 06134, Italia
Cite as
Alfano S., Anna Brettone S., Groccia M.C, Sia R., and Vita A.F., (2022).,Optimizing the order picking process via simulation: a case study. Proceedings of the 21st International Conference on Modelling and Applied Simulation MAS 2022). , 017 . DOI: https://doi.org/10.46354/i3m.2022.mas.017

Abstract

A simulation-based approach is proposed to evaluate the performance of one of the most complex and expensive logistic functions taking place in distribution centers: the order picking process. The resulting case study is triggered by the need to switch from an order packing policy by item quantity to item volume in PAC 2000A, the largest cooperative of the CONAD consortium in Central-Southern Italy. The underlying discrete-event simulation model mimics warehouse organization, rules and behavior with the aim of pursuing optimality in terms of daily productivity and the corresponding savings on resources to be deployed. In particular, focus is on the number of supports and, thus, the space required to fulfil orders placed by retailers. Ad hoc scenarios are used to verify simulation credibility, while verification is carried out by using real data from the company’s 2022 database. Numerical results show how, upon the non-deterministic arrival of customer orders in batch, the simulator allows to verify that the order packing policy by item volume, rather than quantity significantly outperforms the current company practice.

References

  1. Banks, J., Carson, J.S., Nelson, B.L. and Nicol., D.M. (2000). Discrete-event system simulation. 3rd ed. Upper Saddle River, New Jersey: Prentice-Hall, Inc.
  2. de Koster, R., Le-Duc, T., and Roodbergen, K.J. (2007). Design and control of warehouse order picking: a literature review. European Journal of Operational Research 182(2):481-501.
  3. Furfaro, A., Pace, P., and Parise, A. (2020). Facing DDoS bandwidth flooding attacks. Simulation Modelling Practice and Theory 98, 101984.
  4. Gong, Y. and De Koster, R. (2011). A review on stochastic models and analysis of warehouse operations. Logistics Research 3(4):191-205.
  5. Kelton, W.D. and Sadowski, D.A. (2002). Simulation with Arena, McGraw-Hill, New York, Second Edition.
  6. Legato, P., Mazza, R.M. and Trunfio, R. (2013). Medcenter Container Terminal uses simulation in housekeeping operations. Interfaces 43:313-324.
  7. Legato, P. and Mazza, R.M. (2018). A Decision Support System for Integrated Container Handling in a Transshipment Hub. Decision Support Systems 108:45-56.
  8. Marchet, G., Melacini, M. and Perotti, S. (2015). Investigating order picking system adoption: a case-study-based approach. International Journal of Logistics Research and Applications 18(1):82-98.
  9. Nakayama, M. (2006). Output analysis for simulation. In Proceedings of the 2006 Winter Simulation Conference, edited by L.F. Perrone, F.P. Wieland, J. Liu, B.G. Lawson, D.M. Nicol, and R.M. Fujimoto, 36–46. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
  10. Rockwell's Arena version 11.00.00 CPR 7 [Copyright© 1983–2006 Rockwell Automation, Inc.].
  11. Sargent, R.G. (2010). Verification and validation of simulation models, In Proceedings – Winter Simulation Conference, Orlando, FL, USA, IEEE, pp. 166–183.
  12. van Gils T., Ramaekers, K., Caris, A., and de Koster, R. (2018). Designing efficient order picking systems by combining planning problems: state-of-the-art classification and review. European Journal of Operational Research 267:1–15.