1. Proceedings
  2. I3M
  3. 2022
  4. MAS
  5. 10.46354/i3m.2022.mas.019

Event-based modeling and simulation for optimizing order picking

  • a Pasquale Legato ,
  • b Massimiliano Matteucci,
  • c Rina Mary Mazza
  • a,c University of Calabria, Via P. Bucci 42C, Rende (CS), 87036, Italy
  • b PAC 2000 A Società Cooperativa, Via del Rame, Perugia (PG), 06134, Italy
Cite as
Legato P., Matteucci M., and Mazza R.M. (2022).,Event-based modeling and simulation for optimizing order picking. Proceedings of the 21st International Conference on Modelling and Applied Simulation MAS 2022). , 019 . DOI: https://doi.org/10.46354/i3m.2022.mas.019

Abstract

Manually-performed order picking is a very common, yet very expensive process in warehouse operation. Multiple pickers work simultaneously in the picking area to respond to customer orders “as soon as possible”. To do so, company policies are devised to meet performance requirements, while guaranteeing minimum interference among pickers. Here we model a person-to-goods manual system by means of an event graph (EG). An EG-based representation covers the event-driven logic of the picking system, as well as the need of providing a highly-detailed description of the system logic. EGs also represent the common ground for integrating complementary analysis techniques, such as discrete-event simulation, digital twins and process mining, which allows to move towards a truly connected supply chain. We then resort to simulation to exploit the benefit of replacing a time-free order behavior with a time-window based organization for order collection. Numerical results are presented to support decision making in a real cooperative that provides wholesale distribution to customers in Central and Sothern Italy.

References

  1. Al-Araidah, O., Okudan-Kremer, G., Gunay, E.E. and Chu, C.-Y. (2021). A Monte Carlo simulation to estimate fatigue allowance for female order pickers in high traffic manual picking systems. International Journal of Production Research, 59(15): 4711-4722.
  2. Alfano, S., Brettone A.S., Groccia M.C., Sia, R., and Vita, A.F. (to appear). Optimizing the order picking process via simulation: a case study. In 21st International Conference on Modelling and Applied Simulation, MAS 2022, Rome, IT, September 19-21.
  3. Altarazi, S.A. and Ammouri, M.M. (2018). Concurrent manual-order-picking warehouse design: A simulation-based design of experiments approach. International Journal of Production Research, 56(23):7103-7121.
  4. Andriansyah, R., De Koning, W.W.H., Jordan, R.M.E., Etman, L.F.P. and Rooda, J.E. (2011). A process algebra based simulation model of a miniload-workstation order picking system”. Computers in Industry, 62(3):292-300.
  5. Andriansyah, R. Etman, P, and Rooda, J. (2009). Simulation model of a single-server order picking workstation using aggregate process times. In 1st International Conference on Advances in System Simulation, SIMUL 2009, September 20-25, Porto, PT, art. no. 5283980, 23-31.
  6. Bahrami, B., Aghezzaf, E.-H., and Limere, V. (2017). Using simulation to analyze picker blocking in manual order picking systems”. Procedia Manufacturing, 11 1798-1808.
  7. Bharre, S. and Chung, S.H. (2020). Routing optimization for warehouse order picking via simulation. In Proceedings of the 2016 Industrial and Systems Engineering Research Conference, ISERC 2016, Anaheim, CA, USA, May 21-24, 2210-2215.
  8. Bozer, Y.A. and Aldarondo, F.J. (2018). A simulation-based comparison of two goods-to-person order picking systems in an online retail setting. International Journal of Production Research, 56(11):3838-3858.
  9. Bučková, M., Krajčovič, M. and Edl, M (2017). Computer simulation and optimization of transport distances of order picking processes. In Procedia Engineering, 192:69-74.
  10. Burinskienė, A, Davidavičienė, V., Raudeliūnienė, J., and Meidutė-Kavaliauskienė, I. (2018). Simulation and order picking in a very-narrow-aisle warehouse. Economic Research-Ekonomska Istrazivanja, 31(1):1574-1589.
  11. Coelho, F., Relvas, S., and Barbosa-Póvoa, A.P. (2018). Simulation of an order picking system in a manufacturing supermarket using collaborative robots. In Proceedings - European Council for Modelling and Simulation, ECMS 2018, Wilhelmshaven, DE, May 22-25, 83-88.
  12. Elbert, R. and Muller, J.P. (2017). The impact of item weight on travel times in picker-to-parts order picking: An agent-based simulation approach. In Proceedings - Winter Simulation Conference, Las Vegas, NV, USA, December 3-6, 3162-3173.
  13. Faria, F. and Reis, V. (2015). An original simulation model to improve the order picking performance: Case study of an automated warehouse. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9335, 689-703.
  14. Francisco, R.P., Campos, D.P., Frazzon, E.N., and Machado, R.L. (2016). On the application of modelling and simulation to compare human- and automation-based order-picking systems. In IFAC-PapersOnLine, 49(12):1062-1067. 
  15. Glock, C.H., Grosse, E.H., Neumann, W.P., and Feldman, A. (2021). Assistive devices for manual materials handling in warehouses: A systematic literature review. International Journal of Production Research, 59(11):3446-3469.
  16. Goldscheid, C., Deuse, J., Schlüter, N., and Crostack, H.-A. (2007). Optimization of order picking quality by simulation of test strategies. ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, 102(6):351-354.
  17. Güller, M. and Hegmanns, T. (2014). Simulation-based performance analysis of a miniload multishuttle order picking System. In Procedia CIRP, 17 475-480.
  18. Guo, J., Zhou, L., and Zhu, J. (2011). Stochastic model and simulation research for random-storage S-type manual order picking. In Key Engineering Materials, 467 92-97.
  19. Hong, S. (2019). A performance evaluation of bucket simulation approaches. Computers and Industrial Engineering, 135:120-131.
  20. Kašparová, P. and Dyntar, J. (2021). Effective designing of order picking systems using dynamic simulation. Acta Informatica Pragensia, 10(1):108-120.
  21. Kauke, D, Rett, A., and Fottner, J. (2019). Draft planning of order picking systems by simulation – impact of various planning problems on the performance development of different order picking systems. Logistics Journal.
  22. Kawczynski, L. and Aguilar-Sommar, R. (2006). Comprehensive design of an order picking line by simulation. In IFAC Proceedings Volumes, 39(3):365-370.
  23. Kelly, D.L. (1979). Application of simulations to order-picking planning and design. In Fall Industrial Engineering Conference, American Institute of Industrial Engineers, Houston, TX, USA, November 13-16, 135-140.
  24. Klodawski, M., Jachimowski, R., Jacyna-Golda, I., and Izdebski, M. (2018). Simulation analysis of order picking efficiency with congestion situations. International Journal of Simulation Modelling, 17(3):431-443.
  25. Kostrzewski, M. (2020). Sensitivity analysis of selected parameters in the order picking process simulation model, with randomly generated orders. Entropy, 22(4), art. no. 423.
  26. Kostrzewski, M., Gnap, J., Varjan, P., and Likos, M. (2020). Application of simulation methods for study on availability of one-aisle machine order picking process. Communications - Scientific Letters of the University of Zilina, 22(2):107-114.
  27. Kumar, S., Narkhede, B.E., and Jain, K. (2021). Revisiting the warehouse research through an evolutionary lens: A review from 1990 to 2019. International Journal of Production Research, 59(11):3470-3492
  28. Lerher, T., Strmčnik, V., Potrč, I., Šraml, M., Jerman, B., and Zrnić, N. (2016). Simulation-based performance analysis of order picking systems. In 13th International Conference on Industrial Logistics, ICIL 2016, September 28 - October 1, Zakopane, PL, 144-155.
  29. Lu, M., Kise, H. Karuno, Y., and Tanabe, M. (2001). Simulation for permutational circulative vehicle routing system: Application to order picking in an AS/RS. Transactions of the Japan Society of Mechanical Engineers, Part C, 67(661):3040-3046.
  30. Marcoulaki, E.C., Broulias, G.P., and Chondrocoukis, G.P. (2005). Optimizing warehouse arrangement using order picking data and Monte Carlo simulation. Journal of Interdisciplinary Mathematics, 8(2):253-263.
  31. Masae, M., Glock, C.H. and Grosse, E.H. (2020). Order picker routing in warehouses: A systematic literature review. International Journal of Production Economics, 224 art. no. 107564
  32. Masae, M., Glock, C.H. and Grosse, E.H. (2020). Order picker routing in warehouses: A systematic literature review. International Journal of Production Economics, 224 art. no. 107564
  33. Molnár, B. (2005). Multi-criteria scheduling of order picking processes with simulation optimization. Periodica Polytechnica Transportation Engineering, 33(1-2):59-68.
  34. Navarro, M.M. (2020). Simulation of a manual order picking system in a convenience store chain distribution center. In Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology, July 27-31, art. no. 629:759-767.
  35. Quinn, E.B. and Norman, T.A. (1979). Application of simulations to order-picking planning and design. In Fall Industrial Engineering Conference, American Institute of Industrial Engineers, Houston, TX, USA, November 13-16, 126-134.
  36. Renner, P. and Pfeiffer, T. (2017). Augmented reality assistance in the central field-of-view outperforms peripheral displays for order picking: Results from a virtual reality simulation study. In Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017, Nantes, FR, October 9-13, art. no. 8088476, 176-181.
  37. Schruben, L.W. (1983). Simulation modeling with event graphs. Communications of the ACM, 26(11):957-963.
  38. Schruben, L.W. (1995). Building reusable simulators using hierarchical event graphs. In Winter Simulation Conference Proceedings, 1995, Arlington, VA, USA, December 03-06, 472-475.
  39. Shetty, N., Sah, B., and Chung, S.H. (2020). Route optimization for warehouse order picking operations via vehicle routing and simulation. SN Applied Sciences, 2(2), art. no. 311.
  40. Souiden, S., Cerqueus, A., Delorme, X., and Rascle, J.-L. (2021). Simulation model for a semi-automated retail order picking system under uncertainty. In IFIP Advances in Information and Communication Technology, IFIPAICT, 629 759-767.
  41. Ulbrich, A., Galka, S., and Günthner, W.A. (2007). Simulation of multi-Level order-picking systems within rough planning for decision making. In 21st European Conference on Modelling and Simulation: Simulations in United Europe, ECMS 2007, Prague, CZ, June 4 - 6, 542-547.
  42. Ulbrich, A., Galka, S., and Günthner, W.A. (2009). Simulation as decision support for the planning of order-picking-systems. In Summer Computer Simulation Conference 2009, SCSC 2009, Part of the 2009 ISMc, Istanbul, TR, June 13-16, 41(3):322-327.
  43. Ulbrich, A., Galka, S., and Günthner, W.A. (2010). Secure planning of order picking systems with the aid of simulation. In Proceedings of the Annual Hawaii International Conference on System Sciences, Koloa, Kauai, HI, USA, art. no. 5428648.
  44. Urzúa, M., Mendoza, A. and González, A.O. (2019). Evaluating the impact of order picking strategies on the order fulfilment time: A simulation study. Acta Logistica, 6(4):103-114.
  45. Urzúa, M., Mendoza, A. and González, A.O. (2018). Improving order fulfillment time: A simulation study to evaluate picking strategies. In IISE Annual Conference and Expo, Orlando, FL, USA, May 19-22, 1139-1144.
  46. Wasusri, T. and Theerawongsathon, P. (2016). An application of discrete event simulation on order picking strategies: A case study of footwear warehouses. In Proceedings - 30th European Conference on Modelling and Simulation, ECMS 2016, Regensburg, DE, May 31 - June 3, 121-127.
  47. Winkelhaus, S., Zhang, Grosse, E.H., and Glock, C.H. (2022). Hybrid order picking: A simulation model of a joint manual and autonomous order picking system. Computers and Industrial Engineering, 167 art. no. 107981.
  48. 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.
  49. Wagner, G. (2021). Business process modeling and simulation with DPMN: Processing activities. In Proceedings - Winter Simulation Conference, Phoenix, AZ, USA, December 13-15, 1-15.
  50. Yang, M.-F. (2008). Using simulation to object-oriented order picking system. Information Technology Journal 7(1):224-227.
  51. Yener, F. and Yazgan, H.R. (2019). Optimal warehouse design: Literature review and case study application. Computers and Industrial Engineering, 129:1-13.
  52. Zhou, L., Zhu, J., and Guo, J. (2010). Stochastic model and simulation research for random-storage return-type manual order picking. In 2010 International Conference on Logistics Engineering and Intelligent Transportation Systems, LEITS2010 - Proceedings, Wuhan, CN, November 26-28, art. no. 5664970 308-311.
  53. Zhu, J., Guo, J., and Zhou, L. (2011). Research on stochastic model and simulation for sorted-storage S-type manual order picking. In Key Engineering Materials 467-469:98-103.