IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v219y2012i2p335-346.html
   My bibliography  Save this article

Optimizing a dynamic order-picking process

Author

Listed:
  • Bukchin, Yossi
  • Khmelnitsky, Eugene
  • Yakuel, Pini

Abstract

This research studies the problem of batching orders in a dynamic, finite-horizon environment to minimize order tardiness and overtime costs of the pickers. The problem introduces the following trade-off: at every period, the picker has to decide whether to go on a tour and pick the accumulated orders, or to wait for more orders to arrive. By waiting, the picker risks higher tardiness of existing orders on the account of lower tardiness of future orders. We use a Markov decision process (MDP) based approach to set an optimal decision making policy. In order to evaluate the potential improvement of the proposed approach in practice, we compare the optimal policy with two naïve heuristics: (1) “Go on tour immediately after an order arrives”, and, (2) “Wait as long as the current orders can be picked and supplied on time”. The optimal policy shows a considerable improvement over the naïve heuristics, in the range of 7–99%, where the specific values depend on the picking process parameters. We have found that one measure, the slack percentage of the picking process, associated with the difference between the promised lead time and the single item picking time, predicts quite accurately the cost reduction generated by the optimal policy. Since relatively small-scale problems could be solved by the optimal algorithm, a heuristic was developed, based on the structure and properties of the optimal solutions. Numerical results show that the proposed heuristic, MDP-H, outperforms the naïve heuristics in all experiments. As compared to the optimal solution, MDP-H provides close to optimal results for a slack of up to 40%.

Suggested Citation

  • Bukchin, Yossi & Khmelnitsky, Eugene & Yakuel, Pini, 2012. "Optimizing a dynamic order-picking process," European Journal of Operational Research, Elsevier, vol. 219(2), pages 335-346.
  • Handle: RePEc:eee:ejores:v:219:y:2012:i:2:p:335-346
    DOI: 10.1016/j.ejor.2011.12.041
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221711011350
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2011.12.041?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gibson, David R. & Sharp, Gunter P., 1992. "Order batching procedures," European Journal of Operational Research, Elsevier, vol. 58(1), pages 57-67, April.
    2. Jane, Chin-Chia & Laih, Yih-Wenn, 2005. "A clustering algorithm for item assignment in a synchronized zone order picking system," European Journal of Operational Research, Elsevier, vol. 166(2), pages 489-496, October.
    3. Petersen, Charles G. & Aase, Gerald, 2004. "A comparison of picking, storage, and routing policies in manual order picking," International Journal of Production Economics, Elsevier, vol. 92(1), pages 11-19, November.
    4. Roodbergen, Kees Jan & de Koster, Rene, 2001. "Routing order pickers in a warehouse with a middle aisle," European Journal of Operational Research, Elsevier, vol. 133(1), pages 32-43, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Boysen, Nils & de Koster, René & Weidinger, Felix, 2019. "Warehousing in the e-commerce era: A survey," European Journal of Operational Research, Elsevier, vol. 277(2), pages 396-411.
    2. Nicolas, Lenoble & Yannick, Frein & Ramzi, Hammami, 2018. "Order batching in an automated warehouse with several vertical lift modules: Optimization and experiments with real data," European Journal of Operational Research, Elsevier, vol. 267(3), pages 958-976.
    3. Zhang, Jun & Wang, Xuping & Huang, Kai, 2018. "On-line scheduling of order picking and delivery with multiple zones and limited vehicle capacity," Omega, Elsevier, vol. 79(C), pages 104-115.
    4. Pardo, Eduardo G. & Gil-Borrás, Sergio & Alonso-Ayuso, Antonio & Duarte, Abraham, 2024. "Order batching problems: Taxonomy and literature review," European Journal of Operational Research, Elsevier, vol. 313(1), pages 1-24.
    5. Giannikas, Vaggelis & Lu, Wenrong & Robertson, Brian & McFarlane, Duncan, 2017. "An interventionist strategy for warehouse order picking: Evidence from two case studies," International Journal of Production Economics, Elsevier, vol. 189(C), pages 63-76.
    6. Zhang, Jun & Liu, Feng & Tang, Jiafu & Li, Yanhui, 2019. "The online integrated order picking and delivery considering Pickers’ learning effects for an O2O community supermarket," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 180-199.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. de Koster, Rene & Le-Duc, Tho & Roodbergen, Kees Jan, 2007. "Design and control of warehouse order picking: A literature review," European Journal of Operational Research, Elsevier, vol. 182(2), pages 481-501, October.
    2. Masae, Makusee & Glock, Christoph H. & Vichitkunakorn, Panupong, 2021. "A method for efficiently routing order pickers in the leaf warehouse," International Journal of Production Economics, Elsevier, vol. 234(C).
    3. Yu, M. & de Koster, M.B.M., 2007. "Performance Approximation and Design of Pick-and-Pass Order Picking Systems," ERIM Report Series Research in Management ERS-2007-082-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    4. Le-Duc, T. & de Koster, M.B.M., 2005. "Determining Number of Zones in a Pick-and-pack Orderpicking System," ERIM Report Series Research in Management ERS-2005-029-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. De Santis, Roberta & Montanari, Roberto & Vignali, Giuseppe & Bottani, Eleonora, 2018. "An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses," European Journal of Operational Research, Elsevier, vol. 267(1), pages 120-137.
    6. de Koster, M.B.M. & Le-Duc, T. & Roodbergen, K.J., 2006. "Design and Control of Warehouse Order Picking: a literature review," ERIM Report Series Research in Management ERS-2006-005-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Anderson Rogério Faia Pinto & Marcelo Seido Nagano, 2020. "Genetic algorithms applied to integration and optimization of billing and picking processes," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 641-659, March.
    8. Gu, Jinxiang & Goetschalckx, Marc & McGinnis, Leon F., 2007. "Research on warehouse operation: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 177(1), pages 1-21, February.
    9. Zapfel, Gunther & Wasner, Michael, 2006. "Warehouse sequencing in the steel supply chain as a generalized job shop model," International Journal of Production Economics, Elsevier, vol. 104(2), pages 482-501, December.
    10. Kovács, András, 2011. "Optimizing the storage assignment in a warehouse served by milkrun logistics," International Journal of Production Economics, Elsevier, vol. 133(1), pages 312-318, September.
    11. Le-Duc, T. & de Koster, M.B.M., 2004. "Travel Time Estimation and Order Barching in a 2-Block Warehouse," ERIM Report Series Research in Management ERS-2004-098-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    12. van Gils, Teun & Ramaekers, Katrien & Braekers, Kris & Depaire, Benoît & Caris, An, 2018. "Increasing order picking efficiency by integrating storage, batching, zone picking, and routing policy decisions," International Journal of Production Economics, Elsevier, vol. 197(C), pages 243-261.
    13. Li, Xiaowei & Hua, Guowei & Huang, Anqiang & Sheu, Jiuh-Biing & Cheng, T.C.E. & Huang, Fengquan, 2020. "Storage assignment policy with awareness of energy consumption in the Kiva mobile fulfilment system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    14. Rajabighamchi, Farzaneh & van Hoesel, Stan & Defryn, Christof, 2023. "Graph reduction for the planar Travelling Salesman Problem," Research Memorandum 004, Maastricht University, Graduate School of Business and Economics (GSBE).
    15. Hsieh, Ling-Feng & Huang, Yi-Chen, 2011. "New batch construction heuristics to optimise the performance of order picking systems," International Journal of Production Economics, Elsevier, vol. 131(2), pages 618-630, June.
    16. Lu, Wenrong & McFarlane, Duncan & Giannikas, Vaggelis & Zhang, Quan, 2016. "An algorithm for dynamic order-picking in warehouse operations," European Journal of Operational Research, Elsevier, vol. 248(1), pages 107-122.
    17. Çağla Cergibozan & A. Serdar Tasan, 2019. "Order batching operations: an overview of classification, solution techniques, and future research," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 335-349, January.
    18. Theys, Christophe & Bräysy, Olli & Dullaert, Wout & Raa, Birger, 2010. "Using a TSP heuristic for routing order pickers in warehouses," European Journal of Operational Research, Elsevier, vol. 200(3), pages 755-763, February.
    19. Laura Korbacher & Katrin Heßler & Stefan Irnich, 2023. "The Single Picker Routing Problem with Scattered Storage: Modeling and Evaluation of Routing and Storage Policies," Working Papers 2302, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    20. Gerhard Wäscher & André Scholz, 2015. "A Solution Approach for the Joint Order Batching and Picker Routing Problem in a Two-Block Layout," FEMM Working Papers 150004, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:219:y:2012:i:2:p:335-346. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.