IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i23p9818-d450296.html
   My bibliography  Save this article

The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

Author

Listed:
  • Gabriel Fedorko

    (Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Košice, Letná 9, 042 00 Košice, Slovakia)

  • Vieroslav Molnár

    (Faculty of Manufacturing Technologies, Technical University of Košice, Bayerova 1, 080 01 Prešov, Slovakia vieroslav.molnar@tuke.sk)

  • Nikoleta Mikušová

    (Faculty of Mining, Ecology, Process Control and Geotechnologies, Technical University of Košice, Letná 9, 042 00 Košice, Slovakia)

Abstract

This paper examines the use of computer simulation methods to streamline the process of picking materials within warehouse logistics. The article describes the use of a genetic algorithm to optimize the storage of materials in shelving positions, in accordance with the method of High-Runner Strategy. The goal is to minimize the time needed for picking. The presented procedure enables the creation of a software tool in the form of an optimization model that can be used for the needs of the optimization of warehouse logistics processes within various types of production processes. There is a defined optimization problem in the form of a resistance function, which is of general validity. The optimization is represented using the example of 400 types of material items in 34 categories, stored in six rack rows. Using a simulation model, a comparison of a normal and an optimized state is realized, while a time saving of 48 min 36 s is achieved. The mentioned saving was achieved within one working day. However, the application of an approach based on the use of optimization using a genetic algorithm is not limited by the number of material items or the number of categories and shelves. The acquired knowledge demonstrates the application possibilities of the genetic algorithm method, even for the lowest levels of enterprise logistics, where the application of this approach is not yet a matter of course but, rather, a rarity.

Suggested Citation

  • Gabriel Fedorko & Vieroslav Molnár & Nikoleta Mikušová, 2020. "The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics," Sustainability, MDPI, vol. 12(23), pages 1-17, November.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:9818-:d:450296
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/23/9818/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/23/9818/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brynzer, H. & Johansson, M. I., 1996. "Storage location assignment: Using the product structure to reduce order picking times," International Journal of Production Economics, Elsevier, vol. 46(1), pages 595-603, December.
    2. Milosav Georgijevic & Sanja Bojic & Dejan Brcanov, 2013. "The location of public logistic centers: an expanded capacity-limited fixed cost location-allocation modeling approach," Transportation Planning and Technology, Taylor & Francis Journals, vol. 36(2), pages 218-229, April.
    3. Diefenbach, Heiko & Glock, C. H., 2019. "Ergonomic and economic optimization of layout and item assignment of a U-shaped order picking zone," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 117196, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Ashayeri, J. & Gelders, L. F., 1985. "Warehouse design optimization," European Journal of Operational Research, Elsevier, vol. 21(3), pages 285-294, September.
    5. 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.
    6. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
    7. Baker, Peter & Canessa, Marco, 2009. "Warehouse design: A structured approach," European Journal of Operational Research, Elsevier, vol. 193(2), pages 425-436, March.
    8. Karaenke, Paul & Bichler, Martin & Merting, Soeren & Minner, Stefan, 2020. "Non-monetary coordination mechanisms for time slot allocation in warehouse delivery," European Journal of Operational Research, Elsevier, vol. 286(3), pages 897-907.
    9. Diefenbach, Heiko & Glock, C. H., 2019. "Ergonomic and economic optimization of layout and item assignment of a U-shaped order picking zone," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 116991, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    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. Kateryna Czerniachowska & Radosław Wichniarek & Krzysztof Żywicki, 2023. "A Model for an Order-Picking Problem with a One-Directional Conveyor and Buffer," Sustainability, MDPI, vol. 15(18), pages 1-18, September.

    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. Loske, Dominic & Klumpp, Matthias & Grosse, Eric H. & Modica, Tiziana & Glock, Christoph H., 2023. "Storage systems’ impact on order picking time: An empirical economic analysis of flow-rack storage systems," International Journal of Production Economics, Elsevier, vol. 261(C).
    2. Vasiliki Kapou & Stavros T. Ponis & George Plakas & Eleni Aretoulaki, 2022. "An Innovative Layout Design and Storage Assignment Method for Manual Order Picking with Respect to Ergonomic Criteria," Logistics, MDPI, vol. 6(4), pages 1-21, December.
    3. 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.
    4. Li Zhou & Huwei Liu & Junhui Zhao & Fan Wang & Jianglong Yang, 2022. "Performance Analysis of Picking Routing Strategies in the Leaf Layout Warehouse," Mathematics, MDPI, vol. 10(17), pages 1-28, September.
    5. 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.
    6. 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.
    7. 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.
    8. Heiko Diefenbach & Simon Emde & Christoph H. Glock & Eric H. Grosse, 2022. "New solution procedures for the order picker routing problem in U-shaped pick areas with a movable depot," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(2), pages 535-573, June.
    9. Maria A. M. Trindade & Paulo S. A. Sousa & Maria R. A. Moreira, 2021. "Defining a storage-assignment strategy for precedence-constrained order picking," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(2), pages 146-160.
    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. A. Scholz & G. Wäscher, 2017. "Order Batching and Picker Routing in manual order picking systems: the benefits of integrated routing," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 491-520, June.
    12. Janka Saderova & Andrea Rosova & Marian Sofranko & Peter Kacmary, 2021. "Example of Warehouse System Design Based on the Principle of Logistics," Sustainability, MDPI, vol. 13(8), pages 1-16, April.
    13. Rafael Diaz, 2016. "Using dynamic demand information and zoning for the storage of non-uniform density stock keeping units," International Journal of Production Research, Taylor & Francis Journals, vol. 54(8), pages 2487-2498, April.
    14. K. L. Choy & G. T. S. Ho & C. K. H. Lee, 2017. "A RFID-based storage assignment system for enhancing the efficiency of order picking," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 111-129, January.
    15. Manuel Ostermeier & Andreas Holzapfel & Heinrich Kuhn & Daniel Schubert, 2022. "Integrated zone picking and vehicle routing operations with restricted intermediate storage," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(3), pages 795-832, September.
    16. Derhami, Shahab & Smith, Jeffrey S. & Gue, Kevin R., 2020. "A simulation-based optimization approach to design optimal layouts for block stacking warehouses," International Journal of Production Economics, Elsevier, vol. 223(C).
    17. 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.
    18. Henn, Sebastian & Wäscher, Gerhard, 2012. "Tabu search heuristics for the order batching problem in manual order picking systems," European Journal of Operational Research, Elsevier, vol. 222(3), pages 484-494.
    19. Fangyu Chen & Yongchang Wei & Hongwei Wang, 2018. "A heuristic based batching and assigning method for online customer orders," Flexible Services and Manufacturing Journal, Springer, vol. 30(4), pages 640-685, December.
    20. Matusiak, Marek & de Koster, René & Saarinen, Jari, 2017. "Utilizing individual picker skills to improve order batching in a warehouse," European Journal of Operational Research, Elsevier, vol. 263(3), pages 888-899.

    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:gam:jsusta:v:12:y:2020:i:23:p:9818-:d:450296. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.