IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i4p2021-d498797.html
   My bibliography  Save this article

A New Sustainable Warehouse Management Approach for Workforce and Activities Scheduling

Author

Listed:
  • Vlado Popović

    (Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia)

  • Milorad Kilibarda

    (Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia)

  • Milan Andrejić

    (Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia)

  • Borut Jereb

    (Faculty of Logistics, University of Maribor, Mariborska Cesta 7, 3000 Celje, Slovenia)

  • Dejan Dragan

    (Faculty of Logistics, University of Maribor, Mariborska Cesta 7, 3000 Celje, Slovenia)

Abstract

Sustainable engineering is very important for logistics systems. Nowadays, sustainable warehouse management is a key factor in market success. Workforce fluctuation and inverting the number of customers’ demands make a lot of problems in distribution warehouses. This study addresses a sustainable approach for the workforce scheduling problem recognized in a real distribution warehouse. The problem arises from the high variability of demand for workers over one workday, which causes workforce surplus in some periods of the workday and shortages in others. Engineering managers of the distribution warehouse already use different full-time and part-time shifts, and schedule workers on different activities, but they still have significant workforce surpluses or shortages in some periods. This study proposes the scheduling of activities’ execution together with workers to face that variability and decrease the cost of the workforce. This idea comes from the fact that some activities in a distribution warehouse can be done in a specific time period after the need for them occurs. In this way, the variability of demand for workers can be decreased, and a lower workforce cost may be ensured. Based on this idea, the entire problem is modeled as integer linear programming. The real example of the problem is solved, and the proposed model is tested on randomly generated instances of the problem in Python by means of the PuLP linear programming package. The results indicate different positive effects in the manner of sustainable warehouse management: lower workforce costs, time savings, better utilization of all types of resources and equipment, increased employee satisfaction, and so on. For even 61% of instances of the introduced problem, the obtained cost of the workforce is lower by more than 20% if activities’ executions are scheduled together with employees.

Suggested Citation

  • Vlado Popović & Milorad Kilibarda & Milan Andrejić & Borut Jereb & Dejan Dragan, 2021. "A New Sustainable Warehouse Management Approach for Workforce and Activities Scheduling," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:2021-:d:498797
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/4/2021/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/4/2021/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jonathan F. Bard & Lin Wan, 2008. "Workforce Design with Movement Restrictions Between Workstation Groups," Manufacturing & Service Operations Management, INFORMS, vol. 10(1), pages 24-42, November.
    2. Natallia Pashkevich & Darek Haftor & Mikael Karlsson & Soumitra Chowdhury, 2019. "Sustainability through the Digitalization of Industrial Machines: Complementary Factors of Fuel Consumption and Productivity for Forklifts with Sensors," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    3. Dekker, Rommert & Bloemhof, Jacqueline & Mallidis, Ioannis, 2012. "Operations Research for green logistics – An overview of aspects, issues, contributions and challenges," European Journal of Operational Research, Elsevier, vol. 219(3), pages 671-679.
    4. Van den Bergh, Jorne & Beliën, Jeroen & De Bruecker, Philippe & Demeulemeester, Erik & De Boeck, Liesje, 2013. "Personnel scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 226(3), pages 367-385.
    5. Marc A. Rosen, 2012. "Engineering Sustainability: A Technical Approach to Sustainability," Sustainability, MDPI, vol. 4(9), pages 1-23, September.
    6. A.T. Ernst & H. Jiang & M. Krishnamoorthy & B. Owens & D. Sier, 2004. "An Annotated Bibliography of Personnel Scheduling and Rostering," Annals of Operations Research, Springer, vol. 127(1), pages 21-144, March.
    7. Billionnet, Alain, 1999. "Integer programming to schedule a hierarchical workforce with variable demands," European Journal of Operational Research, Elsevier, vol. 114(1), pages 105-114, April.
    8. Raffaele Carli & Mariagrazia Dotoli & Salvatore Digiesi & Francesco Facchini & Giorgio Mossa, 2020. "Sustainable Scheduling of Material Handling Activities in Labor-Intensive Warehouses: A Decision and Control Model," Sustainability, MDPI, vol. 12(8), pages 1-25, April.
    9. Ernst, A. T. & Jiang, H. & Krishnamoorthy, M. & Sier, D., 2004. "Staff scheduling and rostering: A review of applications, methods and models," European Journal of Operational Research, Elsevier, vol. 153(1), pages 3-27, February.
    10. Bartolini, M. & Bottani, E. & Grosse, E. H., 2019. "Green warehousing: systematic literature review and bibliometric analysis," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 112369, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    11. Ladier, Anne-Laure & Alpan, Gülgün & Penz, Bernard, 2014. "Joint employee weekly timetabling and daily rostering: A decision-support tool for a logistics platform," European Journal of Operational Research, Elsevier, vol. 234(1), pages 278-291.
    12. Larry P. Ritzman & LeRoy J. Krajewski & Michael J. Showalter, 1976. "The Disaggregation of Aggregate Manpower Plans," Management Science, INFORMS, vol. 22(11), pages 1204-1214, July.
    13. Ene, Seval & Küçükoğlu, İlker & Aksoy, Aslı & Öztürk, Nursel, 2016. "A genetic algorithm for minimizing energy consumption in warehouses," Energy, Elsevier, vol. 114(C), pages 973-980.
    14. Restrepo, María I. & Lozano, Leonardo & Medaglia, Andrés L., 2012. "Constrained network-based column generation for the multi-activity shift scheduling problem," International Journal of Production Economics, Elsevier, vol. 140(1), pages 466-472.
    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. Milan Andrejić & Milorad Kilibarda & Vukašin Pajić, 2022. "Job Satisfaction and Labor Fluctuation: A Case Study in the Logistics Sector in Serbia," Logistics, MDPI, vol. 6(3), pages 1-15, July.
    2. Daria Minashkina & Ari Happonen, 2023. "Warehouse Management Systems for Social and Environmental Sustainability: A Systematic Literature Review and Bibliometric Analysis," Logistics, MDPI, vol. 7(3), pages 1-33, July.
    3. Péter Dobos & Ákos Cservenák & Róbert Skapinyecz & Béla Illés & Péter Tamás, 2021. "Development of an Industry 4.0-Based Analytical Method for the Value Stream Centered Optimization of Demand-Driven Warehousing Systems," Sustainability, MDPI, vol. 13(21), pages 1-33, October.
    4. Xuan Zhang & Tiantian Mo & Yougong Zhang, 2023. "Optimization of Storage Location Assignment for Non-Traditional Layout Warehouses Based on the Firework Algorithm," Sustainability, MDPI, vol. 15(13), pages 1-21, June.

    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. Restrepo, María I. & Gendron, Bernard & Rousseau, Louis-Martin, 2017. "A two-stage stochastic programming approach for multi-activity tour scheduling," European Journal of Operational Research, Elsevier, vol. 262(2), pages 620-635.
    2. Lin, Shih-Wei & Ying, Kuo-Ching, 2014. "Minimizing shifts for personnel task scheduling problems: A three-phase algorithm," European Journal of Operational Research, Elsevier, vol. 237(1), pages 323-334.
    3. Jaime Miranda & Pablo A. Rey & Antoine Sauré & Richard Weber, 2018. "Metro Uses a Simulation-Optimization Approach to Improve Fare-Collection Shift Scheduling," Interfaces, INFORMS, vol. 48(6), pages 529-542, November.
    4. Van den Bergh, Jorne & Beliën, Jeroen & De Bruecker, Philippe & Demeulemeester, Erik & De Boeck, Liesje, 2013. "Personnel scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 226(3), pages 367-385.
    5. Hans Corsten & Ferdinand Becker & Hagen Salewski, 2020. "Integrating truck and workforce scheduling in a cross-dock: analysis of different workforce coordination policies," Journal of Business Economics, Springer, vol. 90(2), pages 207-237, March.
    6. David Rea & Craig Froehle & Suzanne Masterson & Brian Stettler & Gregory Fermann & Arthur Pancioli, 2021. "Unequal but Fair: Incorporating Distributive Justice in Operational Allocation Models," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2304-2320, July.
    7. Arpan Rijal & Marco Bijvank & Asvin Goel & René de Koster, 2021. "Workforce Scheduling with Order-Picking Assignments in Distribution Facilities," Transportation Science, INFORMS, vol. 55(3), pages 725-746, May.
    8. Ferdinand Kiermaier & Markus Frey & Jonathan F. Bard, 2020. "The flexible break assignment problem for large tour scheduling problems with an application to airport ground handlers," Journal of Scheduling, Springer, vol. 23(2), pages 177-209, April.
    9. Young-Chae Hong & Amy Cohn & Stephen Gorga & Edmond O’Brien & William Pozehl & Jennifer Zank, 2019. "Using Optimization Techniques and Multidisciplinary Collaboration to Solve a Challenging Real-World Residency Scheduling Problem," Interfaces, INFORMS, vol. 49(3), pages 201-212, May.
    10. Lotfi Hidri & Achraf Gazdar & Mohammed M. Mabkhot, 2020. "Optimized Procedure to Schedule Physicians in an Intensive Care Unit: A Case Study," Mathematics, MDPI, vol. 8(11), pages 1-24, November.
    11. Damcı-Kurt, Pelin & Zhang, Minjiao & Marentay, Brian & Govind, Nirmal, 2019. "Improving physician schedules by leveraging equalization: Cases from hospitals in U.S," Omega, Elsevier, vol. 85(C), pages 182-193.
    12. Banu Sungur & Cemal Özgüven & Yasemin Kariper, 2017. "Shift scheduling with break windows, ideal break periods, and ideal waiting times," Flexible Services and Manufacturing Journal, Springer, vol. 29(2), pages 203-222, June.
    13. Jonathan F. Bard & Lin Wan, 2008. "Workforce Design with Movement Restrictions Between Workstation Groups," Manufacturing & Service Operations Management, INFORMS, vol. 10(1), pages 24-42, November.
    14. De Bruecker, Philippe & Beliën, Jeroen & Van den Bergh, Jorne & Demeulemeester, Erik, 2018. "A three-stage mixed integer programming approach for optimizing the skill mix and training schedules for aircraft maintenance," European Journal of Operational Research, Elsevier, vol. 267(2), pages 439-452.
    15. Suk Ho Jin & Ho Yeong Yun & Suk Jae Jeong & Kyung Sup Kim, 2017. "Hybrid and Cooperative Strategies Using Harmony Search and Artificial Immune Systems for Solving the Nurse Rostering Problem," Sustainability, MDPI, vol. 9(7), pages 1-19, June.
    16. Ladier, Anne-Laure & Alpan, Gülgün & Penz, Bernard, 2014. "Joint employee weekly timetabling and daily rostering: A decision-support tool for a logistics platform," European Journal of Operational Research, Elsevier, vol. 234(1), pages 278-291.
    17. Paola Cappanera & Filippo Visintin & Roberta Rossi, 2022. "The emergency department physician rostering problem: obtaining equitable solutions via network optimization," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 916-959, December.
    18. Lusby, Richard Martin & Range, Troels Martin & Larsen, Jesper, 2015. "A Benders decomposition-based Matheuristic for the Cardinality Constrained Shift Design Problem," Discussion Papers on Economics 9/2015, University of Southern Denmark, Department of Economics.
    19. Chapados, Nicolas & Joliveau, Marc & L’Ecuyer, Pierre & Rousseau, Louis-Martin, 2014. "Retail store scheduling for profit," European Journal of Operational Research, Elsevier, vol. 239(3), pages 609-624.
    20. Lusby, Richard Martin & Range, Troels Martin & Larsen, Jesper, 2016. "A Benders decomposition-based matheuristic for the Cardinality Constrained Shift Design Problem," European Journal of Operational Research, Elsevier, vol. 254(2), pages 385-397.

    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:13:y:2021:i:4:p:2021-:d:498797. 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.