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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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

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