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Pallet Distribution Affecting a Machine’s Utilization Level and Picking Time

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  • Taniya Mukherjee

    (Department of Mathematics & Statistics, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
    Administrative Science Department, College of Administrative and Financial Science, Gulf University, Sanad 26489, Bahrain)

  • Isha Sangal

    (Department of Mathematics & Statistics, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India)

  • Biswajit Sarkar

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul 03722, Republic of Korea
    Center for Transdisciplinary Research (CFTR), Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, Tamil Nadu, India)

  • Tamer M. Alkadash

    (Administrative Science Department, College of Administrative and Financial Science, Gulf University, Sanad 26489, Bahrain)

  • Qais Almaamari

    (Administrative Science Department, College of Administrative and Financial Science, Gulf University, Sanad 26489, Bahrain)

Abstract

Space and labor are the two internal resources within a warehouse or cross-dock center which seek attention. Meaningful efforts in optimizing these two resources can reduce the operational cost or time of the goods delivery. The timely allocation of resources to order picking not only reduces the makespan and operational time but can also evade delay. In decentralized settings, where all the information is not properly shared between the players of the supply chain, miscommunication results in delays in product delivery. In this study, efforts were made to determine the pallet quantity of different product types in an order quantify when there is a gap in information shared and, based on that, the allocation of material handling devices or pickers was conducted. Each handling device is bounded by a workload to eliminate the option of idle resources and ensure it is utilized properly. A mixed integer linear programming model was formulated for this study and was solved using Lingo. Numerical experiments were performed under varying resource numbers and pallet quantities to investigate the circumstances where the number of pallet types and allocation of machines have the highest benefit. The results confirm that a change in the pallet quantity of the products increases the total picking time. However, an increase in the number of handling devices minimizes the level of over-utilization of a particular machine.

Suggested Citation

  • Taniya Mukherjee & Isha Sangal & Biswajit Sarkar & Tamer M. Alkadash & Qais Almaamari, 2023. "Pallet Distribution Affecting a Machine’s Utilization Level and Picking Time," Mathematics, MDPI, vol. 11(13), pages 1-17, July.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2956-:d:1185440
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    References listed on IDEAS

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    1. Robin Hanson & Lars Medbo & Majeed Assaf & Patrik Jukic, 2018. "Time efficiency and physical workload in manual picking from large containers," International Journal of Production Research, Taylor & Francis Journals, vol. 56(3), pages 1109-1117, February.
    2. Y Li & A Lim & B Rodrigues, 2004. "Crossdocking—JIT scheduling with time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1342-1351, December.
    3. van Gils, Teun & Caris, An & Ramaekers, Katrien & Braekers, Kris, 2019. "Formulating and solving the integrated batching, routing, and picker scheduling problem in a real-life spare parts warehouse," European Journal of Operational Research, Elsevier, vol. 277(3), pages 814-830.
    4. 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.
    5. Zijian He & Vaneet Aggarwal & Shimon Y. Nof, 2018. "Differentiated service policy in smart warehouse automation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(22), pages 6956-6970, November.
    6. Zhang, Minqi & Grosse, Eric H. & Glock, Christoph H., 2023. "Ergonomic and economic evaluation of a collaborative hybrid order picking system," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 136174, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    7. Raj Kumar Bachar & Shaktipada Bhuniya & Santanu Kumar Ghosh & Biswajit Sarkar, 2022. "Controllable Energy Consumption in a Sustainable Smart Manufacturing Model Considering Superior Service, Flexible Demand, and Partial Outsourcing," Mathematics, MDPI, vol. 10(23), pages 1-29, November.
    8. Sarkar, Mitali & Dey, Bikash Koli & Ganguly, Baishakhi & Saxena, Neha & Yadav, Dharmendra & Sarkar, Biswajit, 2023. "The impact of information sharing and bullwhip effects on improving consumer services in dual-channel retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    9. Sarkar, Biswajit & Kar, Sumi & Basu, Kajla & Seo, Yong Won, 2023. "Is the online-offline buy-online-pickup-in-store retail strategy best among other product delivery strategies under variable lead time?," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    10. Hermel, Dror & Hasheminia, Hamed & Adler, Nicole & Fry, Michael J., 2016. "A solution framework for the multi-mode resource-constrained cross-dock scheduling problem," Omega, Elsevier, vol. 59(PB), pages 157-170.
    11. Cleophas, Catherine & Cottrill, Caitlin & Ehmke, Jan Fabian & Tierney, Kevin, 2019. "Collaborative urban transportation: Recent advances in theory and practice," European Journal of Operational Research, Elsevier, vol. 273(3), pages 801-816.
    12. G A Álvarez-Pérez & J L González-Velarde & J W Fowler, 2009. "Crossdocking— Just in Time scheduling: an alternative solution approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(4), pages 554-564, April.
    13. 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.
    14. Jamili, Negin & van den Berg, Pieter L. & de Koster, René, 2022. "Quantifying the impact of sharing resources in a collaborative warehouse," European Journal of Operational Research, Elsevier, vol. 302(2), pages 518-529.
    15. Srinivas, Sharan & Yu, Shitao, 2022. "Collaborative order picking with multiple pickers and robots: Integrated approach for order batching, sequencing and picker-robot routing," International Journal of Production Economics, Elsevier, vol. 254(C).
    16. Zhang, Minqi & Grosse, Eric H. & Glock, Christoph H., 2023. "Ergonomic and economic evaluation of a collaborative hybrid order picking system," International Journal of Production Economics, Elsevier, vol. 258(C).
    17. 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).
    18. Fonseca, Gabriela B. & Nogueira, Thiago H. & Ravetti, Martín Gómez, 2019. "A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 275(1), pages 139-154.
    19. Guajardo, Mario & Rönnqvist, Mikael & Flisberg, Patrik & Frisk, Mikael, 2018. "Collaborative transportation with overlapping coalitions," European Journal of Operational Research, Elsevier, vol. 271(1), pages 238-249.
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