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Just-In-Time Vehicle Routing for In-House Part Feeding to Assembly Lines

Author

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  • Simon Emde

    (Technische Universität Darmstadt, 64289 Darmstadt, Germany)

  • Michael Schneider

    (RWTH Aachen University, 52072 Aachen, Germany)

Abstract

This paper deals with the problem of routing in-house transport vehicles that feed parts to workstations in assembly plants or workshops just in time. The capacitated vehicles, typically so-called tow trains, perform their assigned route cyclically without break and provide each station with the exact quantity of parts required until the next arrival of the vehicle. Hence, the demand of each station depends on the duration of the route serving the station: The longer the route duration, the less frequently the station is visited and the higher its demand. The goal is to minimize first the number of vehicles and second the total route duration, while respecting given minimum service frequencies at the stations. We provide a mathematical formulation of this novel problem and address it by means of a large neighborhood search. The algorithm is able to solve realistic instances in acceptable time and vastly outperforms a default solver. We discuss two variants of the problem, one in which split deliveries to stations are allowed and another assuming that all stations lie on a straight line. Finally, we investigate the extent to which assuming constant demand rates may lead to problems during the day-to-day operations of the part-feeding system, where demands are not necessarily constant.

Suggested Citation

  • Simon Emde & Michael Schneider, 2018. "Just-In-Time Vehicle Routing for In-House Part Feeding to Assembly Lines," Transportation Science, INFORMS, vol. 52(3), pages 657-672, June.
  • Handle: RePEc:inm:ortrsc:v:52:y:2018:i:3:p:657-672
    DOI: 10.1287/trsc.2018.0824
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    References listed on IDEAS

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    2. Emilio Moretti & Elena Tappia & Veronique Limère & Marco Melacini, 2021. "Exploring the application of machine learning to the assembly line feeding problem," Operations Management Research, Springer, vol. 14(3), pages 403-419, December.
    3. Masood Fathi & Morteza Ghobakhloo, 2020. "Enabling Mass Customization and Manufacturing Sustainability in Industry 4.0 Context: A Novel Heuristic Algorithm for in-Plant Material Supply Optimization," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    4. Bingtao Quan & Sujian Li & Kuo-Jui Wu, 2022. "Optimizing the Vehicle Scheduling Problem for Just-in-Time Delivery Considering Carbon Emissions and Atmospheric Particulate Matter," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    5. Diefenbach, Heiko & Emde, Simon & Glock, Christoph H., 2023. "Multi-depot electric vehicle scheduling in in-plant production logistics considering non-linear charging models," European Journal of Operational Research, Elsevier, vol. 306(2), pages 828-848.
    6. Emilio Moretti & Elena Tappia & Martina Mauri & Marco Melacini, 2022. "A performance model for mobile robot-based part feeding systems to supermarkets," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 580-613, September.
    7. Emde, Simon & Tahirov, Nail & Gendreau, Michel & Glock, Christoph H., 2021. "Routing automated lane-guided transport vehicles in a warehouse handling returns," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1085-1098.
    8. Diefenbach, Heiko & Emde, Simon & Glock, Christoph H., 2020. "Loading tow trains ergonomically for just-in-time part supply," European Journal of Operational Research, Elsevier, vol. 284(1), pages 325-344.
    9. Baals, Julian & Emde, Simon & Turkensteen, Marcel, 2023. "Minimizing earliness-tardiness costs in supplier networks—A just-in-time truck routing problem," European Journal of Operational Research, Elsevier, vol. 306(2), pages 707-741.
    10. Christos Orlis & Nicola Bianchessi & Roberto Roberti & Wout Dullaert, 2020. "The Team Orienteering Problem with Overlaps: An Application in Cash Logistics," Transportation Science, INFORMS, vol. 54(2), pages 470-487, March.

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