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When delivering goods in the warehouses of enterprises, courier and forwarding companies, and for logistics operators, loading and unloading is usually done manually or mechanically. On the other hand, the load can first be placed on the ground next to the vehicle and then accepted in the pile, or a ramp can be used so that it can be delivered directly to the warehouse or vice versa. When there is a ramp, the loading and discharging activity is performed faster and it is much easier. When there are many vehicles serviced on ramps, it is necessary to have a free ramp available. This is often not the case when the warehouse has more ramps and a large exchange of goods. In this case, a time schedule is usually made for the reception and handling of vehicles, which is communicated to carriers and drivers so that there is no unnecessary downtime of vehicles and overloading of points with ramps. There are cases in which the established organization of work cannot be performed due to various force majeure or other reasons, such as delays at border crossings, bans on passing through certain sections, change in the working hours of warehouses, pandemic and other reasons. The vehicles then arrive at the checkpoints at a time that is different from their schedule and have to wait to be serviced. Waiting at the unloading points makes drivers nervous and they become dissatisfied with the working conditions. In this respect, a solution has been proposed based on the working hours and occupancy of the loading and discharging point and the time of arrival of the vehicles at the point, and how to receive the vehicles so that the waiting time between them is the shortest. For this purpose, a partially integer linear optimization model has been created in Matlab, which provides a valid plan with the shortest waiting times for all vehicles. Simulations have been made for different numbers of ramps and vehicles. The results show that the model is suitable for pre-creating a valid plan for the operation of the vehicle warehouse, if any, with a minimum waiting time.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
23--34
Opis fizyczny
Bibliogr. 21 poz.
Twórcy
autor
- University of Ruse “Angel Kanchev” 8 Studentska, 7017 Ruse, Bulgaria
autor
- University of Ruse “Angel Kanchev” 8 Studentska, 7017 Ruse, Bulgaria
autor
- University of Ruse “Angel Kanchev” 8 Studentska, 7017 Ruse, Bulgaria
Bibliografia
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- 7. Baldacci, R. & Mingozzi, A. & Roberti, R. Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints. European Journal of Operational Research. 2012. Vol. 218. No. 1. P. 1-6. Available at: https://backend.orbit.dtu.dk/ws/portalfiles/portal/102382394/Recent_exact_algorithms_for_solving_the_vehicle_routing_problem_under_capacity_and_time_window_constraints.pdf.
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- 9. Calabrò, G. & Torrisi, V. & Inturri, G. & Ignaccolo, M. Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization. European Transport Research Review. 2020. Vol. 12. No. 21. Available at: https://etrr.springeropen.com/articles/10.1186/s12544-020-00409-7.
- 10. Chen, G.X. & Sieber, W.K. & Lincoln, J.E. & Birdsey, J. & Hitchcock, E.M. & Nakata, A. & Robinson, C.F. & Collins, J.W. & Sweeney, M.H. NIOSH national survey of long-haul vehicle drivers: Injury and safety. Accident Analysis and Prevention. 2015. No 85(0001-4575). P. 66-72, Available at: https://www.sciencedirect.com/science/article/pii/S0001457515300580?via%3Dihub.
- 11. Friswell, R. & Williamson, A. Management of heavy vehicle driver queuing and waiting for loading and discharging at road transport depots. Safety Science. 2019. Vol. 120, P. 194-205. Available at: https://www.sciencedirect.com/science/article/pii/S0925753518320708.
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- 17. Muñoz Hernández, H. & Parodi Camaño, T.A. & Soto de la Vega, D.A. & López Pereira, J.M. Metaheuristics applied to the fleet size and mix vehicle routing problems with soft time windows and stochastic times. 6th International Conference on Advanced Engineering Theory and Applications. 2019. LNEE. P. 649-659.
- 18. Williamson, A. & Lombardi, D.A. & Folkard, S. & Stutts, J. & Courtney, T.K. & Connor J.L. The link between fatigue and safety. Accident Analysis and Prevention. 2011. No 43. P. 498-515. Available at: https://www.sciencedirect.com/science/article/pii/S0001457509003121.
- 19. Todorov, Б. & Dimov, V.I. & Dimitrov, Y. Efficient quasi-Monte Carlo methods for multiple integrals in option pricing. AIP Conference Proceedings. 2018. Vol. 2025. No. 1. AIP Publishing LLC.
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Bibliografia
Identyfikator YADDA
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