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Planning and modeling of the time for acceptance and stay of vehicles at the loading and discharging points

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EN
Abstrakty
EN
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
Strony
23--34
Opis fizyczny
Bibliogr. 21 poz.
Twórcy
autor
  • University of Ruse “Angel Kanchev” 8 Studentska, 7017 Ruse, Bulgaria
  • University of Ruse “Angel Kanchev” 8 Studentska, 7017 Ruse, Bulgaria
  • University of Ruse “Angel Kanchev” 8 Studentska, 7017 Ruse, Bulgaria
Bibliografia
  • 1. Bhunia, A.K. & Sahoo, L. & Shaikh, A.A. Advanced Optimization and Operations Research. Singapore: Springer. 2019. ISBN 978-981-329-967-2.
  • 2. Cominetti, R. & Facchinei, F. & Lasserre, J.B. Modern Optimization Modelling Techniques. Springer Science & Business Media. 2012. ISBN 978-3-0348-0291-8.
  • 3. Dimov, I.А. & Georgieva, R. & Todorov, V. Balancing of systematic and stochastic errors in Monte Carlo Algorithms for integral equations. 2015. In: Dimov, I. & Fidanova, S. & Lirkov, I. (eds.) Numerical Methods and Applications. NMA 2014. Lecture Notes in Computer Science. 2014. Vol. 8962. Cham: Springer.
  • 4. Kallrath, В.J. Business Optimization Using Mathematical Programming. Springer International Publishing. 2021. P. 633. ISBN 978-3-030-73237-0.
  • 5. Симеонов, Д. & Пенчева, В. Взаимодействие между видовете транспорт. Печатна база при РУ „Ангел Кънчев“. Русе, България, 2001. 308 c. [In Bulgarian: Simeonov, D. & Pencheva, V. Interaction between modes of transport. „Angel Kanchev” Ruse University Printing Base. Ruse, Bulgaria. 2001. 308 p.].
  • 6. Таха, H. Operation Research. An Introduction. University of Arcansas. Prentice Hill. 1997. ISBN 0-13-272915.
  • 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.
  • 8. Boyce, W.S. Does vehicle driver health and wellness deserve more attention? Journal of Transport & Health. 2016. Vol. 3. No. 1. P. 124-128. Available at: https://www.sciencedirect.com/science/article/pii/S2214140516000116.
  • 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.
  • 12. Goel, R. & Maini, R. Vehicle routing problem and its solution methodologies: a survey. Int. J. Logistics Systems and Management. 2017. Vol. 28. No. 4. P. 419-435. Available at: https://www.researchgate.net/publication/344245589_Vehicle_routing_problem_and_its_solution _methodologies_a_survey.
  • 13. Helmkamp, J.C. & Lincoln, J.E. & Sestito, J. & Wood, E. & Birdsey, J. & Kiefer, M. Risk factors, health behaviors, and injury among adults employed in the transportation, warehousing, and utilities super sector. American Journal of Industrial Medicine. 2013. Vol. 56. No. 5. P. 556-568.
  • 14. Huynh, N. & Walton, C.M. Robust scheduling of truck arrivals at marine container terminals. Journal of Transportation Engineering. 2008. Vol. 134. No. 8. P. 347-353.
  • 15. Lin, B.K. & Cheng, W.M. Performance analysis of in-out system of railway container terminal based on fuzzy queuing model. Advanced Materials Research. 2012. Vol. 543. P. 324-327.
  • 16. Moore, A. Innovative scenarios for modelling intra-city freight delivery. Transportation Research Interdisciplinary Perspectives. 2019. Vol. 3(100024). P. 1-7. Available at: https://www.sciencedirect.com/science/article/pii/S2590198219300247.
  • 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.
  • 20. Caliper. TransCAD Transportation Planning Software. Available at: https://www.caliper.com/tcovu.htm.
  • 21. Web page of the Mathworks. Available at: https://www.mathworks.com/products/optimization.html.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d0ffb923-0ebd-4ea6-a433-8af07741e4a2
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