PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Forming delivery routes while processing the stochastic flow of requests for forwarding services

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Formation of rational delivery routes is the main way to increase the effectiveness of client services for freight forwarding companies. The main part of the requests for transport services comes from occasional (non-constant) clients. The set of those requests forms a stochastic flow. For stochastic requests flow, the delivery routes are formed in the process of the requests receipt. Therefore, the standard approaches for merging of requests from multiple clients into routes, based on linear programming techniques, cannot be used in such conditions. An algorithm of formation under the stochastic demand conditions of such delivery routes, which allow servicing of two or more shippers, is proposed in the paper. The author has developed a specialized software to support decisions made by dispatchers of forwarding companies.
Czasopismo
Rocznik
Strony
73--82
Opis fizyczny
Bibliogr. 21 poz.
Twórcy
autor
  • Cracow University of Technology Warszawska 24, 31-155 Krakow, Poland
Bibliografia
  • 1. Simchi-Levi, D. & Chen, X. & Bramel, J. The logic of logistics: theory, algorithms, and applications for logistics and supply chain management. New York: Springer Science. 2005. 355 p.
  • 2. Кожин, А.П. & Мезенцев, В.Н. Математические методы в планировании и управлении грузовыми автомобильными перевозками. Москва. Транспорт. 1994. 304 p. [In Russian: Kozhin, A.P. & Mezentsev, V.N. Mathematical methods in planning and management of road freight transport. Moscow. Transport].
  • 3. Bovy, P.H.L. On modelling route choice sets in transportation networks: A synthesis. Transport Reviews. 2009. Vol. 29(1). P. 43-68.
  • 4. Hasani-Goodarzi, A. & Tavakkoli-Moghaddam, R. Capacitated vehicle routing problem for multi-product crossdocking with split deliveries and pickups. Procedia – Social and Behavioral Sciences. 2012. Vol. 62. P. 1360-1365.
  • 5. Wedyan, A.F. & Narayanan, A. Solving capacitated vehicle routing problem using intelligent water drops algorithm. In: IEEE International Conference on Natural Computation. 2014. P. 469-474.
  • 6. El-Sherbeny, N.A. Vehicle routing with time windows: An overview of exact, heuristic and metaheuristic methods. Journal of King Saud University (Science). 2010. Vol. 22. P. 123-131.
  • 7. Doerner, K. & Fuellerer, G. & Gronalt, M. & Hartl, R. & Iori, M. Metaheuristics for vehicle routing problems with loading constraints. Networks. 2007. Vol. 49. P. 294-307.
  • 8. Fuellerer, G. & Doerner, K. & Hartl, R. & Iori, M. Ant colony optimization for the two-dimensional loading vehicle routing problem. Computational Operation Research. 2009. Vol. 36. P. 655–673.
  • 9. Bertsimas, D. & Simchi-Levi, D. A new generation of vehicle routing problem: robust algorithms, addressing uncertainty. Operations Research. 1996. Vol. 44(2). P. 286-304.
  • 10. Barbucha, D. & Czarnowski, P. Multi-agent platform for solving the dynamic vehicle routing problem. IEEE Conference on Intelligent Transportation Systems. 2008. P. 517-522.
  • 11. Rancourt, M.-E. & Cordeau, J.-F. & Laporte, G. Long-haul vehicle routing and scheduling with working hour rules. Transportation Science. 2013. Vol. 47(1). P. 81–107.
  • 12. Stenger, A. & Vigo, D. & Enz, S. & Schwind, M. An adaptive variable neighborhood search algorithm for a vehicle routing problem arising in small package shipping. Transportation Science. 2013. Vol. 47(1). P. 64-80.
  • 13. Dell’Amico, M. & Falavigna, S. & Iori, M. Optimization of a real-world auto-carrier transportation problem. Transportation Science. 2014. Vol. 49(2). P. 402–419.
  • 14. Liñán-García, E. & Cruz-Villegas, L.C. & González-González, D.S. Solving the capacitated vehicle routing problem with stochastic demands applying the simulated annealing algorithm. Programación Matemática y Software. 2014. Vol. 6(2). P. 36-45.
  • 15. Laporte, G. & Louveaux, F.V. & van Hamme, L. An integer L-shaped algorithm for the capacitated vehicle routing problem with stochastic demands. Operations Research. 2002. Vol. 50(3). P. 415-423.
  • 16. Gaur, D.R. & Mudgal, A. & Singh, R.R. Approximation algorithms for cumulative VRP with stochastic demands. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2016. Vol. 9602. P. 176-189.
  • 17. Luo, Z. & Qin, H. & Zhang, D. & Lim, A. Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost. Transportation Research Part E: Logistics and Transportation Review. 2016. Vol. 85. P. 69-89.
  • 18. Jiang, J. & Gee, S.B. & Arokiasami, W.A. & Tan, K.C. Solving vehicle routing problem with stochastic demand using multi-objective evolutionary algorithm. Proceedings – 2014 International Conference on Soft Computing and Machine Intelligence. P. 121-125.
  • 19. Zare Mehrjerdi, Y. & Nadizadeh, A. Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands. European Journal of Operational Research. 2013. Vol. 229(1). P. 75-84.
  • 20. Gupta, A. & Nagarajan, V. & Ravi, R. Approximation algorithms for VRP with stochastic demands. Operations Research. 2012. Vol. 60(1). P. 123-127.
  • 21. Naumov, V. & Kholeva, O. Studying demand for freight forwarding services in Ukraine on the base of logistics portals data. Procedia Engineering. 2017. Vol. 187. P. 317-323.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4ae7ad76-7bf7-4702-a67c-8d099cef298c
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.