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Innovative modular approach based on vehicle routing problem and ant colony optimization for order splitting in real warehouses

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
Języki publikacji
EN
Abstrakty
EN
A crucial part to any warehouse workflow is the process of order picking. Orders can significantly vary in the number of items, mass, volume and the total path needed to collect all the items. Some orders can be picked by just one worker, while others are required to be split up and shrunk down, so that they can be assigned to multiple workers. This paper describes the complete process of optimal order splitting. The process consists of evaluating if a given order requires to be split, determining the number of orders it needs to be split into, assigning items for every worker and optimizing the order picking routes. The complete order splitting process can be used both with and without the logistic data (mass and volume), but having logistic data improves the accuracy. Final step of the algorithm is reduction to Vehicle Routing Problem where the total number of vehicles is known beforehand. The process described in this paper is implemented in some of the largest warehouses in Bosnia and Herzegovina.
Rocznik
Tom
Strony
125--129
Opis fizyczny
Bibliogr. 15 poz., wz., wykr., tab.
Twórcy
autor
  • Info Studio d.o.o., Sarajevo, Bosnia and Herzegovina
  • Faculty of Electrical Engineering at University of Sarajevo, Bosnia and Herzegovina
  • Faculty of Science at University of Sarajevo, Bosnia and Herzegovina
  • Info Studio d.o.o., Sarajevo, Bosnia and Herzegovina
  • Faculty of Electrical Engineering at University of Sarajevo, Bosnia and Herzegovina
  • Info Studio d.o.o., Sarajevo, Bosnia and Herzegovina
  • Faculty of Electrical Engineering at University of Sarajevo, Bosnia and Herzegovina
  • Info Studio d.o.o., Sarajevo, Bosnia and Herzegovina
  • Faculty of Science at University of Sarajevo, Bosnia and Herzegovina
  • Info Studio d.o.o., Sarajevo, Bosnia and Herzegovina
Bibliografia
  • 1. D. M. H. Chiang, C. P. Lin and M. C. Chen, "The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres," Enterprise Information Systems 5.2, 2011. https://doi.org/10.1080/17517575.2010.537784
  • 2. R. de Koster, T. Le-Duc, and K. J. Roodbergen, “Design and control of warehouse order picking: A literature review,” Eur. J. Oper. Res., 2007. https://doi.org/10.1016/j.ejor.2006.07.009
  • 3. E. Zunic, S. Delalic, K. Hodzic, A. Besirevic, and H. Hindija, “Smart Warehouse Management System Concept with Implementation,” in 2018 14th Symposium on Neural Networks and Applications, NEUREL 2018, 2018. https://doi.org/10.1109/NEUREL.2018.8587004
  • 4. E. Zunic, A. Besirevic, R. Skrobo, H. Hasic, K. Hodzic, and A. Djedovic, “Design of optimization system for warehouse order picking in real environment,” in ICAT 2017 - 26th International Conference on Information, Communication and Automation Technologies, Proceedings, 2017. https://doi.org/10.1109/ICAT.2017.8171630
  • 5. E. Zunic, A. Besirevic, S. Delalic, K. Hodzic, and H. Hasic, “A generic approach for order picking optimization process in different warehouse layouts,” in 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018 - Proceedings, 2018. https://doi.org/10.23919/MIPRO.2018.8400183
  • 6. E. Zunic, H. Hasic, K. Hodzic, S. Delalic, and A. Besirevic, “Predictive analysis based approach for optimal warehouse product positioning,” in 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018 - Proceedings, 2018. https://doi.org/10.23919/MIPRO.2018.8400174
  • 7. E. Zunic, K. Hodzic, H. Hasic, R. Skrobo, A. Besirevic, and D. Donko, “Application of advanced analysis and predictive algorithm for warehouse picking zone capacity and content prediction,” in ICAT 2017 - 26th International Conference on Information, Communication and Automation Technologies, Proceedings, 2017. https://doi.org/10.1109/ICAT.2017.8171629
  • 8. M. N. Kritikos and G. Ioannou, “The balanced cargo vehicle routing problem with time windows,” Int. J. Prod. Econ., 2010. https://doi.org/10.1016/j.ijpe.2009.07.006
  • 9. I. H. Osman, "Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem," Annals of operations research 41.4, 1993. https://doi.org/10.1007/BF02023004
  • 10. M. Gendreau, A. Hertz, and G. Laporte, "A tabu search heuristic for the vehicle routing problem." Management science 40.10, 1994. https://doi.org/10.1287/mnsc.40.10.1276
  • 11. S. Acharya, "Vehicle Routing and Scheduling Problems with time window constraints-Optimization Based Models," Int Jr. of Mathematical Sciences Applications 3.1, 2013.
  • 12. M. Desrochers, J. Desrosiers and M. Solomon, "A new optimization algorithm for the vehicle routing problem with time windows," Operations research 40.2, 1992. https://doi.org/10.1287/opre.40.2.342
  • 13. J. E. Bell and P. R. McMullen, "Ant colony optimization techniques for the vehicle routing problem,” Adv. Eng. Informatics, 2004. https://doi.org/10.1016/j.aei.2004.07.001
  • 14. M. Yu, “Enhancing Warehouse Performance by Efficient Order Picking,” 2008. https://doi.org/10.1287/opre.37.3.404
  • 15. M. Dorigo and L. M. Gambardella, “Ant colonies for the travelling salesman problem,” BioSystems, 1997. https://doi.org/10.1016/S0303-2647(97)01708-5
Uwagi
1. Track 4: Information Systems and Technologies
2. Technical Session: 25th Conference on Knowledge Acquisition and Management
3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c4eaead5-3356-4dee-9aa5-eb617097cb15
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