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Two-Phase Approach for Solving the Rich Vehicle Routing Problem Based on Firefly Algorithm Clustering

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Proceedings of Sixth International Congress on Information and Communication Technology

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 235))

Abstract

The Vehicle Routing Problem (VRP) is an important optimization problem, the solution of which brings great savings to the company. Finding the optimal solution is significantly hampered by the introduction of realistic constraints such as time windows, capacity, customer-vehicle restrictions, and more. The paper presents a two-phase approach to solving the problem of vehicle routing with the fulfillment of several realistic conditions. The approach consists of customer clustering based on the firefly algorithm and process to solve rich VRP based on the created clusters. The algorithm was implemented in the real world and tested in some of the largest distribution companies in Bosnia and Herzegovina. The algorithm showed quality results in relation to the previously used methods, and in relation to the manual division of customers by the distribution manager.

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References

  1. Zunic E, Delalic S, Tucakovic Z, Hodzic K, Besirevic A (2019) Innovative modular approach based on vehicle routing problem and ant colony optimization for order splitting in real warehouses. In: Communication papers of the 14th federated conference on computer science and information systems (FedCSIS). https://doi.org/10.15439/2019f196

  2. Delalic S, Zunic E, Alihodzic A, Selmanovic E (2020) The order batching concept implemented in real smart warehouse. In: 2020 43rd international convention on information and communication technology, electronics and microelectronics (MIPRO). https://doi.org/10.23919/mipro48935.2020.9245256

  3. Žunić E, Delalić S, Hodžić K, Beširević A, Hindija H (2018) Smart warehouse management system concept with implementation. In: 14th symposium on neural networks and applications (NEUREL). https://doi.org/10.1109/NEUREL.2018.8587004

  4. Baker BM, Ayechew MA (2003) A genetic algorithm for the vehicle routing problem. Comput Oper Res. https://doi.org/10.1016/S0305-0548(02)00051-5

  5. Chiang WC, Russell RA (1996) Simulated annealing metaheuristics for the vehicle routing problem with time windows. Ann Oper Res. https://doi.org/10.1007/BF02601637

  6. Gendreau M, Hertz A, Laporte G (1994) A tabu search heuristic for the vehicle routing problem. Manag Sci. https://doi.org/10.1287/mnsc.40.10.1276

  7. Caceres-Cruz J, Arias P, Guimarans D, Riera D, Juan AA (2014) Rich vehicle routing problem: survey. ACM Comput Surv (CSUR). https://doi.org/10.1145/2666003

  8. Osaba E, Yang XS, Del Ser J (2020) Is the vehicle routing problem dead? An overview through bioinspired perspective and a prospect of opportunities. In: Nature-inspired computation in navigation and routing problems. https://doi.org/10.1007/978-981-15-1842-3_3

  9. Ai TJ, Kachitvichyanukul V (2009) A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Comput Oper Res. https://doi.org/10.1016/j.cor.2008.04.003

  10. Belmecheri F, Prins C, Yalaoui F, Amodeo L (2013) Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows. J Intell Manuf. https://doi.org/10.1007/s10845-012-0627-8

  11. Taha A, Hachimi M, Moudden A (2015) Adapted bat algorithm for capacitated vehicle routing problem. Int Rev Comput Softw (IRECOS). https://doi.org/10.15866/irecos.v10i6.6512

  12. Osaba E, Carballedo R, Yang XS, Fister I Jr, Lopez-Garcia P, Del Ser J (2018) On efficiently solving the vehicle routing problem with time windows using the bat algorithm with random reinsertion operators. In: Nature-inspired algorithms and applied optimization. https://doi.org/10.1007/978-3-319-67669-2_4

  13. Yang W, Ke L (2019) An improved fireworks algorithm for the capacitated vehicle routing problem. Front Comput Sci. https://doi.org/10.1007/s11704-017-6418-9

  14. Osaba E, Yang XS, Diaz F, Onieva E, Masegosa AD, Perallos A (2017) A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy. Soft Comput. https://doi.org/10.1007/s00500-016-2114-1

  15. Altabeeb AM, Mohsen AM, Ghallab A (2019) An improved hybrid firefly algorithm for capacitated vehicle routing problem. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2019.105728

  16. Osaba E, Carballedo R, Yang XS, Diaz F (2016) An evolutionary discrete firefly algorithm with novel operators for solving the vehicle routing problem with time windows. In: Nature-inspired computation in engineering. https://doi.org/10.1007/978-3-319-30235-5_2

  17. Vidal T, Battarra M, Subramanian A, Erdogan G (2015) Hybrid metaheuristics for the clustered vehicle routing problem. Comput Oper Res. https://doi.org/10.1016/j.cor.2014.10.019

  18. Dondo R, Cerdá J (2007) A cluster-based optimization approach for the multi-depot heterogeneous fleet vehicle routing problem with time windows. Eur J Oper Res. https://doi.org/10.1016/j.ejor.2004.07.077

  19. Expósito-Izquierdo C, Rossi A, Sevaux M (2016) A two-level solution approach to solve the clustered capacitated vehicle routing problem. Comput Ind Eng. https://doi.org/10.1016/j.cie.2015.11.022

  20. Žunić E, Đonko D, Šupić H, Delalić S (2020) Cluster-based approach for successful solving real-world vehicle routing problems. In: 15th conference on computer science and information systems (FedCSIS). https://doi.org/10.15439/2020F184

  21. Senthilnath J, Omkar SN, Mani V (2011) Clustering using firefly algorithm: performance study. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2011.06.003

  22. Žunić E, Delalić S, Hodžić K, Tucaković Z (2019) Innovative GPS data anomaly detection algorithm inspired by QRS complex detection algorithms in ECG signals. In: EUROCON 2019—18th international conference on smart technologies. https://doi.org/10.1109/EUROCON.2019.8861619

  23. Žunić E, Hindija H, Beširević A, Hodžić K, Delalić S (2018) Improving performance of vehicle routing algorithms using GPS data. In: 14th symposium on neural networks and applications (NEUREL). https://doi.org/10.1109/NEUREL.2018.8586982

  24. Žunić E, Delalić S, Đonko, Dž (2020) Adaptive multi-phase approach for solving the realistic vehicle routing problems in logistics with innovative comparison method for evaluation based on real GPS data. Transp Lett. https://doi.org/10.1080/19427867.2020.1824311

  25. Žunić E, Kuric A, Delalić S (2020) Improving unloading time prediction for vehicle routing problem based on GPS data. In: Position papers of the 15th federated conference on computer science and information systems (FedCSIS). https://doi.org/10.15439/2020F123

  26. Yang X (2010) Nature-inspired metaheuristic algorithms, 2nd edn. ISBN: 1905986289, 9781905986286

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Correspondence to Emir Žunić .

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Žunić, E., Delalić, S., Đonko, D., Šupić, H. (2022). Two-Phase Approach for Solving the Rich Vehicle Routing Problem Based on Firefly Algorithm Clustering. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_25

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