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Modeling and Solving the Vehicle Routing Problem with Multiple Fuzzy Time Windows

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Proceedings of the Eleventh International Conference on Management Science and Engineering Management (ICMSEM 2017)

Part of the book series: Lecture Notes on Multidisciplinary Industrial Engineering ((LNMUINEN))

Abstract

Considering that the customers could accept service in multiple time periods and the fuzziness of service time periods, this paper deals with the multiple time windows as fuzzy variables and quantifies the customer satisfaction level according to the membership function of the beginning time to be served, on the basis of the given acceptable satisfaction level, the vehicle routing model with multiple fuzzy time windows is constructed in order to minimize the transportation cost and number of vehicles and maximize the satisfaction level. Then according to the model characteristics, we use the punishment factors to deal with the constraints and apply particle swarm operations to solve the proposed problems. The experimental results show the effectiveness of proposed algorithm in solving the vehicle routing problems with multiple fuzzy time windows. Comparing the calculated results with the hard time window model results, it is found that our proposed model is more effective to reduce the cost of distribution.

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References

  1. Ai TJ, Kachitvichyanukul V (2009) A particle swarm optimisation for vehicle routing problem with time windows. Int J Oper Res 6(19):519–537

    Article  MATH  Google Scholar 

  2. Belhaiza S, Hansen P, Laporte G (2014) A hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows. Comput Oper Res 52:269–281

    Article  MathSciNet  MATH  Google Scholar 

  3. Bent R, Van Hentenryck P (2004) A two-stage hybrid local search for the vehicle routing problem with time windows. Transp Sci 38(4):515–530

    Article  Google Scholar 

  4. Bitao P, Fei W (2010) Hybrid intelligent algorithm for vehicle routing problem with multiple time windows. Int Forum Inf Technol Appl 1:181–184

    Google Scholar 

  5. Dantzig GB, Ramser JH (1959) The truck dispatching problem. Manage Sci 6(1):80–91

    Article  MathSciNet  MATH  Google Scholar 

  6. Desrochers M, Desrosiers J, Solomon M (1992) A new optimization algorithm for the vehicle routing problem with time windows. Oper Res 40(40):342–354

    Article  MathSciNet  MATH  Google Scholar 

  7. Doerner KF, Gronalt M et al (2008) Exact and heuristic algorithms for the vehicle routing problem with multiple interdependent time windows. Comput Oper Res 35(9):3034–3048

    Article  MathSciNet  MATH  Google Scholar 

  8. Favaretto D, Moretti E, Pellegrini P (2013) Ant colony system for a VRP with multiple time windows and multiple visits. J Interdiscip Math 10(2):263–284

    Article  MATH  Google Scholar 

  9. Ghannadpour SF, Noori S et al (2014) A multi-objective dynamic vehicle routing problem with fuzzy time windows: model, solution and application. Appl Soft Comput 14(1):504–527

    Article  Google Scholar 

  10. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: 1995 Proceedings of the IEEE International Conference on Neural Networks, vol 4, pp 1942–1948

    Google Scholar 

  11. Li Z, Zhao F, Liu H (2014) Intelligent water drops algorithm for vehicle routing problem with time windows. In: 11th International Conference on Service Systems and Service Management, vol 24, pp 1–10

    Google Scholar 

  12. Lin JJ (2006) Multi-objective decision making for vehicle routing problem with fuzzy due time. IEEE Int Conf Syst Man Cybern 4:2903–2908

    Google Scholar 

  13. Ma H, Zuo C, Yang S (2009) Modeling and solving for vehicle routing problem with multiple time windows. J Syst Eng 24(5):607–613 (in Chines)

    MATH  Google Scholar 

  14. Ma HW, Ye HR, Xia W (2012) Improved ant colony algorithm for solving split delivery vehicle routing problem with multiple time windows. Chin J Manage Sci 1:43–47 (in Chinese)

    Google Scholar 

  15. Ombuki B, Ross BJ, Hanshar F (2006) Multi-objective genetic algorithms for vehicle routing problem with time windows. Appl Intell 24(1):17–30

    Article  Google Scholar 

  16. Salman A, Ahmad I, Al-Madani S (2002) Particle swarm optimization for task assignment problem. Microprocess Microsyst 26(8):363–371

    Article  Google Scholar 

  17. Toklu NE, Gambardella LM, Montemanni R (2014) A multiple ant colony system for a vehicle routing problem with time windows and uncertain travel times. J Traffic Logistics Eng 2(1):52–58

    Article  Google Scholar 

  18. Yan H, Gao L et al (2015) Petrol-oil and lubricants support model based on multiple time windows. J Comput Appl 35(7):2096–2100

    Google Scholar 

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Acknowledgements

This research was supported by NSFC (Grant No. 71401020, Grant No. 71401093) and Human Social Science for Universities of Hebei (Grant No. BJ2016057).

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Authors and Affiliations

  1. School of Economic and Management, Chongqing Jiaotong University, Chongqing, 400074, People’s Republic of China

    Fang Yan & Yuanyuan Wang

Authors
  1. Fang Yan
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  2. Yuanyuan Wang
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Corresponding author

Correspondence to Fang Yan .

Editor information

Editors and Affiliations

  1. Business and Administration, Sichuan University, Chengdu, China

    Jiuping Xu

  2. Fuzzy Logic Systems Institute, Tokyo University of Science, Tokyo, Tokyo, Japan

    Mitsuo Gen

  3. Azerbaijan National Academy of Sciences, Baku, Azerbaijan

    Asaf Hajiyev

  4. Monash University, Clayton, Victoria, Australia

    Fang Lee Cooke

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Yan, F., Wang, Y. (2018). Modeling and Solving the Vehicle Routing Problem with Multiple Fuzzy Time Windows. In: Xu, J., Gen, M., Hajiyev, A., Cooke, F. (eds) Proceedings of the Eleventh International Conference on Management Science and Engineering Management. ICMSEM 2017. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-59280-0_69

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  • DOI: https://doi.org/10.1007/978-3-319-59280-0_69

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59279-4

  • Online ISBN: 978-3-319-59280-0

  • eBook Packages: EngineeringEngineering (R0)

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