The Personalized Multi-criteria Route Planning Problem in Repeated Travel and Its Solution Algorithm

  • Ci-yun Lin
  • Bowen Gong
  • Zhi-jian Wang
  • Man-rong Yuan
  • Kai-jian Hu
  • Chen-gang Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)


Existing research on the personalized multi-criteria route planning (PMRP) problem seldom considers drivers’ travel characteristics for different types of travel, which significantly affects a driver’s performance in reality. In this research, the PMRP problem in repeated travel is presented and defined. The relative differences between route-costs and their respective minimums are considered as the driver’s route choice criteria for repeated travel. The range of each criterion value from the driver’s experience data is introduced into the problem definition as the constraint. In addition, a travel-law-based route planning (TRP) algorithm is designed, implemented, and evaluated in comparison to the genetic algorithm (GA) for solving the proposed problem. The comparison results show that the TRP algorithm achieved better results in terms of running time, criteria values, and comprehensive objective function values. The experimental results also show that for the given cases, the TRP algorithm effectively avoided impractical solutions and achieved a 0.96-second average run time to reach approximate comprehensive objective function values for the routes chosen by two drivers in practice over a real-road network with 2000 nodes and 7014 edges using a PC with a 2.53-GHz-CoreTM i5-based dual-core processor.


Personalized multi-criteria route planning Driver’s route choice behavior Travel-law-based route planning algorithm Genetic algorithm Shortest path 



The authors acknowledge the Science and technology project of Jilin Provincial Education Department (Grant No. JJKH20170810KJ and JJKH20180150KJ) and Youth Scientific Research Fund of Jilin (Grant No. 20180520075JH) are partly support this work.


  1. 1.
    Gandibleux X, Beugnies F, Randriamasy S (2006) Martins’ algorithm revisited for multi-objective shortest path problems with a MaxMin cost function. 4OR: Q J Oper Res 4(1):47–59MathSciNetCrossRefGoogle Scholar
  2. 2.
    Mooney P, Winstanley A (2006) An evolutionary algorithm for multicriteria path optimization problems. Int J Geogr Inf Sci 20(4):401–423CrossRefGoogle Scholar
  3. 3.
    Pahlavani P, Delavar MR (2014) Multi-criteria route planning based on a driver’s preferences in multi-criteria route selection. Transp Res C-Emer 40:14–35CrossRefGoogle Scholar
  4. 4.
    Kovacs AA, Parragh SN, Hartl RF (2015) The multi-objective generalized consistent vehicle routing problem. Eur J Oper Res 247(2):441–458MathSciNetCrossRefGoogle Scholar
  5. 5.
    Coelho LC, LAPORTE G (2013) A branch-and-cut algorithm for the multi-product multi-vehicle inventory-routing problem. Int J Prod Res 51(23–24):7156–7169CrossRefGoogle Scholar
  6. 6.
    Smilowitz K, Nowak M, Jiang TT (2013) Workforce management in periodic delivery operations. Transp Sci 47(2):214–230CrossRefGoogle Scholar
  7. 7.
    Liang WY, HUANG CC, Lin YC et al (2013) The multi-objective label correcting algorithm for supply chain modeling. Int J Prod Econ 142(1):172–178CrossRefGoogle Scholar
  8. 8.
    Iori M, Martello S, Pretolani D (2010) An aggregate label setting policy for the multi-objective shortest path problem. Eur J Oper Res 207(3):1489–1496MathSciNetCrossRefGoogle Scholar
  9. 9.
    Pulido FJ, Mandow L, Perez-De-la-cruz JL (2015) Dimensionality reduction in multiobjective shortest path search. Comput Oper Res 64:60–70MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kang TP, Zhang XG, Wang ZF (2011) Algorithm for shortest path of multi-objectives based on k short path algorithm. J Changzhou Inst Technol 24(3):25–28Google Scholar
  11. 11.
    Rajabi-Bahaabadi M, Shariat-Mohaymany A, Babaei M et al (2015) Multi-objective path finding in stochastic time-dependent road networks using non-dominated sorting genetic algorithm. Expert Syst Appl 42(12):5056–5064CrossRefGoogle Scholar
  12. 12.
    Ombuki B, Ross BJ, HANSHAR F (2006) Multi-objective genetic algorithms for vehicle routing problem with time windows. Appl Intell 24(1):17–30CrossRefGoogle Scholar
  13. 13.
    Osman MS, ABO-SINNA MA, MOUSA AA (2005) An effective genetic algorithm approach to multiobjective routing problems (MORPs). Appl Math Comput 163(2):769–781MathSciNetzbMATHGoogle Scholar
  14. 14.
    Guerriero F, Musmanno R (2001) Label correcting methods to solve multicriteria shortest path problems. J Optimiz Theory App 111(3):589–613MathSciNetCrossRefGoogle Scholar
  15. 15.
    Ji ZW, Kim YS, Chen A (2011) Multi-objective alpha-reliable path finding in stochastic networks with correlated link costs: a simulation-based multi-objective genetic algorithm approach (SMOGA). Expert Syst Appl 38(3):1515–1528CrossRefGoogle Scholar
  16. 16.
    Fang Z, Zong X, Li Q et al (2011) Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach. J Transp Geogr 19(3):443–451CrossRefGoogle Scholar
  17. 17.
    Ghoseiri K, Nadjari B (2010) An ant colony optimization algorithm for the bi-objective shortest path problem. Appl Soft Comput 10(4):1237–1246CrossRefGoogle Scholar
  18. 18.
    Bezerra LCT, Goldbarg EFG, Goldbarg MC et al (2013) Analyzing the impact of MOACO components: an algorithmic study on the multi-objective shortest path problem. Expert Syst Appl 40(1):345–355CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ci-yun Lin
    • 1
  • Bowen Gong
    • 1
  • Zhi-jian Wang
    • 2
  • Man-rong Yuan
    • 3
  • Kai-jian Hu
    • 4
  • Chen-gang Wang
    • 5
  1. 1.College of Transportation, Jilin UniversityChangchunChina
  2. 2.College of Electrical and Control EngineeringNorth China University of TechnologyBeijingChina
  3. 3.Traffic Police SquadKunming Municipal Public Security BureauKunmingChina
  4. 4.Jinan Automobile Test CentreJinanChina
  5. 5.Some Troops of North TheaterShenyangChina

Personalised recommendations