Skip to main content
Springer Nature Link
Log in

Research on Vehicle Routing Problem and Its Optimization Algorithm Based on Assembled Building

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10955))

Included in the following conference series:

  • 2401 Accesses

Abstract

Assembled building flow distribution has become the main problem facing the industry. The vehicle routing problem (VRP) is the key link in the distribution system. This article systematically summarizes the classification of common and the basic algorithm of VRP problems. It fully understands the commonly used and efficient heuristic algorithms for solving VRPs and the corresponding research status. Finally, it summarizes the problems existing in the research. The future research and prospective solution methods of VRPs are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Dantzig, G.B., Ramser, J.K.: The truck dispatching problem. Manag. Sci. 6, 80–91 (1959)

    Article  MathSciNet  Google Scholar 

  2. Bi, G.-t.: Business School, Henan University, Kaifeng 475000, China

    Google Scholar 

  3. Baker, B.M., Ayechew, M.A.: A genetic algorithm for the vehicle routing problem. Comput. Oper. Res. 30, 787–800 (2003)

    Article  MathSciNet  Google Scholar 

  4. Prins, C.: A simple and effective evolutionary algorithm for the vehicle routing problem. Comput. Oper. Res. 31, 1985–2002 (2004)

    Article  MathSciNet  Google Scholar 

  5. Zhang, Q., Yan, R.: School of Economics and Management, University of Science & Technology Beijing, Beijing 100083, China

    Google Scholar 

  6. Hwang, H.S.: An improved model for vehicle routing problem with time constraint based on genetic algorithm. Comput. Ind. Eng. 42, 361–369 (2002)

    Article  Google Scholar 

  7. Pei, X.-b., Jia, D.-f.: School of Management, Tianjin University of Technology, Tianjin 300384, China

    Google Scholar 

  8. Wang, B., Shang, X.-c., Li, H.-f.: Applied Institute, University of Science and Technology Beijing, Beijing 100083, China; Transport Planning and Research Institute Ministry of Communications, Beijing 100028, China

    Google Scholar 

  9. Mu, D., Wang, C., Wang, S.-c., Zhou, S.-c.: .School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China; School of Economics and Management, Beijing University of Technology, Beijing 100124; School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China; Qingdao Geotechnical Investigation and Surveying Research Institute, Qingdao 266032, China

    Google Scholar 

  10. Mirabi, M., Ghomi, S.F., Jolai, F.: Efficent stochastic hybrid heuristics for the multi-depot vehicle routing problem. Rob. Cim.-Int. Manufac. 26, 564–569 (2010)

    Article  Google Scholar 

  11. Chen, Y.-x.: School of Economics & Management, Harbin Engineering University, Harbin 150001, China

    Google Scholar 

  12. Zhang, W.-z., Lin, J.-b., Wu, H.-s., Tong, R.-f., Dong, J.-x.: Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, China; Zhejiang Jinji Electronic Co. Ltd. Hangzhou 310013, China; Basic Study, Zhejiang Police College, Hangzhou 310053, China

    Google Scholar 

  13. Yue, Y.-x., Zhhou, L.-s., Yue, q.-x., Sun, Q.: Sch. of Traffic & Transportation, Beijing Jiaotong Univ., Beijing 100044, China; Sch. of Economics & Management, Beihang Univ., Beijing 100083, China

    Google Scholar 

  14. Liu, Z., Shen, J.: An adaptive ant colony algorithm for vehicle routing problem based on the evenness of solution. Acta Simulata Systematica Sinica. 5, 016 (2002)

    Google Scholar 

  15. Qin, Y.Q., Sun, D.B., Li, N., et al.: Path planning for mobile robot using the particle swarm optimization with mutation operator. In: Proceedings of 2004 IEEE International Conference on Machine Learning and Cybernetics 2004, vol. 4, pp. 2473–2478 (2004)

    Google Scholar 

  16. Mohemmed, A.W., Sahoo, N.C., Geok, T.K.: Solving shortest path problem using particle swarm optimization. Appl. Soft Comput. 8, 1643–1653 (2008)

    Article  Google Scholar 

  17. Wu, Y.-H., Zhang, N.-Z.: Modified particle swarm optimization algorithm for vehicle routing problem with time windows. Comput. Eng. Appl. 46(15), 230–234 (2010)

    Google Scholar 

  18. Wei, Z.U., Gang, L.I., Zhengxia, Q.I.: Study on a path planning method based on improved particle swarm optimization. J. Projectiles Rockets Missiles Guidance (2008)

    Google Scholar 

  19. Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol. 65. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12538-6_6

    Chapter  Google Scholar 

  20. Ma, X.-I., Zhang, H.-z., Ma, L.: School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China

    Google Scholar 

  21. Wang, G.G., Chu, H.C.E., Mirjalili, S.: Three-dimensional path planning for UCAV using an improved bat algorithm. Aerosp. Sci. Technol. 49, 231–238 (2008)

    Article  Google Scholar 

  22. Sun, Q., Zhang, H.: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China

    Google Scholar 

  23. Deng, Y., Chen, Y., Zhang, Y., et al.: Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment. Appl. Soft Comput. 12, 1231–1237 (2012)

    Article  Google Scholar 

  24. Noto, M., Sato, H.: A method for the shortest path search by extended Dijkstra algorithm. In: 2000 IEEE International Conference on Systems, Man, and Cybernetics.vol. 3, pp. 2316–2320. IEEE (2000)

    Google Scholar 

  25. Yuan, B., Liu, J.-s., Qian, D., Luo, D.-h.: School of Institute of mechanical and electrical engineering, Nanchang University, Nanchang 330031, China

    Google Scholar 

  26. Wang, J., Zhang, X., Chen, B., Chen, H.: Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China; Informat ion Engineering School, Wuhan University of S cience and Technology, Wuhan 430081, China

    Google Scholar 

  27. Zhou, Y., Luo, Q., Xie, J., Zheng, H.: A hybrid bat algorithm with path relinking for the capacitated vehicle routing problem. In: Yang, X.-S., Bekdaş, G., Nigdeli, S.M. (eds.) Metaheuristics and Optimization in Civil Engineering. MOST, vol. 7, pp. 255–276. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-26245-1_12

    Chapter  Google Scholar 

  28. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1, 53–66 (1997)

    Article  Google Scholar 

  29. Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant algorithms for discrete optimization. Artif. Life. 5, 137–172 (1999)

    Article  Google Scholar 

  30. Roberge, V., Tarbouchi, M., Labonté, G.: Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Ind. Inform. 9, 132–141 (2012)

    Article  Google Scholar 

  31. Li, G., Shi, H.: Path planning for mobile robot based on particle swarm optimization. Robotica, 3290–3294(2004)

    Google Scholar 

  32. Wang, G., Guo, L., Duan, H., et al.: A bat algorithm with mutation for UCAV path planning. Sci. World J. 2012, 15 (2012)

    Google Scholar 

  33. Liberatore, F., Ortuño, M.T., Tirado, G., et al.: A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in Humanitarian Logistics. Comput. Oper. Res. 42, 3–13 (2014)

    Article  MathSciNet  Google Scholar 

  34. Sheu, J.B.: A hybrid fuzzy-optimization approach to customer grouping-based logistics distribution operations. Appl. Math. Model. 31, 1048–1066 (2007)

    Article  Google Scholar 

  35. Gu, Q.I.N.: Logistics distribution center allocation based on ant colony optimization. Syst. Eng. Theor. Pract. 4, 120–124 (2006)

    Google Scholar 

  36. Caramia, M., Dell’Olmo, P.: Multi-Objective Management in Freight Logistics: Increasing Capacity, Service Level and Safety with Optimization Algorithm. Springer, London (2008). https://doi.org/10.1007/978-1-84800-382-8

    Book  Google Scholar 

  37. Zhang, J., Zhou, Q.: Study on the optimization of logistics distribution VRP based on immune clone algorithm. J. Hunan Univ. (Natural Science) 5, 013 (2004)

    Google Scholar 

  38. Luo, Y., Chen, Z.Y.: Path optimization of logistics distribution based on improved genetic algorithm. Syst. Eng. 30, 118–122 (2012)

    Google Scholar 

  39. Li, R., Yuan, J.: Research on the optimization of logistics distribution routing based on improved genetic algorithm. J. Wuhan Univ. Technol. 12, 028 (2004)

    Google Scholar 

  40. Marinakis, Y., Marinaki, M.: A particle swarm optimization algorithm with path relinking for the location routing problem. J. Math. Model. Alg. 7, 59–78 (2008)

    Article  MathSciNet  Google Scholar 

  41. Wang, X., Li, Y.: Research on optimization of logistics distribution routing under electronic commerce. Jisuanji Gongcheng/ Comput. Eng. 33, 202–204 (2007)

    Google Scholar 

  42. Wang, Y., Ma, X., Xu, M., et al.: Two-echelon logistics distribution region partitioning problem based on a hybrid particle swarm optimization–genetic algorithm. Exper. Syst. Appl. 42, 5019–5031 (2015)

    Article  Google Scholar 

  43. Bell, J.E., Griffis, S.E.: Swarm intelligence: application of the ant colony optimization algorithm to logistics-oriented vehicle routing problems. J. Bus. Logistics 31, 157–175 (2010)

    Article  Google Scholar 

  44. Wang, H., Li, W.: Study on logistics distribution route optimization by improved particle swarm optimization. ACM Trans. Model Comput. Simul. 5, 243–246 (2012)

    Google Scholar 

  45. Jiang, Z., Wang, D.: Model and algorithm of location optimization of distribution centers for B2C E-commerce. Control Decis. 20, 1125 (2005)

    Google Scholar 

  46. Jie-ming, W.U.: Vehicle routing optimization problem of logistics distribution. ACM Trans. Model Comput. Simul. 7, 357–360 (2011)

    Google Scholar 

  47. Jiang, Z., Wang, D.: Model and algorithm for logistics distribution routing of B2C e-commerce. Inf. Control-Shenyang 34, 481 (2005)

    Google Scholar 

  48. Jianya, Y.Y.G.: An efficient implementation of shortest path algorithm based on dijkstra algorithm. J. Wuhan Tech. Univ. Surv. Mapping. 24(3), 208–212 (1999)

    Google Scholar 

  49. Kang, H.I., Lee, B., Kim, K.: Path planning algorithm using the particle swarm optimization and the improved Dijkstra algorithm. In: 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 2008, vol. 2, pp. 1002–1004. IEEE (2008)

    Google Scholar 

Download references

Acknowledgement

This research is partially supported by the National Science Foundation of China (61773192, 61503170, 61603169, 61773246), Shandong Province Higher Educational Science and Technology Program (J17KZ005, J14LN28), Natural Science Foundation of Shandong Province (ZR2016FL13, ZR2017BF039), Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education (K93-9-2017-02), and State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201602).

Author information

Authors and Affiliations

  1. School of Information, Shandong Normal University, Jinan, 250014, China

    Kun Jiang, Jun-qing Li & Pei-yong Duan

  2. School of Computer, Liaocheng University, Liaocheng, 252059, China

    Jun-qing Li

  3. China Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, 211189, People’s Republic of China

    Jun-qing Li, Ben Niu, Yongqin Jiang & Xiaoping Lin

  4. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China

    Jun-qing Li

Authors
  1. Kun Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun-qing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ben Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongqin Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoping Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pei-yong Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jun-qing Li or Pei-yong Duan .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Wuhan University of Science and Technology, Wuhan City, China

    Xiao-Long Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jiang, K., Li, Jq., Niu, B., Jiang, Y., Lin, X., Duan, Py. (2018). Research on Vehicle Routing Problem and Its Optimization Algorithm Based on Assembled Building. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_83

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95933-7_83

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95932-0

  • Online ISBN: 978-3-319-95933-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics