Skip to main content
SpringerLink
Log in

Improving Urban Air Quality Through Long-Term Optimisation of Vehicle Fleets

  • Conference paper
  • First Online:
Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1038))

Included in the following conference series:

Abstract

Pollution from vehicles in congested cities is becoming increasing concern throughout the world. Indeed, many busy cities have introduced clean air policies such as congestion charges to reduce air pollution from road traffic. One contributor to traffic pollution is fleets of vehicles being used to perform scheduled tasks such as deliveries or maintenance. This paper will demonstrate how heuristic optimisation can better schedule the allocation of tasks to vehicles over longer term periods such that considerable reductions in vehicle usage can be achieved. Genetic Algorithms and Ant Colony Optimisation approaches will be compared as to their respective ability to reduce long term vehicle usage for a Birmingham based maintenance company which has a fleet of vans. Indeed, this paper demonstrates that with longer range optimisation as much as a 45% reduction in vehicle usage and hence emissions can be achieved with the associated benefit of reduced fuel costs.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Air pollution levels rising in many of the worlds poorest cities. http://www.who.int/mediacentre/news/releases/2016/air-pollution-rising/en.

  2. 2.

    A Clean Air Zone for Birmingham

    https://www.birmingham.gov.uk/caz.

References

  1. Requia, W.J., Adams, M.D., Arain, A., Papatheodorou, S., Koutrakis, P., Mahmoud, M.: Global association of air pollution and cardiorespiratory diseases: a systematic review, meta-analysis, and investigation of modifier variables. Am. J. Public Health 108(S2), S123–S130 (2018)

    Article  Google Scholar 

  2. Calderón-Garcidueñas, L., Leray, E., Heydarpour, P., Torres-Jardón, R., Reis, J.: Air pollution, a rising environmental risk factor for cognition, neuroinflammation and neurodegeneration: the clinical impact on children and beyond. Revue Neurologique 172(1), 69–80 (2016)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  4. Gilbert, L.: The vehicle routing problem: an overview of exact and approximate algorithms. Eur. J. Oper. Res. 59(3), 345–358 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  5. Gilbert, L., Desrochers, M., Norbert, Y.: Two exact algorithms for the distance-constrained vehicle routing problem. Networks 14(1), 161–172 (1984)

    Article  MATH  Google Scholar 

  6. Laporte, G., Nobert, Y., Taillefer, S.: Solving a family of multi-depot vehicle routing and location-routing problems. Transp. Sci. 22(3), 161–172 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  7. Dondo, R., Mendez, C.A., Cerdá, J.: An optimal approach to the multiple-depot heterogeneous vehicle routing problem with time window and capacity constraints. Lat. Am. Appl. Res. 33(2), 129–134 (2003)

    Google Scholar 

  8. Dondo, R., Méndez, C.A., Cerdá, J.: Optimal management of logistic activities in multi-site environments. Comput. Chem. Eng. 32(11), 2547–2569 (2008)

    Article  Google Scholar 

  9. Dondo, R.G., Cerdá, J.: A hybrid local improvement algorithm for large-scale multi-depot vehicle routing problems with time windows. Comput. Chem. Eng. 33(2), 513–530 (2009)

    Article  Google Scholar 

  10. Benavent, E., Martínez, A.: Multi-depot multiple TSP: a polyhedral study and computational results. Ann. Oper. Res. 207(1), 7–25 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  11. Braekers, K., Caris, A., Janssens, G.K.: Exact and meta-heuristic approach for a general heterogeneous dial-a-ride problem with multiple depots. Transp. Res. Part B: Methodol. 67, 166–186 (2014)

    Article  Google Scholar 

  12. Clarke, G., Wright, J.W.: Scheduling of vehicles from a central depot to a number of delivery points. Oper. Res. 12(4), 568–581 (1964)

    Article  Google Scholar 

  13. Tillman, F.A.: The multiple terminal delivery problem with probabilistic demands. Transp. Sci. 3(3), 192–204 (1969)

    Article  Google Scholar 

  14. Tillman, F.A., Hering, R.W.: A study of a look-ahead procedure for solving the multiterminal delivery problem. Transp. Res. 5(3), 225–229 (1971)

    Article  Google Scholar 

  15. Wren, A., Holliday, A.: Computer scheduling of vehicles from one or more depots to a number of delivery points. J. Oper. Res. Soc. 23(3), 333–344 (1972)

    Article  Google Scholar 

  16. Gillett, B.E., Johnson, J.G.: Multi-terminal vehicle-dispatch algorithm. Omega 4(6), 711–718 (1976)

    Article  Google Scholar 

  17. Golden, B.L., Magnanti, T.L., Nguyen, H.Q.: Implementing vehicle routing algorithms. Networks 7(2), 113–148 (1977)

    Article  MATH  Google Scholar 

  18. Raft, O.M.: A modular algorithm for an extended vehicle scheduling problem. Eur. J. Oper. Res. 11(1), 67–76 (1982)

    Article  MATH  Google Scholar 

  19. Chao, I.M., Golden, B.L., Wasil, E.: A new heuristic for the multi-depot vehicle routing problem that improves upon best-known solutions. Am. J. Math. Manag. Sci. 13(3–4), 371–406 (1993)

    MATH  Google Scholar 

  20. Salhi, S., Sari, M.: A multi-level composite heuristic for the multi-depot vehicle fleet mix problem. Eur. J. Oper. Res. 103(1), 95–112 (1997)

    Article  MATH  Google Scholar 

  21. Salhi, S., Nagy, G.: A cluster insertion heuristic for single and multiple depot vehicle routing problems with backhauling. J. Oper. Res. Soc. 50(10), 1034–1042 (1999)

    Article  MATH  Google Scholar 

  22. Giosa, I., Tansini, I., Viera, I.: New assignment algorithms for the multi-depot vehicle routing problem. J. Oper. Res. Soc. 53(9), 977–984 (2002)

    Article  MATH  Google Scholar 

  23. Gendreau, M., Hertz, A., Laporte, G.: A tabu search heuristic for the vehicle routing problem. Manag. Sci. 40(10), 1276–1290 (1994)

    Article  MATH  Google Scholar 

  24. Renaud, J., Laporte, G., Boctor, F.F.: A tabu search heuristic for the multi-depot vehicle routing problem. Comput. Oper. Res. 23(3), 229–235 (1996)

    Article  MATH  Google Scholar 

  25. Holland, J.H.: Adaptation in Natural And Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. U Michigan Press (1975)

    Google Scholar 

  26. Filipec, M., Skrlec, D., Krajcar, S.: Darwin meets computers: new approach to multiple depot capacitated vehicle routing problem. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 1, pp. 421–426. IEEE (1997)

    Google Scholar 

  27. Salhi, S., Thangiah, S.R., Rahman, F.: A genetic clustering method for the multi-depot vehicle routing problem. In: Artificial Neural Nets and Genetic Algorithms, pp. 234–237. Springer (1998)

    Google Scholar 

  28. Skok, M., Skrlec, D., Krajcar, S.: The non-fixed destination multiple depot capacitated vehicle routing problem and genetic algorithms. In: 2000 Proceedings of the 22nd International Conference on Information Technology Interfaces, ITI 2000, pp. 403–408. IEEE (2000)

    Google Scholar 

  29. Skok, M., Skrlec, D., Krajcar, S.: The genetic algorithm scheduling of vehicles from multiple depots to a number of delivery points. Artif. Intell. 349 (2001)

    Google Scholar 

  30. Thangiah, S.R., Salhi, S.: Genetic clustering: an adaptive heuristic for the multidepot vehicle routing problem. Appl. Artif. Intell. 15(4), 361–383 (2001)

    Article  Google Scholar 

  31. Ho, W., Ho, G.T., Ji, P., Lau, H.C.: A hybrid genetic algorithm for the multi-depot vehicle routing problem. Eng. Appl. Artif. Intell. 21(4), 548–557 (2008)

    Article  Google Scholar 

  32. Surekha, P., Sumathi, S.: Solution to multi-depot vehicle routing problem using genetic algorithms. World Appl. Program. 1(3), 118–131 (2011)

    Google Scholar 

  33. Alba, E., Dorronsoro, B.: Computing nine new best-so-far solutions for capacitated VRP with a cellular genetic algorithm. Inf. Process. Lett. 98(6), 225–230 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  34. Karakatič, S., Podgorelec, V.: A survey of genetic algorithms for solving multi depot vehicle routing problem. Appl. Soft Comput. 27, 519–532 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  36. Yalian, T.: An improved ant colony optimization for multi-depot vehicle routing problem. Int. J. Eng. Tech 8, 385–388 (2016)

    Article  Google Scholar 

  37. Yu, B., Yang, Z., Xie, J.: A parallel improved ant colony optimization for multi-depot vehicle routing problem. J. Oper. Res. Soc. 62(1), 183–188 (2011)

    Article  Google Scholar 

  38. Yao, B., Hu, P., Zhang, M., Tian, X.: Improved ant colony optimization for seafood product delivery routing problem. PROMET-Traffic Transp. 26(1), 1–10 (2014)

    Google Scholar 

  39. Calvete, H.I., Galé, C., Oliveros, M.J.: Evolutive and ACO strategies for solving the multi-depot vehicle routing problem. In: IJCCI (ECTA-FCTA), pp. 73–79 (2011)

    Google Scholar 

  40. Stodola, P.: Using metaheuristics on the multi-depot vehicle routing problem with modified optimization criterion. Algorithms 11(5), 74 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  41. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: 1995 Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43. IEEE (1995)

    Google Scholar 

  42. Wenjing, Z., Ye, J.: An improved particle swarm optimization for the multi-depot vehicle routing problem. In: 2010 International Conference on E-Business and E-Government, pp. 3188–3192. IEEE (2010)

    Google Scholar 

  43. Wen, L., Meng, F.: An improved PSO for the multi-depot vehicle routing problem with time windows. In: 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 2008, vol. 1, pp. 852–856. IEEE (2008)

    Google Scholar 

  44. Geetha, S., Vanathi, P., Poonthalir, G.: Metaheuristic approach for the multi-depot vehicle routing problem. Appl. Artif. Intell. 26(9), 878–901 (2012)

    Article  MATH  Google Scholar 

  45. Geetha, S., Poonthalir, G., Vanathi, P.: Nested particle swarm optimisation for multi-depot vehicle routing problem. Int. J. Oper. Res. 16(3), 329–348 (2013)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgment

This work was carried out under the System Analytics for Innovation project, which is part-funded by the European Regional Development Fund (ERDF).

Author information

Authors and Affiliations

  1. Aston University, University Triangle, Birmingham, B4 7ET, UK

    Darren M. Chitty, Rakhi Parmar & Peter R. Lewis

Authors
  1. Darren M. Chitty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rakhi Parmar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter R. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Darren M. Chitty .

Editor information

Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chitty, D.M., Parmar, R., Lewis, P.R. (2020). Improving Urban Air Quality Through Long-Term Optimisation of Vehicle Fleets. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_6

Download citation

Publish with us

Policies and ethics

Search

Navigation