Skip to main content

Advertisement

SpringerLink
Log in

Bayesian hierarchical multi-objective optimization for vehicle parking route discovery

  • S.I. : ARIIS
  • Published:
Innovations in Systems and Software Engineering Aims and scope Submit manuscript

Abstract

Discovering an optimal route to the most feasible parking lot has been a matter of apprehension for any driver. Selecting the most optimal route to the most favorable lot aggravates further during peak hours of the day and at congested places. This leads to a considerable wastage of resources specifically time and fuel. This work proposes a Bayesian hierarchical technique for obtaining this optimal route. The route selection is based on conflicting objectives, and hence, the problem belongs to the domain of multi-objective optimization. A probabilistic data-driven method has been used to overcome the inherent problem of weight selection in the popular weighted sum technique. The weights of these conflicting objectives have been refined using a Bayesian hierarchical model based on multinomial and Dirichlet prior. Genetic algorithm has been used to obtain optimal solutions. Simulated data have been used to obtain routes which are in close agreement with real-life situations. Statistical analyses have shown the superiority of the weights obtained using the proposed algorithm based on Bayesian technique over the existing frequentist technique.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Coello Coello CA, Van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems. Springer US, New York

    Book  Google Scholar 

  3. Fonseca CM, Fleming PJ (1995) An overview of evolutionary algorithms in multi-objective optimization. Evol Comput 3:1–16

    Article  Google Scholar 

  4. Van Veldhuizen DA, Lamont GB (2000) Multi-objective evolutionary algorithms: analyzing the state-of-the-art. Evol Comput 8:125–147

    Article  Google Scholar 

  5. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York

    MATH  Google Scholar 

  6. Goldberg DE (1998) Genetic algorithm in search, optimization, and machine learning. Addison-Wesley, Reading

    Google Scholar 

  7. Haupt RL, Haupt HS (2004) Practical genetic algorithms, 2nd edn. Wiley, Hoboken

    MATH  Google Scholar 

  8. Arulmozhiyal R, Jubril AM (2012) A nonlinear weights selection in weighted sum for convex multi-objective optimization. Facta Univ Ser Math Inform 27:357–372

    MathSciNet  MATH  Google Scholar 

  9. Zadeh LA (1963) Optimality & non-scalar-valued performance criteria. IEEE Trans Autom Control 8:59–60

    Article  Google Scholar 

  10. Konak A, Coitb DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliabil Eng Syst Saf 91:992–1007

    Article  Google Scholar 

  11. Athan TW, Papalambros PY (1996) A note on weighted criteria methods for compromise solutions in multi-objective optimization. Eng Opt 27:155–176

    Article  Google Scholar 

  12. Ryu JH, Kim S, Wan H (2009) Pareto front approximation with adaptive weighted sum method in multi-objective simulation optimization. In: Proceedings of 2009 Winter Simulation Conference, Austin, TX, pp 623–633

  13. Schmaranzer D, Braune R, Doerner KF (2019) Multi-objective simulation optimization for complex urban mass rapid transit systems. Ann Oper Res 1–38

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

    Article  MathSciNet  Google Scholar 

  15. Carić T, Galić A, Fosin J, Gold H, Reinholz A (2008) A modelling and optimization framework for real-world vehicle routing problems, vehicle routing problem. I-Tech Education and Publishing, Vienna

    Google Scholar 

  16. Golden B, Raghavan S, Wasil E (2008) The vehicle routing problem: latest advances and new challenges. Operations research-computer science interfaces series. Springer, Berlin, p 43

    Book  Google Scholar 

  17. Han Y, Shan J, Wang M, Yang G (2017) Optimization design and evaluation of parking route based on automatic assignment mechanism of parking lot. Adv Mech Eng 9:1–9

    Google Scholar 

  18. Siemens (2020) Intelligent Parking Solutions—Siemens. www.mobility.siemens.com/global/en/portfolio/road/parking-solutions/intelligent-parking-solutions.html. Accessed 23 June 2020

  19. Xiao Y, Konak A (2017) A genetic algorithm with exact dynamic programming for green vehicle routing & scheduling problem. J Clean Prod 167:1450–1463

    Article  Google Scholar 

  20. Lee HY, Shin H, Chae J (2018) Path planning for mobile agents using a genetic algorithm with a direction guided factor. Electronics 7:212

    Article  Google Scholar 

  21. Fogel D (2005) Evolutionary computation: toward a new philosophy of machine intelligence, 3rd edn. Wiley, Hoboken

    Book  Google Scholar 

  22. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. MIT Press, Cambridge

    Book  Google Scholar 

  23. Bryant K (2000) Genetic algorithms and the traveling salesman problem. Master’s Dissertation. Harvey Mudd College, Claremont, United States

  24. Chand P, Mohanty JR (2013) A Multi-objective vehicle, routing problem using dominant rank method. In: Proceedings of international conference in Distributed Computing & Internet Technology, International Journal of Computer Applications 0975-8887, pp 29–34

  25. Pelikan M (2010) Genetic algorithms. MEDAL Report No. 2010007

  26. Athan TW, Papalambros PY (1996) A quasi-Monte Carlo method for multi-criteria optimization. Eng Opt 27:177–198

    Article  Google Scholar 

  27. Gennert MA, Yuille AL (1998) Determining the optimal weights in multiple objective function optimization. In: 2nd International Conference on Computer Vision, IEEE, Los Alamos, CA, pp 87–89

  28. Martins MSR, Delgado MRBS, Lüders R (2018) Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem. J Heuristics 24:25–47

    Article  Google Scholar 

  29. Tang Y, Marshall L, Sharma A, Ajami H (2018) A Bayesian alternative for multi-objective eco-hydrological model specification. J Hydrol 556:25–38

    Article  Google Scholar 

  30. Wada T, Hino H (2019) Bayesian optimization for multi-objective optimization and multi-point search. ArXiv, abs/1905.02370

  31. Brochu E, Cora VM, de Freitas N (2010) A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv:1012.2599

  32. Hernández-Lobato D, Hernandez-Lobato J, Shah A, Adams R (2016) Predictive entropy search for multi-objective bayesian optimization. In: International conference on machine learning, pp 1492–1501

  33. Tsoulos IG, Stavrou V, Mastorakis NE, Tsalikakis D (2019) GenConstraint: a programming tool for constraint optimization problems. SoftwareX 10:100355

    Article  Google Scholar 

  34. Mockus J (1989) Bayesian approach to global optimization: theory and applications. Springer, Dordrecht

    Book  Google Scholar 

  35. Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems, vol 25, pp 2951–2959

  36. Tajbakhsh SD (2012) A fully Bayesian approach to the efficient global optimization algorithm. Ph.D. Dissertation. The Pennsylvania State University, Pennsylvania

  37. Galuzio PP, Hochsteiner de Vasconcelos Segundo E, Santos-Coelho LD, Mariani VC (2020) MOBOpt—multi-objective Bayesian optimization. SoftwareX 12:100520

    Article  Google Scholar 

Download references

Funding

This work is not funded by any agency/organization.

Author information

Authors and Affiliations

  1. Department of Computer Science, St. Xavier’s College (Autonomous), Kolkata, India

    Romit S. Beed

  2. Department of Computer Science and Engineering, Assam University, Silchar, India

    Sunita Sarkar

  3. Department of Computer Science, Assam University, Silchar, India

    Arindam Roy

Authors
  1. Romit S. Beed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sunita Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arindam Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sunita Sarkar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Beed, R.S., Sarkar, S. & Roy, A. Bayesian hierarchical multi-objective optimization for vehicle parking route discovery. Innovations Syst Softw Eng 17, 109–120 (2021). https://doi.org/10.1007/s11334-020-00373-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11334-020-00373-4

Keywords

Search

Navigation