[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

A multi-objective open set orienteering problem

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

We have constructed a multi-objective set orienteering problem to model real-world problems more fittingly than the existing models. It attaches (i) a predefined profit associated with each cluster of customers, and (ii) a preset maximum service time associated with each customer of all the clusters. When a customer from cluster visits, it allows the earning of a profit score. Our purpose is primarily to search for a route that (i) on the one hand allows us to service each cluster for maximizing customer satisfaction and (ii) on the other hand allows us to maximize our profits, too. The model assumes that the more time we spend on servicing, the more customer satisfaction it yields. It tries to cover as many clusters as possible within a specified time budget. In this paper, we also consider third-party logistics to allow the flexibility of ending our journey at any cluster of our choice. The proposed model is solved using the nondominated sorting genetic algorithm and the strength Pareto evolutionary algorithm. Here, we also generate the dataset to test the proposed model by using instances from the literature of the generalized traveling salesman problem. Finally, we present a comparative result analysis with the help of some statistical tools and discuss the results.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Lawler E, Lenstra J, Rinnooy K, Shmoys D (1985) The traveling salesman problem: a guided tour of combinatorial optimization. Wiley, NY

    MATH  Google Scholar 

  2. Balas E (1989) The prize collecting traveling salesman problem. Networks 19:621–636

    Article  MathSciNet  Google Scholar 

  3. Fischetti M, Gonzlez JJS, Toth P (1997) A branch-and-cut algorithm for the symmetric generalized traveling salesman problem. Oper Res 45(3):378–394

    Article  MathSciNet  Google Scholar 

  4. Feillet D, Dejax P, Gendreau M (2005) Traveling salesman problems with profits. Transpor Sci 39:188–205

    Article  Google Scholar 

  5. Dantzig G, Ramser J (1959) The truck dispatching problem’. Manag Sci 6:80. https://doi.org/10.1287/mnsc.6.1.80

    Article  MathSciNet  MATH  Google Scholar 

  6. Tsiligirides T (1984) Heuristic methods applied to orienteering. J Oper Res Soc 35:797–809

    Article  Google Scholar 

  7. Angelelli E, Archetti C, Vindigni M (2014) The clustered orienteering problem. Eur J Oper Res 238:404–414

    Article  MathSciNet  Google Scholar 

  8. Archetti C, Carrabs F, Cerulli R (2017) The set orienteering problem. Eur J Oper Res 267(1):264–272

    Article  MathSciNet  Google Scholar 

  9. Pěnička R, Faigl J, Saska M (2019) Variable neighborhood search for the set orienteering problem and its application to other orienteering problem variants. Eur J Oper Res. https://doi.org/10.1016/j.ejor.2019.01.047

    Article  MathSciNet  MATH  Google Scholar 

  10. Faigl J, Pěnička R, Best G (2016) Self-organizing map-based solution for the orienteering problem with neighborhoods. In: Proceedings of the IEEE international conference on systems, man, and cybernetics, pp 1315–1321

  11. Pěnička R, Faigl J, Váˇna P, Saska M (2017) Dubins orienteering problem. IEEE Robot Autom Lett 2(2):1210–1217

    Article  Google Scholar 

  12. Chao I, Golden B, Wasil E (1996) Theory and methodology—a fast and effective heuristic for the orienteering problem. Eur J Oper Res 88:475–489

    Article  Google Scholar 

  13. Laporte G, Martello S (1990) The selective traveling salesman problem. Discrete Appl Math 26:193–207

    Article  MathSciNet  Google Scholar 

  14. Kataoka S, Morito S (1988) An algorithm for the single constraint maximum collection problem. J Oper Res Soc Jpn 31(4):515–530

    MathSciNet  MATH  Google Scholar 

  15. Arkin E, Mitchell J, Narasimhan G (1998) Resource-constrained geometric network optimization. In: Proceedings 14th ACM symposium on computational geometry, June, pp 307–316

  16. Vansteenwegen P, Souffriau W, Van Oudheusden D (2011) The orienteering problem: a survey. Eur J Oper Res 209:1–10. https://doi.org/10.1016/j.ejor.2010.03.045

    Article  MathSciNet  MATH  Google Scholar 

  17. Gunawan A, Lau H, Vansteenwegen P (2016) Orienteering problem: a survey of recent variants, solution approaches, and applications. Eur J Oper Res. https://doi.org/10.1016/j.ejor.2016.04.059

    Article  MathSciNet  MATH  Google Scholar 

  18. Mukhina K, Visheratin A, Nasonov D (2019) Orienteering problem with functional profits for multi-source dynamic path construction. PLoS ONE 14(4):e0213777. https://doi.org/10.1371/journal.pone.0213777

    Article  Google Scholar 

  19. Hanafi S, Mansini R, Zanotti R (2019) The multi-visit team orienteering problem with precedence constraints. In: European journal of operational research (In Press)

  20. Yu V, Redi A, Jewpanya P, Gunawan A (2019) Selective discrete particle swarm optimization for the team orienteering problem with time windows and partial scores. Comput Ind Eng. https://doi.org/10.1016/j.cie.2019.106084

    Article  Google Scholar 

  21. Schilde M, Doerner KF, Hartl RF, Kiechle G (2009) Metaheuristics for the bi-objective orienteering problem. Swarm Intelligence. 3(3):179–201. https://doi.org/10.1007/s11721-009-0029-5

    Article  Google Scholar 

  22. Chen YH, Sun WJ, Chiang TC (2015) Multiobjective orienteering problem with time windows: An ant colony optimization algorithm. In: 2015 conference on technologies and applications of artificial intelligence, pp 128–135 (TAAI) https://doi.org/10.13140/rg.2.1.2461.3849

  23. Mei Y, Salim F, Li X (2016) Efficient meta-heuristics for the multi-objective time-dependent orienteering problem. Eur J Oper Res. https://doi.org/10.1016/j.ejor.2016.03.053

    Article  MathSciNet  MATH  Google Scholar 

  24. Yu V, Jewpanya P, Yang ZY, Redi P, Agus Y, Idrakarna P (2017) Solving the multi-objective orienteering problem with time windows using simulated annealing. In: Proceedings of the international conference on innovation and management 2017, Tokyo, Japan

  25. Wang J, Guo J, Zheng M, Wang Z, Li Z (2018) Uncertain multiobjective orienteering problem and its application to UAV reconnaissance mission planning. J Intell Fuzzy Syst 34(4):2287–2299. https://doi.org/10.3233/JIFS-171331

    Article  Google Scholar 

  26. Hu W, Fathi M, Pardalos P (2018) A multi-objective evolutionary algorithm based on decomposition and constraint programming for the multi-objective team orienteering problem with time windows. Appl Soft Comput 73:383–393

    Article  Google Scholar 

  27. Hapsari I, Surjandari I, Komarudin KJ, Int Ind Eng (2019) Solving multi-objective team orienteering problem with time windows using adjustment iterated local search. J Ind Eng Int 15(4):679–693. https://doi.org/10.1007/s40092-019-0315-9

    Article  Google Scholar 

  28. Yahiaoui AE, Moukrim A, Serairi M (2017) Hybrid Heuristic for the clustered orienteering problem. In: Bektaş T, Coniglio S, Martinez-Sykora A, Voß S. (eds) Computational logistics. ICCL 2017. Lecture Notes in Computer Science, Springer, Cham, vol 10572, pp 19–33. https://doi.org/10.1007/978-3-319-68496-3_2

  29. Álvarez-Miranda E, Luipersbeck M, Sinnl M (2017) Gotta (efficiently) catch them all: pokémon GO meets orienteering problems. Eur J Oper Res. https://doi.org/10.1016/j.ejor.2017.08.012

    Article  MATH  Google Scholar 

  30. Deb K, Agrawal S, Pratap A, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197

    Article  Google Scholar 

  31. Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength pareto evolutionary Algorithm. TIK-Report, p 103

  32. Goldberg D, Lingle R (1985) Alleles, Loci and the traveling salesman problem. In: Proceedings of the 1st international conference on genetic algorithms and their applications, Los Angeles, USA, pp 154–159. https://doi.org/10.1155/2017/7430125

  33. Veldhuizen DA, Lamont GB (1998) Multiobjective evolutionary algorithm research: a history and analysis. Technical Report TR-98-03, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright Paterson, AFB, OH

  34. Zhou A, Jin Y, Zhang Q, Sendho B, Tsang E (2006) Combining model-based and genetics-based offspring generation for multiobjective optimization using a convergence criterion. In: 2006 IEEE congress on evolutionary computation (Sheraton Vancouver Wall Center Vancouver, BC, Canada, pp 3234–3241

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, NSHM Knowledge Campus, Durgapur, West Bengal, 713212, India

    Joydeep Dutta & Partha Sarathi Barma

  2. Department of Mathematics, NIT Durgapur, Durgapur, West Bengal, 713209, India

    Anupam Mukherjee & Samarjit Kar

  3. Department of Computer Science and Engineering, NIT Durgapur, Durgapur, West Bengal, 713212, India

    Tanmay De

Authors
  1. Joydeep Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Partha Sarathi Barma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anupam Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samarjit Kar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tanmay De
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samarjit Kar.

Ethics declarations

Conflict of interest

All authors of this research paper declare that there is no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all participants included in the study.

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

Dutta, J., Barma, P.S., Mukherjee, A. et al. A multi-objective open set orienteering problem. Neural Comput & Applic 32, 13953–13969 (2020). https://doi.org/10.1007/s00521-020-04798-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04798-7

Keywords