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Fuzzy logic based multi-objective optimization of a multi-agent transit control system

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Abstract

This paper models a transit control system for the management of traffic perturbations of public transport. The transit system data is voluminous and highly dynamic. Moreover, the transit domain has a remarkable lack of intelligent systems to monitor and maintain better performance. Consequently, realizing an intelligent transit control system has become a consistent need. The modeling of the system addresses a problem of optimizing performance measures based on key performance indicators. Its objective is to find the optimal control action in disturbance cases. The solution consists in combining all performance measures in a single measure by using fuzzification without neglecting the space and time requirements of the traffic. To model and implement our system we used a multi-agent approach. The experiments performed were based on real network traffic data. The obtained results demonstrate the relevance of the proposed fuzzy approach in our optimization problem and show the advantage of the multi-agent system in the modeling of our control system. We prove that the proposed control system achieves better results than certain existing fuzzy approaches and is able to manage disturbances with a better performance than the existing solutions.

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References

  1. Commission E (2011) Roadmap to a single european transport area: Towards a competitive and resource efficient transport system: White paper. Publications Office of the European Union

  2. Yan X, Crookes RJ (2009) Reduction potentials of energy demand and ghg emissions in china’s road transport sector. Energy Policy 37(2):658–668

    Article  Google Scholar 

  3. Ceder A (2016) Public transit planning and operation: modeling, practice and behavior. CRC Press, Florida

    Book  Google Scholar 

  4. Morri N, El Hadouaj S, Said LB (2020) Intelligent regulation system to optimize the service performance of the public transport. In: ICEIS, vol 1. pp 416–427

  5. Morri N, Hadouaj S, Said LB (2015) Multi-agent optimization model for multi-criteria regulation of multi-modal public transport. In: 2015 World congress on information technology and computer applications (WCITCA). IEEE, pp 1–6

  6. d’Arcier BF, Bouteiller C, Ippoliti L, Regouby R, Khomenko V, Grimberg M (2012) Mesure de la performance des lignes de transport public urbain. Projet aperol: amélioration de la performance économique des réseaux par l’optimisation des lignes. rapport final. PhD thesis, LET

  7. Nagurney A, Zhang W-B (2007) Mathematical models of transportation and networks. Math Models Econ 2:346–384

    Google Scholar 

  8. Miettinen K (2001) Some methods for nonlinear multi-objective optimization. International conference on evolutionary multi-criterion optimization. Springer, New York, pp 1–20

    Google Scholar 

  9. Zeleny M (1973) Compromise programming. Multiple criteria decision making

  10. Fishburn PC (1974) Exceptional paper-lexicographic orders, utilities and decision rules: a survey. Manag Sci 20(11):1442–1471

    Article  MATH  Google Scholar 

  11. Chang C-T (2007) Multi-choice goal programming. Omega 35(4):389–396

    Article  Google Scholar 

  12. Charnes A, Clower R, Kortanek K (1967) Effective control through coherent decentralization with preemptive goals. Econ J Econ Soc 294–320

  13. Gunantara N (2018) A review of multi-objective optimization: methods and its applications. Cogent Eng 5(1):1502242

    Article  Google Scholar 

  14. Emmerich MT, Deutz AH (2018) A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Nat Comput 17(3):585–609

    Article  MathSciNet  Google Scholar 

  15. Thu Bui L, Alam S (2008) Multi-objective optimization in computational intelligence: theory and practice: theory and practice. IGI Global, Hershey

    Google Scholar 

  16. Ehrgott M (2008) Multiobjective optimization. AI Mag 29(4):47

    Google Scholar 

  17. Miettinen K (2012) Nonlinear multiobjective optimization, vol 12. Springer, New York

    MATH  Google Scholar 

  18. Zhang X, Wang H, Stojanovic V, Cheng P, He S, Luan X, Liu F (2021) Asynchronous fault detection for interval type-2 fuzzy nonhomogeneous higher-level markov jump systems with uncertain transition probabilities. IEEE Trans Fuzzy Syst

  19. Cheng P, Wang H, Stojanovic V, He S, Shi K, Luan X, Liu F, Sun C (2021) Asynchronous fault detection observer for 2-d markov jump systems. IEEE Trans Cybern

  20. Cohon JL (2004) Multiobjective programming and planning, vol 140. Courier Corporation, Massachusetts

    MATH  Google Scholar 

  21. Das I, Dennis JE (1997) A closer look at drawbacks of minimizing weighted sums of objectives for pareto set generation in multicriteria optimization problems. Struct Optim 14(1):63–69

    Article  Google Scholar 

  22. Murata T, Ishibuchi H, Tanaka H (1996) Multi-objective genetic algorithm and its applications to flowshop scheduling. Comput Ind Eng 30(4):957–968

    Article  Google Scholar 

  23. Jia J, Fischer GW, Dyer JS (1998) Attribute weighting methods and decision quality in the presence of response error: a simulation study. J Behav Decis Mak 11(2):85–105

    Article  Google Scholar 

  24. Dawes RM, Corrigan B (1974) Linear models in decision making. Psychol Bull 81(2):95

    Article  Google Scholar 

  25. Einhorn HJ, McCoach W (1977) A simple multiattribute utility procedure for evaluation. Behav Sci 22(4):270–282

    Article  MATH  Google Scholar 

  26. Yalcin GD, Erginel N (2011) Determining weights in multi-objective linear programming under fuzziness. In: Proceedings of the world congress on engineering, vol 2. pp 6–8

  27. Lin C-C (2004) A weighted max-min model for fuzzy goal programming. Fuzzy Sets Syst 142(3):407–420

    Article  MathSciNet  MATH  Google Scholar 

  28. Li X-Q, Zhang B, Li H (2006) Computing efficient solutions to fuzzy multiple objective linear programming problems. Fuzzy Sets Syst 157(10):1328–1332

    Article  MathSciNet  MATH  Google Scholar 

  29. Tiwari R, Dharmar S, Rao J (1987) Fuzzy goal programming-an additive model. Fuzzy Sets Syst 24(1):27–34

    Article  MathSciNet  MATH  Google Scholar 

  30. Naaz S, Alam A, Biswas R (2011) Effect of different defuzzification methods in a fuzzy based load balancing application. Int J Comput Sci Issues (IJCSI) 8(5):261

    Google Scholar 

  31. Turoff M, Linstone HA (2002) The delphi method-techniques and applications

  32. Saberi M, Zockaie AK, Fang W, El-Geneidy A (2013) Definition and properties of alternative bus service reliability measures at the stop level

  33. Yachba K, Bendaoud Z, Bouamrane K (2018) Toward a decision support system for regulation in an urban transport network. Int J Strateg Inf Technol Appl (IJSITA) 9(2):1–17

    Article  Google Scholar 

  34. Blackburn P, van Benthem JF, Wolter F (2006) Handbook of modal logic. Elsevier, Amsterdam

    MATH  Google Scholar 

  35. Banihashemi M, Haghani A (2000) Optimization model for large-scale bus transit scheduling problems. Transp Res Rec 1733(1):23–30

    Article  Google Scholar 

  36. Aho AV, Ullman JD (1992) Foundations of computer science. Computer Science Press Inc

  37. Chivers I, Sleightholme J (2015) An introduction to algorithms and the big o notation. Introduction to programming with fortran. Springer, New York, pp 359–364

    Chapter  MATH  Google Scholar 

  38. Zhou X, Wang Y, Ji X, Cottrill C (2019) Coordinated control strategy for multi-line bus bunching in common corridors. Sustainability 11(22):6221

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Author notes

  1. Sameh Hadouaj and Lamjed Ben Said have been contributed equally to this work.

Authors and Affiliations

  1. BIT Departement, Liwa College of Technology, Baniyas Tower, Abu Dhabi, 41009, United Arab Emirates

    Nabil Morri & Lamjed Ben Said

  2. CIS Departement, Higher Colleges of Technology, Ibn Battuta Gate Building Sheikh Zayed Road, Abu Dhabi, 53735, United Arab Emirates

    Sameh Hadouaj & Lamjed Ben Said

  3. SMART Lab, Univetsite de Tunis, Bouchoucha Bardo, 2000, Tunis, Tunisia

    Nabil Morri & Sameh Hadouaj

Authors
  1. Nabil Morri
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  2. Sameh Hadouaj
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  3. Lamjed Ben Said
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Correspondence to Nabil Morri.

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Morri, N., Hadouaj, S. & Said, L.B. Fuzzy logic based multi-objective optimization of a multi-agent transit control system. Memetic Comp. 15, 71–87 (2023). https://doi.org/10.1007/s12293-022-00384-7

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