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Optimal Scheduling for Delay Management in Railway Network Using Hybrid Bat Algorithm

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Intelligent Computing in Control and Communication

Abstract

This paper aims to solve the timetable rescheduling problem of a railway network to minimize the total delay in journey time of trains under any disastrous situation. Here, multiple tracks and platforms are taken into account to dynamically update the timetable. A Bat Algorithm (BA) based meta-heuristic approach is proposed to solve it. The results obtained from the proposed approach depicts that it can readily solve such problems in an efficient and optimized way. Moreover, the comparative study shows that this approach outperforms the existing method in this domain.

Supported by BIT Sindri.

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References

  1. Abid MM, Khan MB (2015) Sensitivity analysis of train schedule of a railway track network using an optimization modeling technique. Eur Transp Res Rev 7:2–7

    Article  Google Scholar 

  2. Acuna-Agost R, Michelon PcDF, Gueye S (2011) Sapi: statistical analysis of propagation of incidents. a new approach for rescheduling trains after disruptions. Eur J Oper Res 215:227–243

    Google Scholar 

  3. Alwadood Z, Shuib A, Hamid NA (2012) A review on quantitative models in railway rescheduling. Int J Sci Eng Res 3:1–7

    Google Scholar 

  4. Beasley JE, Krishnamoorthy M, Sharaiha YM, Abramson D (2000) Scheduling aircraft landings-the static case. Transp Sci 34(2):180–197

    Article  Google Scholar 

  5. Beg S, Khan A, Nauman U, Mohsin S (2011) Performance evaluation of bionomic algorithm (ba) in comparison with genetic algorithm (ga) for shortest path finding problem. Int J Comput Sci Issues (IJCSI) 8(6):238

    Google Scholar 

  6. Brentnall AR, Cheng RC (2009) Some effects of aircraft arrival sequence algorithms. J Oper Res Soc 60(7):962–972

    Article  Google Scholar 

  7. Corman F, D’Ariano A, Marra AD, Pacciarelli D, Samà M (2017) Integrating train scheduling and delay management in real-time railway traffic control. Transp Res Part E Logist Transp Rev 105:213–239

    Article  Google Scholar 

  8. D’Ariano A, Pacciarelli D, Sama M, Corman F (2017) Microscopic delay management: minimizing train delays and passenger travel times during real-time railway traffic control. In: 2017 5th IEEE international conference on models and technologies for intelligent transportation systems (MT-ITS). IEEE, pp 309–314

    Google Scholar 

  9. Eaton J, Yang S, Gongora M (2017) Ant colony optimization for simulated dynamic multi-objective railway junction rescheduling. IEEE Trans Intell Transp Syst 18(11):2980–2992

    Article  Google Scholar 

  10. Eaton J, Yang S, Mavrovouniotis M (2016) Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays. Soft Comput 20(8):2951–2966

    Article  Google Scholar 

  11. Gafarov ER, Dolgui A, Lazarev AA (2015) Two-station single-track railway scheduling problem with trains of equal speed. Comput Indus Eng 85:260–267

    Article  Google Scholar 

  12. Gaied M, Lefebvre D, M’halla A, Othmen KB (2018) Modelling and performance evaluation of railway transport systems using p-timed petri nets. In: 2018 5th international conference on control, decision and information technologies (CoDIT). IEEE, pp 841–846

    Google Scholar 

  13. Gandomi AH, Yang XS, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255

    Article  Google Scholar 

  14. Giglio D, Sacco N (2016) A petri net model for analysis, optimisation, and control of railway networks and train schedules. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC). IEEE, pp 2442–2449

    Google Scholar 

  15. Hancerliogullari G, Rabadi G, Al-Salem AH, Kharbeche M (2013) Greedy algorithms and metaheuristics for a multiple runway combined arrival-departure aircraft sequencing problem. J Air Transp Manage 32:39–48

    Article  Google Scholar 

  16. Hassan GM, Reynolds M (2018) Genetic algorithms for scheduling and optimization of ore train networks. In: GCAI, pp 81–92

    Google Scholar 

  17. He D, Lu G, Yang Y (2019) Research on optimization of train energy-saving based on improved chicken swarm optimization. IEEE Access 7:121675–121684

    Article  Google Scholar 

  18. Kersbergen B, van den Boom T, Schutter BD (2013) Reducing the time needed to solve the global rescheduling problem for railway networks. In: Proceedings of the 16th international IEEE annual conference on intelligent transportation systems (ITSC 2013), pp 791–796

    Google Scholar 

  19. Li X, Shou B, Ralescu D (2014) Train rescheduling with stochastic recovery time: a new track-backup approach. IEEE Trans Syst Man Cybern Syst 44(9):1216–1233

    Article  Google Scholar 

  20. Salehipour A, Modarres M, Naeni LM (2013) An efficient hybrid meta-heuristic for aircraft landing problem. Comput Operat Res 40(1):207–213

    Article  MathSciNet  Google Scholar 

  21. Taha A, Hachimi M, Moudden A (2017) A discrete bat algorithm for the vehicle routing problem with time windows. In: 2017 international colloquium on logistics and supply chain management (LOGISTIQUA). IEEE, pp 65–70

    Google Scholar 

  22. Talal R (2014) Comparative study between the (ba) algorithm and (pso) algorithm to train (rbf) network at data classification. Int J Comput Appl 92(5):16–22

    Google Scholar 

  23. Wang G, Guo L, Duan H, Liu L, Wang H (2012) A bat algorithm with mutation for UCAV path planning. Sci World J

    Google Scholar 

  24. Xie J, Zhou Y, Chen H (2013) A novel bat algorithm based on differential operator and lévy flights trajectory. Comput Intell Neurosci

    Google Scholar 

  25. Xie J, Zhou Y, Zheng H (2013) A hybrid metaheuristic for multiple runways aircraft landing problem based on bat algorithm. J Appl Math

    Google Scholar 

  26. Yaman O, Karakose E, Karakose M (2018) PSO based traffic optimization approach for railway networks. In: 2018 international conference on artificial intelligence and data processing (IDAP). IEEE, pp 1–4

    Google Scholar 

  27. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, pp 65–74

    Google Scholar 

  28. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput

    Google Scholar 

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Acknowledgements

This research work is a part of the project [Project Id No.: 1-5728195821] named “Multi-Agent Based Modelling for Collision Handling and Delay Optimization in Indian Railway System” and funded by MHRD and World-bank under TEQIP Collaborative Research Scheme, Govt. of India.

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Correspondence to Poulami Dalapati .

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Dalapati, P., Paul, K. (2021). Optimal Scheduling for Delay Management in Railway Network Using Hybrid Bat Algorithm. In: Sekhar, G.C., Behera, H.S., Nayak, J., Naik, B., Pelusi, D. (eds) Intelligent Computing in Control and Communication. Lecture Notes in Electrical Engineering, vol 702. Springer, Singapore. https://doi.org/10.1007/978-981-15-8439-8_8

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  • DOI: https://doi.org/10.1007/978-981-15-8439-8_8

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  • Print ISBN: 978-981-15-8438-1

  • Online ISBN: 978-981-15-8439-8

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