{"type":"ScholarlyArticle","title":"Linear-Time Optimal Deadlock Detection for Efficient Scheduling in Multi-Track Railway Networks","authors":[{"name":"Hastyn Doshi","slug":"hastyn-doshi","given":"Hastyn","family":"Doshi","affiliation":"Department of Computer Science and Engineering , IIT Bombay"},{"name":"Ayush Tripathi","slug":"ayush-tripathi","given":"Ayush","family":"Tripathi","affiliation":"Department of Computer Science and Engineering , IIT Bombay"},{"name":"Keshav Agarwal","slug":"keshav-agarwal","given":"Keshav","family":"Agarwal","affiliation":"Department of Computer Science and Engineering , IIT Bombay"},{"name":"Harshad Khadilkar","slug":"harshad-khadilkar","given":"Harshad","family":"Khadilkar","affiliation":"Department of Computer Science and Engineering , IIT Bombay"},{"name":"Shivaram Kalyanakrishnan","slug":"shivaram-kalyanakrishnan","given":"Shivaram","family":"Kalyanakrishnan","affiliation":"Department of Computer Science and Engineering , IIT Bombay"}],"abstract":"The railway scheduling problem requires the computation of an operable timetable that satisfies constraints involving railway infrastructure and resource occupancy times, while minimising average delay over a set of events. Since this problem is computationally hard, practical solutions typically roll out feasible (but suboptimal) schedules one step at a time, by choosing which train to move next in every step. The choices made by such algorithms are necessarily myopic, and incur the risk of driving the system to a deadlock. To escape deadlocks, the predominant approach is to stay away from states flagged as potentially unsafe by some fast-to-compute rule R. While many choices of R guarantee deadlock avoidance, they are suboptimal in the sense of also flagging some safe states as unsafe. In this paper, we revisit the literature on process scheduling and describe a rule R0 that is (i) necessary and sufficient for deadlock detection when the network has at least two tracks in each  resource (station / track section), (ii) computable in linear time, and (iii) yields lower delays when combined with existing scheduling algorithms on both synthetic and real data sets from Indian Railways.","doi":"10.24963/ijcai.2024/641","url":"https://ijcai.org/proceedings/2024/641","pdf":"https://ijcai.org/proceedings/2024/0641.pdf","markdown":"https://ijcai.org/proceedings/2024/641.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/641","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":641,"pages":{"first":5799,"last":5807},"keywords":["Multidisciplinary Topics and Applications: MTA: Transportation","Planning and Scheduling: PS: Applications","Planning and Scheduling: PS: Markov decisions processes","Planning and Scheduling: PS: Scheduling"],"videos":[],"references":[{"position":1,"raw":"[Agarwal, 2022] Keshav Agarwal. Real-time railway scheduling. Master's thesis, Indian Institute of Technol- ogy Bombay, 2022.","title":"Real-time railway scheduling","authors":"Keshav Agarwal","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Araki et al., 1977] Toshiro Araki, Yuji Sugiyama, Tadao Kasami, and Jun Okui. Complexity of the deadlock avoid- ance problem. In 2nd IBM Symp. on Mathematical Foun- dations of Computer Science, pages 229-257, 1977.","title":"Complexity of the deadlock avoidance problem","authors":"Araki","year":"1977","venue":"2nd IBM Symp. on Mathematical Foundations of Computer Science","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cai and Goh, 1994] X Cai and C Goh. A fast heuristic for the train scheduling problem. Computers & Op. Res., 21(5):499-510, 1994.","title":"A fast heuristic for the train scheduling problem","authors":"Goh ; X Cai, C Cai, Goh","year":"1994","venue":"Computers & Op. Res","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2015] L Chen, C Roberts, F Schmid, and E Stewart. Modeling and solving real-time train reschedul- ing problems in railway bottleneck sections. IEEE Trans. on Intelligent Transportation Systems, 16(4):1896-1904, 2015.","title":"Modeling and solving real-time train rescheduling problems in railway bottleneck sections","authors":"C Chen, F Roberts, Schmid, Stewart","year":"2015","venue":"IEEE Trans. on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cocco and Monasson, 2001] Simona Cocco and Rémi Monasson. Trajectories in phase diagrams, growth processes, and computational complexity: How search algorithms solve the 3-satisfiability problem. Physical review letters, 86(8):1654, 2001.","title":"Trajectories in phase diagrams, growth processes, and computational complexity: How search algorithms solve the 3-satisfiability problem","authors":"Simona Cocco, Rémi Monasson","year":"2001","venue":"Physical review letters","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Fanti et al., 1997] Maria Pia Fanti, Bruno Maione, Saverio Mascolo, and A Turchiano. Event-based feedback con- trol for deadlock avoidance in flexible production sys- tems. IEEE Transactions on Robotics and Automation, 13(3):347-363, 1997.","title":"Event-based feedback control for deadlock avoidance in flexible production systems","authors":"Pia Maria, Bruno Fanti, Saverio Maione, Mascolo, Turchiano","year":"1997","venue":"IEEE Transactions on Robotics and Automation","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gold, 1978] E Mark Gold. Deadlock prediction: Easy and difficult cases. SIAM Journal on Computing, 7(3):320- 336, 1978.","title":"Deadlock prediction: Easy and difficult cases","authors":"Mark Gold","year":"1978","venue":"SIAM Journal on Computing","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Goverde, 2010] Rob MP Goverde. A delay propaga- tion algorithm for large-scale railway traffic networks. Transportation Research Part C: Emerging Technologies, 18(3):269-287, 2010.","title":"A delay propagation algorithm for large-scale railway traffic networks","authors":"M P Rob, Goverde","year":"2010","venue":"Transportation Research Part C: Emerging Technologies","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Higgins et al., 1996] A Higgins, E Kozan, and L Ferreira. Optimal scheduling of trains on a single line track. Trans- portation Research Part B, 30(2):147-161, 1996.","title":"Optimal scheduling of trains on a single line track","authors":"E Higgins, L Kozan, Ferreira","year":"1996","venue":"Transportation Research Part B","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Higgins et al., 1997] A Higgins, E Kozan, and L Ferreira. Heuristic techniques for single line train scheduling. Jour- nal of Heuristics, 3(1):43-62, 1997.","title":"Heuristic techniques for single line train scheduling","authors":"E Higgins, L Kozan, Ferreira","year":"1997","venue":"Journal of Heuristics","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Khadilkar, 2017a] Harshad Khadilkar. Data-enabled stochastic modeling for evaluating schedule robust- ness of railway networks. Transportation Science, 51(4):1161-1176, 2017.","title":"Data-enabled stochastic modeling for evaluating schedule robustness of railway networks","authors":"Harshad Khadilkar, Khadilkar","year":"2017","venue":"Transportation Science","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Khadilkar, 2017b] Harshad Khadilkar. Scheduling of vehi- cle movement in resource-constrained transportation net- works using a capacity-aware heuristic. In 2017 Ameri- can Control Conference (ACC), pages 5617-5622. IEEE, 2017.","title":"Scheduling of vehicle movement in resource-constrained transportation networks using a capacity-aware heuristic","authors":"Harshad Khadilkar, Khadilkar","year":"2017","venue":"2017 American Control Conference (ACC)","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Khadilkar, 2018] Harshad Khadilkar. A scalable reinforce- ment learning algorithm for scheduling railway lines. IEEE Transactions on Intelligent Transportation Systems, 20(2):727-736, 2018.","title":"A scalable reinforcement learning algorithm for scheduling railway lines","authors":"Harshad Khadilkar","year":"2018","venue":"IEEE Transactions on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu and Kozan, 2009] S Liu and E Kozan. Scheduling trains as a blocking parallel-machine job shop scheduling prob- lem. Computers & Operations Research, 36(10):2840- 2852, 2009.","title":"Scheduling trains as a blocking parallel-machine job shop scheduling problem","authors":"Kozan ; ] S Liu, E Liu, Kozan","year":"2009","venue":"Computers & Operations Research","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Lovett et al., 2015] Alexander H Lovett, C Tyler Dick, and Christopher PL Barkan. Determining freight train delay costs on railroad lines in north america. Proceedings of Rail Tokyo, 2015.","title":"Determining freight train delay costs on railroad lines in north america","authors":"C Alexander H Lovett, Tyler Dick, Christopher Pl Barkan","year":"2015","venue":"Proceedings of Rail Tokyo","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Mackenzie, 2010] S. Mackenzie. Train scheduling on long haul railway corridors. PhD thesis, University of South Australia, 2010.","title":"Train scheduling on long haul railway corridors","authors":"S Mackenzie","year":"2010","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Manne, 1960] Alan S Manne. On the job-shop scheduling problem. Operations research, 8(2):219-223, 1960.","title":"On the job-shop scheduling problem","authors":"Alan S Manne","year":"1960","venue":"Operations research","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Mascis and Pacciarelli, 2002] Alessandro Mascis and Dario Pacciarelli. Job-shop scheduling with blocking and no- wait constraints. European Journal of Operational Re- search, 143(3):498-517, 2002.","title":"Job-shop scheduling with blocking and nowait constraints","authors":"Alessandro Mascis, Dario Pacciarelli","year":"2002","venue":"European Journal of Operational Research","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Pachl, 2012] Jörn Pachl. Deadlock avoidance in railroad op- erations simulations. In PROMET Traffic & Transporta- tion, pages 359-369, 2012.","title":"Deadlock avoidance in railroad operations simulations","authors":"Jörn Pachl, Pachl","year":"2012","venue":"PROMET Traffic & Transportation","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Pappaterra et al., 2021] Mauro José Pappaterra, Francesco Flammini, Valeria Vittorini, and Nikola Bešinović. A sys- tematic review of artificial intelligence public datasets for railway applications. Infrastructures, 6(10):136, 2021.","title":"A systematic review of artificial intelligence public datasets for railway applications","authors":"Mauro José Pappaterra, Francesco Flammini, Valeria Vittorini, Nikola Bešinović","year":"2021","venue":"Infrastructures","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Prasad et al., 2021] Rohit Prasad, Harshad Khadilkar, and Shivaram Kalyanakrishnan. Optimising a real-time sched- uler for Indian railway lines by policy search. In 2021 Seventh Indian Control Conference (ICC), pages 75-80. IEEE, 2021.","title":"Optimising a real-time scheduler for Indian railway lines by policy search","authors":"Rohit Prasad, Harshad Khadilkar, Shivaram Kalyanakrishnan","year":"2021","venue":"2021 Seventh Indian Control Conference (ICC)","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Preston et al., 2009] John Preston, Graham Wall, Richard Batley, J Nicolás Ibáñez, and Jeremy Shires. Impact of delays on passenger train services: evidence from great britain. Transportation research record, 2117(1):14-23, 2009.","title":"Impact of delays on passenger train services: evidence from great britain","authors":"John Preston, Graham Wall, Richard Batley, J Nicolás Ibáñez, Jeremy Shires","year":"2009","venue":"Transportation research record","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Reveliotis et al., 1997] Spiridon A Reveliotis, Mark A Law- ley, and Placid M Ferreira. Polynomial-complexity dead- lock avoidance policies for sequential resource alloca- tion systems. IEEE transactions on automatic control, 42(10):1344-1357, 1997.","title":"Polynomial-complexity deadlock avoidance policies for sequential resource allocation systems","authors":"Mark A Spiridon A Reveliotis, Placid M Lawley, Ferreira","year":"1997","venue":"IEEE transactions on automatic control","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Russell and Norvig, 2022] Stuart Russell and Peter Norvig. Artificial intelligence : a Modern Approach. Pearson Ed- ucation, 4th edition, 2022.","title":"Artificial intelligence : a Modern Approach","authors":"Stuart Russell, Peter Norvig","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Schlake et al., 2011] Bryan W Schlake, Christopher PL Barkan, and J Riley Edwards. Train delay and economic impact of in-service failures of railroad rolling stock. Transportation research record, 2261(1):124-133, 2011.","title":"Train delay and economic impact of in-service failures of railroad rolling stock","authors":"Christopher Bryan W Schlake, J Riley Pl Barkan, Edwards","year":"2011","venue":"Transportation research record","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Šemrov et al., 2016] Darja Šemrov, Rok Marsetič, Marijan Žura, Ljupčo Todorovski, and Aleksander Srdic. Rein- forcement learning approach for train rescheduling on a single-track railway. Trans. Res. Part B: Methodological, 86:250-267, 2016.","title":"Reinforcement learning approach for train rescheduling on a single-track railway","authors":"Darja Šemrov, Rok Marsetič, Marijan Žura, Ljupčo Todorovski, Aleksander Srdic","year":"2016","venue":"Trans. Res. Part B: Methodological","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Sinha et al., 2016] Sudhir Kumar Sinha, Shripad Salsingikar, and Siddhartha SenGupta. An iterative bi-level hierarchical approach for train scheduling. Journal of Rail Transport Planning & Management, 6(3):183-199, 2016.","title":"An iterative bi-level hierarchical approach for train scheduling","authors":"Sudhir Kumar Sinha, Shripad Salsingikar, Siddhartha Sengupta","year":"2016","venue":"Journal of Rail Transport Planning & Management","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Strotmann, 2007] Christian Strotmann. Railway scheduling problems and their decomposition. PhD thesis, Univ. Os- nabrück, 2007.","title":"Railway scheduling problems and their decomposition","authors":"Christian Strotmann","year":"2007","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Tesauro and Galperin, 1996] Gerald Tesauro and Gregory Galperin. On-line policy improvement using monte-carlo search. Advances in Neural Information Processing Sys- tems, 9, 1996.","title":"On-line policy improvement using monte-carlo search","authors":"Galperin Tesauro, Gerald Tesauro, Gregory Galperin","year":"1996","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Törnquist and Persson, 2007] Johanna Törnquist and Jan A Persson. N-tracked railway traffic re-scheduling during disturbances. Transportation Research Part B: Method- ological, 41(3):342-362, 2007.","title":"Johanna Törnquist and Jan A Persson. N-tracked railway traffic re-scheduling during disturbances","authors":"Persson Törnquist","year":"2007","venue":"Transportation Research Part B: Methodological","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Vujanic and Hill, 2022] Robin Vujanic and Andrew J Hill. Computationally efficient dynamic traffic optimization of railway systems. IEEE Transactions on Intelligent Trans- portation Systems, 23(5):4706-4719, 2022.","title":"Computationally efficient dynamic traffic optimization of railway systems","authors":"Hill ; Vujanic, Robin Vujanic, Andrew J Hill","year":"2022","venue":"IEEE Transactions on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}