3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 Presentation ARRIVAL – WP3 Algorithms for Robust and online Railway optimization: Improving the Validity and realiAbility of Large scale systems WP3: Robust and Online Timetabling and Timetable Information Updating Matteo Fischetti (WP3 leader) DEI, University of Padova Matteo Fischetti

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 PresentationMatteo Fischetti 2 WP3 – Participants CTI UniKarl EUR ULA TUB UniBo DEI UPVLC SNCF

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 PresentationMatteo Fischetti 3 Problem Areas Robust and on-line timetable design –Find a period or aperiodic train timetable (& platforming) –Maximize the timetable efficiency and reliability –Improve timetable robustness against train delays –Online (real-time) timetable updates after major disruptions General MIP solution techniques –MIP models often used to design timetables –Develop improved MIP solution techniques Timetable information updating –Modeling the timetable information efficiently –New speedup techniques and fundamental data structures to support fast query answering

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 PresentationMatteo Fischetti 4 Main Achievements during the 3rd year –Evaluation of new general models for dealing with uncertain data (light robustness & recoverable robustness) –Integration between robust timetabling planning and delay management policies –Evaluation of heuristic methods for solving online train timetabling problems, and real-time tools to assists railway operators –Efficient data structures and algorithms for efficient answering of shortest path queries and updating in very large networks –Enhancing the performance of MIP solvers by improving the quality of generated cuts and of heuristics used

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 PresentationMatteo Fischetti 5 Recommendation from 2nd review & Actions taken Effectiveness of new MIP techniques evaluated on railways instances (as recommended by the referees) and reported in TR-0237 and D6.3 No significant deviation from the WP3 workplan occurred in the third year

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 Presentation 6 Fast timetable robustness improvement ‏ Problem: optimized timetables might be too sensitive to disturbances need to adjust a given optimal timetable to be robust (allowing for some efficiency loss) ‏ Goal: To find a fast (yet accurate) algorithm to improve the robustness of a timetable Testing framework: Matteo Fischetti

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 Presentation 7 Fast timetable robustness improvement Common assumptions for “robustness training” methods: Allow for some percentage efficiency loss Limit the set of planning actions (good for small disturbances, leads to more tractable models) => add buffer times ( = stretch travel times) Robustness training methods tested: Unif.: uniform allocation of buffer times (e.g. 7% nominal travel time) ‏ Fat: scenario-based stochastic programming formulation, aiming at minimizing expected delay Slim: heuristic version of Fat leading to a more tractable MIP formulation LR: Light Robustness (ARRIVAL TM ) Matteo Fischetti

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 Presentation 8 Fast timetable robustness improvement Results (10% efficiency loss w.r.t. the input timetable): (*) ‏ Unif. is very fast but is the worst in terms of robustness Fat achieves the best robustness but is very slow LR is a good compromise between robusteness and performances (~1000x faster than Fat) ‏ (*) average on 4 real congested corridors from Italian railway company Matteo Fischetti

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 Presentation Robust Platforming Platforming: For a set of trains over time in a station assign conflict-free: –Platforms –Arrival and departure paths Disturbances: –Trains arriving late at the station area –Prolongated stop & boarding may delay departure Matteo Fischetti 9

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 Presentation Robust Platforming Goal: –Keep throughput maximal –Minimize propagated delay Possible approaches: –Classical robust optimization –Application-specific state-of-the-art heuristics –General-purpose method of recoverable robustness (ARRIVAL TM)  Robust Network Buffering Matteo Fischetti 10 Over-conservative!

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 Presentation Comparison Matteo Fischetti % - 25 % delay over the day by using Recoverable Robustness Time Maximal Propagated Delay in min

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 Presentation Improved MIP techniques Railways problems are often modelled as difficult MIPs  even finding a feasible solution may be very challenging In practice, a sound heuristic may be the only option Feasibility Pump (FP) is a recently proposed heuristic embedded in most commercial/free MIP solvers (Cplex, CBC, Xpress, GLPK, etc.) New FP version (FP 2.0) developed within the ARRIVAL project by using Constraint Programming propagation techniques inside the standard FP shell Improved performance for both the success rate (ability of finding any feasible solution) and the solution quality (average optimality gap w.r.t. best-known sol. reduced from 77% to 35% on a large MIPLIB testbed) Successfully evaluated on specific MIP instances from different railways applications (timetable, crew scheduling, etc.) Matteo Fischetti 12

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 PresentationMatteo Fischetti 13 D3.6:Improved Algorithms for Robust and Online Timetabling and for Timetable Information Updating D3.5:New Methods for Robust Timetabling Involving Stochasticity Journals and Chapters in Books: Technical Reports: Deliverables & Publications Conferences: 34

3rd ARRIVAL Review Meeting [Patras, 12 May 2009] – WP3 PresentationMatteo Fischetti 14 WP3 - Effort Total 3 years 1 st plan 1 st actual 1 st own 2 nd plan 2 nd actual 2 nd own CTI UniKarl EUR ULA TUB UniBo DEI UPVLC SNCF Total rd year plan 3 rd year actual 3 rd year own