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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Innovations in Automated Planning and Scheduling 1st workshop of the EC AUTOMAIN Project Francis SOURD – SNCF – WP5 leader Paris, October 4th 2012
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission WP5 team
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Objective of the work - Definitions Develop operations research methods and tools for autonomous maintenance planning and scheduling. Planning identifies the time periods when a track segment should be closed for maintenance. Scheduling computes the start and end times of the operations and adapt the timetables of the commercial trains.
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Operations Research Operations Research: application of advanced analytical methods to help make better decisions Here (as often) the advanced analytical methods are mathematical optimization methods in order to automatically compute the best feasible solution or at least some optimized good schedules.
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Operations Research approach What is a solution? Fixed parameters. KNOWN length of track segment, maintenance operations… Decision variables. UNKNOWN A solution is defined (non ambiguously) when the decision variables are instantiated (values are assigned all the variables). Start dates and times
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Operations Research approach What is a feasible solution? List the constraints that a planning (or a schedule) must satisfy Express these constraints as a mathematical (in)equality in function of the decision variables. If all the (in)equality are satisfied when the decision variables are instantiated, the solution is feasible.
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Operations Research approach What is a good solution? Introduce a mathematical function depending of the decision variables the objective function For each solution, that is for each instantiation of the decision variables, the objective function can be evaluated The higher the evaluation is, the better the solution. Maximize the objective function with respect to the constraints.
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Operations Research approach Some practical considerations We must be able to feed the model with good numerical values for the parameters. Some constraints may be violated. Some constraints are missing. There is no unique objective function. Optimisation is complex and takes CPU time.
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Data Interface language Collaborative planning system Collaborative planning system Conflict detection & GUI OR module Data Interface language Maintenance needs
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Definition of the problems Work in relation with WP1 Two sources Analysis of the answers to the questionnaire Analysis of the state-of-the-art Four new models introduced Long-term planning problem (LTPP) Dynamic planning problem (DynPP) Time-window insertion problem (TWIP) Work Site Scheduling problem (WSSP)
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Long-term planning (LTP) Finds the best days to execute the maintenance operations (daily planning) Planning over several years (typically 3 years)
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission LTP constraints Operation combination constraints defined for maintenance types Routing constraints for maintenance/inspection machines Algorithmic collaboration with TWIP (via TWG) Track availability constraints (not yet implemented) Macroscopic description Maximum possession time for a segment Maximum possession time for a sub-network (set of segments) Maximum number of possessions Incompatibility constraints between track possession The simultaneous possession of two track segments can be forbidden in order to continue the service between two points of the network.
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission LTP Objectives Minimization of track possession for inspection, maintenance and moves of machines Minimize the total cost Maximization of the use of maintenance machines Number of required maintenance machine Work load balancing between pre-determined sub-networks (not yet implemented). work to improve the model is necessary
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Dynamic planning Not yet implemented Variant of LTP re-planning The long-term planning is given in input Some additional maintenance operations become necessary after inspection They must be inserted in the planning/schedule Minimize the insertion cost Minimize the impact of these new tasks on the initial planning (update cost)
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Time window insertion problem (TWIP) Input A railway network A fixed schedule for commercial freight and passenger trains Over about 24 hours Time must be limited due to computational complexity. A short list of time windows and logistics or inspection train paths to be inserted in the commercial schedule
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Simple time window insertion (Example) km S E=S’ t E’
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission TWIP constraints No conflict between paths is allowed, may they be technical or commercial. An input path or time window can be “deformable”: Maintenance train can be parked for some time in some predefined points Speed of the maintenance train is subject to a minimal and maximal speed Some time windows could be defined with alternative modes for instance, 1 single window of 2 hours or 2 windows of 1.5 hours Generalized temporal constraints arrival of the technical train at the latest 30 minutes after the beginning of the works All the paths and windows must be inserted Indeed, the paths and windows given in input are related to each other. We assume that they are all required to perform the maintenance task. Their number is not too large.
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission TWIP Objectives Minimize the cost a cost function must be given in order to assess the cost of a time window according to its start time and its duration Minimize the duration The duration is the time span between the start time of the earliest time window and the completion of the latest one. For example, if one train path is to be inserted, this objective function will minimize the total stopping time of the train Minimize the disturbances on the business service If it is not possible to insert the operations without modifying the business service, a degraded mode can be considered, with the possibility to delay, advance or remove trains. Penalties for early, late and cancelled trains must be given in input.
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Work site scheduling problem (Not implemented) Variant of TWIP Shorter time span and smaller sub-network typically the time and space extent of a track possession More objects to be inserted Here a time window corresponds to a basic maintenance operation Advanced compatibility constraints are required Resource constraints Track / security constraints
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Solution approach Collaborative optimization LTP module Macroscopic long-term planning TWG module Time-window and train paths generation Reference Data XML-based file format defined - RailML import not supported in D5.1 TWIP module Microscopic time-window scheduling
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission The three problems in the tool Long-term planning problem (LTP) Large scale (whole country) over 1-3 years Resource requirements and capacities Time-window/track possession generation (TWG) Cost-time trade-off for moving a maintenance machine Cost-time trade-off for performing a maintenance operations Time-window insertion problem (TWIP) Given existing train paths, how to insert the track possessions in the timetable (local scale, over a few hours)
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Work flow – D5.1 PDD TWIP – WSPP LTP – DynP Informal description All PSD LTP TWG TWIP Formal models Software architecture Algorithms ED + SNCF + TUBS Development LTP - SNCF Development TWG - TUBS Development TWIP - ED D5.1 Prototype ED+SNCF+TUBS Test instances All Maintenance data MERMEC – WP3 ? Network - Trains SNCF – NR/WP3? D5.2 Demo All GUI – MMI in WP3? DLR GUI implementation ? Completed Running Not started Problem
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www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission Next steps Module development phase is finishing. Test case is about to be released. Test and Integration phase in October – December. Release of D5.1 (beta version) in January 2013. Tool will then be finalized. Experimental tests will compare different scenarios based on other WP results.
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