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

A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission WP5 team

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.

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.

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

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.

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.

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.

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

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)

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)

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.

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

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)

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

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’

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.

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.

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

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

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)

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

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  Tool will then be finalized.  Experimental tests will compare different scenarios based on other WP results.