Innovations in Automated Planning and Scheduling

1. Innovations in Automated Planning and Scheduling 1st workshop of the EC AUTOMAIN Project Francis SOURD – SNCF – WP5 leader Paris, October 4th 2012 www.automain.eu A Joint Research Project funded under the Seventh Framework Programme (FP7) of the European Commission

2. WP5 team

3. 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.

4. 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.

5. 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

6. 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.

7. 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.

8. Operations Research approachSome 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.

9. Collaborative planning system Data Interface language CollaborativeplanningsystemConflict detection & GUI Data Interface language OR module OR module Maintenanceneeds OR module OR module

10. 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)

11. Long-term planning (LTP) • Finds the best days to execute the maintenance operations (daily planning) • Planning over several years (typically 3 years)

12. 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.

13. 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

14. Dynamic planningNot 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)

15. 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

16. Simple time window insertion(Example) km E’ E=S’ S t

17. 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.

18. 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.

19. 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

20. Solution approachCollaborative optimization Reference Data XML-based file format defined - RailML import not supported in D5.1 LTP module Macroscopic long-term planning TWG module Time-window and train paths generation TWIP module Microscopic time-window scheduling

21. 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)

22. Work flow – D5.1 PDD TWIP – WSPP LTP – DynP Informal description All Development LTP - SNCF D5.1 Prototype ED+SNCF+TUBS Test instances All PSD LTP TWG  TWIP Formal models Software architecture Algorithms ED + SNCF + TUBS D5.2 Demo All Development TWG - TUBS Development TWIP - ED Maintenance data MERMEC – WP3 ? Completed Network - Trains SNCF – NR/WP3? Running Not started GUI implementation ? GUI – MMI in WP3? DLR Problem

23. 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.