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Experience from designing transport scheduling algorithms

Experience from designing transport scheduling algorithms. Raymond Kwan School of Computing, University of Leeds R.S.Kwan @ leeds.ac.uk. Open Issues in Grid Scheduling Workshop, Oct 21-22, 03. Outline. Public transport scheduling. Optimisation issues. Discussion.

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Experience from designing transport scheduling algorithms

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  1. Experience from designing transport scheduling algorithms Raymond Kwan School of Computing, University of LeedsR.S.Kwan @ leeds.ac.uk Open Issues in Grid Scheduling Workshop, Oct 21-22, 03

  2. Outline • Public transport scheduling • Optimisation issues • Discussion

  3. Depot Operations & management The Public Routes Timetables Fares Vehicle & Driver Operations Transport Operator Payroll Planning & Scheduling Public transport service

  4. Planning and scheduling • Minimise operating costs • Operator: one optimisation problem, all decisions are variables • Solution designer: • Sequential tasks • Some decisions are fixed by earlier tasks • Some decisions are left open for later tasks

  5. Planning and scheduling tasks Service and Timetable Planning Vehicle Scheduling Crew Scheduling Crew Rostering

  6. Research & Development at Leeds • Span over 40 years (22 years myself) • Algorithmic approaches- hueristics- integer linear programming- rule-based/knowledge-based- evolutionary algorithms- tabu search- constraint – based methods- ant colony • Numerous users in the UK bus and train industries

  7. SRA ORR HSE Strategic Rail Authority Office of the Rail Regulator Health and Safety Executive TOCs Track Operator Train Operating Companies UK Train Timetables Parties involved in UK train timetabling

  8. Train timetables generation • Three key types of decision variable • Departure times • Scheduled runtimes • Resource options at a station

  9. Hard Constraints • Headway: time gap between trains on the same track • Junction Margins: time gap between trains at a track crossing point • No train collision! - On a track - At a platform

  10. Soft constraints • (TOCs) Commercial Objectives • Preferred departure/arrival times • Clockface times • Passenger connections • Even service • Efficient train units schedule

  11. Bus Vehicle Scheduling • Selection and sequencing of trips to be covered by each bus • Each link may incur idling or deadrun time • Minimise fleet size, idling time, deadrun time • Other objectives: e.g. preferred block size, route mixing

  12. Arrivals Departures FIFO for regular steady service FILO for end of peak Bus Vehicle Scheduling - FIFO, FILO

  13. Piece of work 0600 0742 0935 1110 1304 Vehicle 38 G S H H S Time Location Driver Scheduling - Vehicle work to be covered ( Relief opportunity )

  14. sign on at depot meal break sign off at depot 2-spell driver shift example Vehicle 1 Vehicle 2 Vehicle 3

  15. More example potential shifts Vehicle 1 Vehicle 2 Vehicle 3

  16. Some characteristics of vehicle and driver scheduling • Jobs to be scheduled have precise starting and ending clock times • Scheduling involves trying to get subsets of jobs to fit within their timings to be collectively served by a resource (vehicle or driver) • Not the type of problem where jobs are queued to be served by a designated resource

  17. Mon S46 0512 - 1357 Tue S46 0512 - 1357 Wed S46 0512 - 1357 Thu S07 1201 - 1846 Fri S14 1350 - 1815 Sat REST Sun REST Driver Rostering • To compile work packages for driverse.g. A one-week rota • Rules on weekly rotas • Drivers may take the rotas in rotation • Optimise fairness across the packages subject to rules and standby requirements

  18. Multi-objectives – what is optimality? • Operators do not always try equally hard to achieve optimal operational efficiency • Union rules • Service reliability • Problem at hand is not on the “critical path”

  19. Global optimisation? • Automatic global optimisation is obviously impractical • Combining two successive tasks for optimisation are sometimes desirable, e.g. • Hong Kong: fixed size fleet, fixed peak time requirements, schedule buses & maximise off-peak service • Sao Paolo: driver and vehicle tied schedules • First (UK bus): “ferry bus” problems

  20. Better optimisation through intelligent integration of the scheduling tasks • Sometimes superior results could be simply obtained where powerful optimisation algorithms fail • A more favourable scheduling condition could be achieved from the preceding scheduling task • E.g. driver forced to take a break after a short work spell – swap in the vehicle schedule to lengthen the work spell • Needs good vision from the human scheduler –rule-based expert system to integrate the scheduling tasks?

  21. Scheduling for different service types • Different types of service may pose different levels of difficulty for scheduling (different algorithmic approaches?) • Urban commuting: high frequency, many stops • Sub-urban and rural: lower frequency, fewer stops • Inter-city and provincial: long distance, few stops • Some problems have to consider route and vehicle type compatibility

  22. Discussion

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