GECCO 2001

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

• Public transport scheduling

• Optimisation issues

• Discussion

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Public transport service
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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

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

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Parties involved in UK train timetabling
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Train timetables generation

• Three key types of decision variable

• Departure times

• Scheduled runtimes

• Resource options at a station

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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
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Soft constraints

• (TOCs) Commercial Objectives

• Preferred departure/arrival times

• Clockface times

• Passenger connections

• Even service

• Efficient train units schedule

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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

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Bus Vehicle Scheduling - FIFO, FILO
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Driver Scheduling - Vehicle work to be covered
( Relief opportunity )
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2-spell driver shift example
Vehicle 1
Vehicle 2
Vehicle 3
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More example potential shifts
Vehicle 1
Vehicle 2
Vehicle 3
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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

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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

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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

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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

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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?

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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

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Discussion