Raymond Kwan

7/29/2019 Raymond Kwan

1/22

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


2/22

2

o Public transport scheduling

Outline

o Optimisation issues

o Discussion


3/22

3

Vehicle &

DriverOperations

Transport

Operator

The

Public

Routes

Timetables

Fares

Planning &Scheduling

Depot Operations& management

Payroll

Public transport service


4/22

4

Planning and scheduling

o Minimise operating costs

o Operator: one optimisation problem, alldecisions are variables

o Solution designer:

Sequential tasks

Some decisions are fixed by earlier tasks

Some decisions are left open for later tasks


5/22

5

Planning and scheduling tasks

Service and

Timetable

Planning

Vehicle

Scheduling

Crew

Scheduling

Crew

Rostering


6/22

6

Research & Development at Leeds

o Span over 40 years (22 years myself)

o Algorithmic approaches

- hueristics- integer linear programming

- rule-based/knowledge-based- evolutionary algorithms- tabu search- constraint based methods- ant colony

o Numerous users in the UK bus and train

industries


7/22

7

Track Operator

UK Train

Timetables

Train Operating

Companies

Strategic Rail

Authority

Office of the Rail

Regulator

Health and Safety

Executive

Parties involved in UK train timetabling


8/22

8

o Three key types of decision variable

Departure times

Scheduled runtimes

Resource options at a station

Train timetables generation


9/22

9

Hard Constraints

o Headway: time gap between trains on

the same track

o Junction Margins: time gap between

trains at a track crossing point

o No train collision!

- On a track

- At a platform


10/22

10

Soft constraints

o (TOCs) Commercial Objectives

Preferred departure/arrival times

Clockface times

Passenger connections

Even service

Efficient train units schedule


11/22

11

Bus Vehicle Scheduling

o Selection and sequencing of trips to

be covered by each bus

o Each link may incur idling or deadrun

time

o Minimise fleet size, idling time,

deadrun time

o Other objectives: e.g. preferred block

size, route mixing


12/22

12

Bus Vehicle Scheduling - FIFO, FILO

Departures

Arrivals

FIFO for regular

steady serviceFILO for end of peak


13/22

13

Driver Scheduling

- Vehicle work to be covered

Vehicle 38 S

13041110093507420600

HHSG

( Relief opportunity )

Location

Time

Piece of work


14/22

14

2-spell driver shift example

Vehicle 1

Vehicle 2

Vehicle 3

sign on at depot

sign off at depotmeal break


15/22

15

Vehicle 1

Vehicle 2

Vehicle 3

More example potential shifts


16/22

16

o Jobs to be scheduled have precise starting

and ending clock times

o Scheduling involves trying to get subsets of jobs to

fit within their timings to be collectively served by aresource (vehicle or driver)

o Not the type of problem where jobs are queued tobe served by a designated resource

Some characteristics of vehicle

and driver scheduling


17/22

17

Driver Rostering

o To compile work packages for drivers

e.g. A one-week rota

SunREST

SatREST

Fri S141350 - 1815

Thu S071201 - 1846

Wed S460512 - 1357

Tue S460512 - 1357

Mon S460512 - 1357

o Rules on weekly rotas

o Drivers may take the rotas in rotation

o Optimise fairness across the packages

subject to rules and standby requirements


18/22

18

Multi-objectives what is optimality?

o 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


19/22

19

o Automatic global optimisation is obviously

impractical

o 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

Global optimisation?


20/22

20

o 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

Better optimisation through intelligent

integration of the scheduling tasks

o Needs good vision from the human schedulerrule-based expert system to integrate thescheduling tasks?


21/22

21

o Different types of service may pose different

levels of difficulty for scheduling (differentalgorithmic 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

Scheduling for different service types


22/22

22

Discussion

Documents

Raymond Kwan