INOC 2013May 2013, Tenerife, Spain

Train unit scheduling with bi-level capacity requirements

Zhiyuan Lin, Eva Barrena, Raymond KwanSchool of Computing, University of Leeds, UK

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CASPT 23 July 2015, Rotterdam

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Motivation Problem description

- Capacity levels Model Computational experiments Conclusions and further work

Outline

Motivation

Minimize operating costs

Satisfy capacity requirements

Train unit scheduling problem

Imprecise definition

Under-utilized train unitsImbalanced demands

Re-balance

Various sources

Best representationHow?

3

Motivation Problem description

– Capacity levels Model Computational experiments Conclusions and further work

Outline

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Train units

5

Class 171/7 (2-car), diesel

Class 375 (4-car), electric

Train unit scheduling

• Train unit scheduling problemTrain ID Origin Destination Dep time Arr time demands

2E59 A B 09:05 10:15 125

2G15 B C 10:30 12:25 206

2G71 C D 15:00 17:35 196

2E59AB

source s

2G15BC

2G71CD

sink t

Sign-on arc

Empty-running connection arc

Path (source to sink) Scheduled work for a train unit

Train node

Sign-off arc

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Station connection arc

)},,{( AtsNG

2E32

1E06

2E11

2E03

1E09

source

sink

1E06

2E11

2E32

2E11

2E03

1E09

Train unit scheduling Integer multicommodity flow representation

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• Paths may overlap for coupling• Coupled units may be of different but compatible types

x1

x1

Outline

• Capacity requirement can be inferred from:– Mandatory minimum provision – Historic provision– Passenger count surveys (PAX)– Future growth expectation

• Problems of a single level:– Requirements not precisely defined / unknown– Under-utilized train units as a result of

optimization techniques

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Train capacity requirements

Under-utilized train units

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Outline

• Implicit information– Pattern of unit resource distribution – Agreements/expectations with transport authorities

• Potential problems– Capacity strengthening could be used for unit

resource redistribution: didn’t reflecting the real level

– Unreasonable pattern may stay in past schedules for years

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Historic capacity provision

Outline

Capacity strengthening for unit resource redistribution in historic provision: an example

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Historic capacity provision

i

j

m

Requires 1 unit

n

Requires 1 unit

Requires 1 unit Requires 2 units

A B

C B

B D D E

Outline

• Actual passenger counts• Only a subset of trains surveyed• Might contradict with historic provisions• Frequency and scale of surveys vary among

operators

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PAX surveys

Outline

• Over-provided (OP): if historic capacity > PAX in terms of number of train units

• Under-provided (UP): if historic capacity < PAX

– No place available for coupling/decoupling– Result of under-optimized schedules– OP: Used for redistributing train unit resources– UP: May be inevitable due to limited fleet size and/or

coupling upper bound

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OP and UP trains

Outline

• A desirable level r’j

– Will be satisfied as much as possible– max {historic, PAX, …}

• A target level rj

– Must be strictly satisfied– min {historic, PAX, …}

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Bi-level capacity requirement (per train j)

jr

jr

Outline

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Bi-level capacity requirement

Historic capacity

PAX

Future growth

Mandatory minimum

…

Desirable capacity

Target capacity

ModelScheduled capacity

information Input data Output data

Motivation Problem description

– Capacity levels Model Computational experiments Conclusions and further work

Outline

16

Outline

Objective function– Minimize operating costs, including

• Fleet size, mileage, empty-running

– Reflect preferences on, e.g., long idle gaps for maintenance

– Achieve the desirable capacity requirements level as much as possible

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The integer multicommodity flow formulation

OutlineConstraints

– Fleet size bounds

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The integer multicommodity flow formulation

– Target capacity requirement– Coupling of compatible types– Complex coupling upper bounds

combined into “train convex hulls”

Outline

Variables

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The formulation

NNjy

KkPpx

j

kp

~,

, ,

R

Z Path variablenumber of units used for path p of type k

Capacity provision variable The capacity provided by the solver at train j

Outline

Realized in the objective

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Desirable level r′

Get the capacity provision in constraints

Minimize the deviation between y and r′

;~

, Njyxq jKk Pp

pk

jkj

Nj

jjKk Pp

pp ryCxcCk ~

21min

;~

,

)(min~

2

Njyyrxq

yyC

jjjKk Pp

pk

Njjj

jkj

Deal with the absolute values

Operating cost Desirable capacitylevel

Outline

(1) Objective

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The ILP formulation

(3) Convex hulls for all trains

(4) Calculate capacity provision variables

(5)(6) Variable domain

(2) Fleet size upper bound

Njy

KkPpx

Njyxq

NjFfdxH

Kkbx

j

kp

jKk Pp

pk

jjf

Kk Ppp

jkf

k

Ppp

jkj

jkj

k

,

;,,

;~

,

;, ,

; ,

,

R

Z

Nj

jjKk Pp

pp ryCxcCk ~

21min

Motivation Problem description

– Capacity levels Model Computational experiments Conclusions and further work

Outline

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• Objective function - Competing terms

Deviation from

desirable level

Operating costs: Fleet size, mileage, ...

𝐶1 𝐶2Weights

Computational experiments: Objective function terms

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Computational experiments

Purposes• Calibrate the objective function weights • Satisfy as much as possible the desirable capacity

level for a given fleet size• Compare with manual schedules

Experiments • E1: Varying weights in the objective function • E2: Fix fleet size & solely minimize r’ deviation

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Computational experiments

Central Scotland railway network; December 2011 timetable

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• Actually operated schedule: 64 OP trains out of 156

• If use PAX, solver = 29 units• If use historic capacity, solver = 33 units

Computational experiments: Input data

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Computational experiments: Results on E1

• E1: Varying weights in the objective function.

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Computational experiments: Results on E1

• Varying weights in the objective function

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Experiments (increasing

Computational experiments: Results on E2

• E2: Fix fleet size & solely minimize OP deviation

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Computational experiments: Comparison between E1 & E2

E2

E1

Actually operating schedule: Fleet size= 33, OP=64 30

Conclusions

• Train unit scheduling with bi-level capacity requirements: Target: PAX; Desirable: historic provisions Schedules with more reasonable/controlled

capacities• Improvements w.r.t. manual schedules:

12% reduction of fleet size Maintaining nearly 60% OP trains

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Further work

• UP trains & limited fleet size• Multicriteria optimization• Trade-offs between depot returns and

maximizing capacity provision• More problem contexts in train unit resource

planning, e.g.– franchise bidding– maintenance scheduling

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Thank you!