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Design of Vehicle

Routing CapabilityJānis Grabis1, Jānis Kampars1,Žanis Bondars1, Ēriks Dobelis2

1Institute of Information Technology, Riga Technical University, 2LLC PricewaterhouseCoopers

{grabis, janis.kampars, zanis.bondars}@rtu.lv, eriks.dobelis @lv.pwc.com

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Design of optimal delivery routes for a fleet

of vehicles serving spatially distributed

customers

– Multi-objective optimization

– Affected by external and uncertain factors

Vehicle Routing as a Service

– Provider’s capability

– Information sharing

Vehicle Routing

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The objective of this paper is to develop

the vehicle routing capability and to

illustrate its application

– CDD methodology is used to develop the

capability model

– Adjustments are used to implement complex

routing logics and adaptations

Objective

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class Ev olution

Capability

Context Element

ProcessGoalKPI

Adjustment

0..*

adapts 1

0..*

uses

0..*

0..*

uses

0..*

1..*

fulfi ls

*

*has

*

0..*

supports

1..*1..*

used for

1..*

Capability Modeling Concepts

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Routing as a Service

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Vehicles routing capability enables optimal vehicle route

planning and supports processes of:

• Route planning

• Route execution

• Performance evaluation

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Goals and KPI

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Customer

service

Labor cost

Labor cost idle

Vehicle

operating cost

Safety

77

Route variability

Route safety

– Number of accidents

– Number of left turns

– Number of hazards

Calendar

Weather

Context elements

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88

Route Optimization Adjustment

• Recalculates routes

KPI Adjustment

• Changes importance of KPI in decision-making

Context Adjustment

• Changes perceived impact of context on decision-making

Adjustments

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99

Routing Capability Model

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10

Minimize routing cost and

penalty for not achieving specific

KPI

X – decision variable of route

assignments

Routing cost c is a function of

distance and other context

factors

Penalty is calculated as

deviation of KPI from their target

values

Weight factors v and w are

updated according to actual

performance

Routing Adjustment

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KPI penalty depends of weight

factors

The weight factors are increased

for KPI with the lowest level of

satisfaction

KPI Adjustment

1

H

h hhP v P

'

1max

H

h hhv P

'

new h h

h

hh

v vv

v

Observe actual route execution performance

Invoke adjustment periodically

Emphasize KPI with the worst performance

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Implementation of Routing

Service

CCPContext Data

CDARouting

application

CDTRouting model

Optimization

engine

CNA

MonitoringAdjustment

services

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LSP1 LSP2

KPI1: customer service

KPI2: travel cost

KPI3: vehicle operating cost

KPI4: safety

KPI1: customer service

KPI2: travel cost

KPI3: vehicle operating cost

KPI4: safety

CTX1: route variability

CTX2: route safety

None

Service Customization

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Each service consumer selects KPI and context

elements corresponding to their needs

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Routing Results

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LSP2

LSP2

LSP1

LSP1 accounts for traffic

time variability and

accidents

LSP2 does not consider

context

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Cycle KPI weights v KPI1 KPI2 KPI3 KPI4

1 (0.25,0.25,0.25,0.25) 0.65 0.92 1.25 1.22

2 (0.3,0.3,0.2,0.2) 0.65 0.89 1.25 1.03

3 (0.36, 0.32,0.16,0.16) 0.65 1.18 1.25 1.20

4 (0.43, 0.31,0.13,0.13) 0.75 1.03 1.25 1.33

5 (0.52, 0.28,0.10,0.10) 0.75 1.01 1.25 1.61

Performing Adjustments

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Capability model as a common basis for

service customization and delivery

Adjustments can be modified

independently of the core routing

application

Correlation among context and KPI

Conclusion

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