1
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
4
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Routing as a Service
5
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
6
Customer
service
Labor cost
Labor cost idle
Vehicle
operating cost
Safety
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Route variability
Route safety
– Number of accidents
– Number of left turns
– Number of hazards
Calendar
Weather
Context elements
7
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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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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