D i i R b t V l C ti Designing Robust Value-Creating Supply Chain NetworksSupply Chain Networks
Alain MartelInteruniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT)g p ( )andFaculté des sciences de l’administrationUniversité Laval, Québec, CanadaUniversité Laval, Québec, Canada
May 2010
CIRRELT
SCN-StudioSCN StudioTool developped in collaboration withModellium and DRDCModellium and DRDCduring the DRESNET project(Design of Robust and Effective Supply(Design of Robust and Effective SupplyNetwork Engineering Tools)
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Designing Robust Value-Creating Supply Chain Networks
1 P bl C t t
Supply Chain Networks
1. Problem Context 2. Modeling SCN Processes and 2. Modeling SCN Processes and
Structures3. Taking Uncertainty into Account4 SCN D i M h d l4. SCN Design Methodology5 Research Challenges5. Research Challenges
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1. Generic Design ProblemgRaw material sources
Deployed Supply...
ManufacturingProcess
Chain Network
SC Risk ExposureFinished Products
DistributionChannels
V l C tiValue Destruction
DisruptionsMarkets
...
Value CreationRobustness ?
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Re-Valorization Network
SUPPLY
VALORIZED PRODUCT MARKETS
N
N
SUPPLY
SOURCESOURCE
RE
RERERE--DISTRIBUTEDISTRIBUTE
CH
AIN
CH
AIN
WO
RK
WO
RK
MANUFACMANUFAC--TURETURE
EE--V
AL
OV
AL
ON
ET
NE
T
VALORIZEVALORIZERe-manufacture
DisassemblyRepairVal e Creation
PP
LY
CP
PL
Y C
NE
TW
ON
ET
WO
OR
IZA
TO
RIZ
AT
TW
OR
KT
WO
RK
RepairValue CreationNetwork
SUP
SUP NN
T
DISTRIBUTEDISTRIBUTE
TIO
NT
ION
KK
RECOVERRECOVER
RETURNRETURNDEMAND-RETURNDISPOSAL
--RETURNRETURN
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SCN Design Modeling
Robust and efficient design under uncertainty
nsio
n
Robust and efficient design under uncertainty • Hazardous/catastrophic disruptive events (risk modeling)• Adequate user decisions anticipation• Resilience strategies for sustainable stakeholder value
RobustDesign
cal E
xpan
Stochastic problems over a long planning horizon • Business as usual context (random events modeling)• Real options and hedging
g
Large
Location- • Generic production-distribution networksM lti l f ilit / t t ti tiod
olog
ic • Real options and hedging• Positioning by anticipation to capture promising contractsSPs
Allocation Problems
• Multiple facility / transportation options• Externalization decisions with partner selection• Multiple value offers to product-markets• Value maximization …
Met
hoLargeMIPs
Functional Expansion
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2. Modeling SCN Process and Structures Structures
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Modeling SCN Process and Structures Structures
SupplySupply1
RMStorage
2
3 ComponentsVS
Raw materials3
1 2 3
2 1 2p ∈ RM
fabrication
SAsmanufacture
414Manufactured prod cts
22
11
4 5
Final assembly
manufacture
Kitting5
6products
2 3 4
16 7p ∈ MP
SupplyDemand
FPStorage
7
8Finished products
8 9
p ∈ FPp ∈ P
Process graph with recipes
pp y
Bill-of-material (BOM) forassembly /disassembly process
pp
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recipesassembly /disassembly process
Modeling SCN Process and StructuresStructures
Raw MaterialVendors
IntermediateProduct Plants
Fournisseurs res (v∈ V)To the network facilities
(s∈ S)
Sources (v ∈ V)Fournisseurs resFournisseurs res (v∈ V)
(s S)
SupplySources (v ∈ V)
FinalProduct Plants
(s ∈ S)(s S)
DCs /Warehouses
Distribution CentersProduction-Distribution Centers
To the demand zones
DemandZones
E h l t tZones dedemande(d∈ D)Zones dedemande(d∈ D)Zones dedemande(d∈ D)DemandZones (d∈ D)
G l t tEchelon structure vs General structure
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Generic Process Modeling Formalism
ActivityStems SupplySupply
RM Suppl
Activity types:
Activity Graphfor the
Bucking
RMStorage Logs
Supply:
Transformation:
Consolidation/ transfer:
for theForest
dSawing
Rejects
Logs
Storage:
Demand:
Transformation:
ProductSawing
Drying
Chipping
Rejects
Rough Lumber
Green Lumber Demand:
Movement types:Inter location:
Industry Planing/Grading
Chips
g Inter-location:(transportation)
Intra-location:(material handling)
SupplyDemand
FPStorage
p
d
Both: (inter or intra location)
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Boards
Activity Graph for CF Prepositioning SCN Case
SupplyDomestic/LocalSupply
(all) (4)(2,3)(6,7) (8,9,10,11)
RepairStaging-Transfer
PalletStorage
HazmatStorage
(11)Refrigerated
Storage(8,9,10,11)
Lane Meter Storage
(6,7) (2,3) (4)(all) (8,9,10,11)(all)
SupplyTheatre
Demand(13)
(1 2 3 4 5 8 9 10 11 14)(12)
Initial provisioning transportation (I)Deployment transportation (D)Sustainment transportation (S )
( ) : ProductsDepot supply transportation (T)Sustainment back‐transportation (B)Redeployment transportation (R)
(1,2,3,4,5,8,9,10,11,14)
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Sustainment transportation (S )Intra‐facility handling (H)
Supply - V Consolidation/Transhipment - C Storage - W Repair - F Demand - D
Redeployment transportation (R)
Potential Supply & Depot Locations
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Potential Theater Locations(Demand Zones ) (Demand Zones )
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Platforms for a Given Site
Current platform layoutAlternative Platforms:
∈ sg GfFixed area Platform 2 layout
Fixed area
Reconfigurable Reconfigurable
areaPlatform 3 layout
area
Fixed area
Site Location s∈S
Fixed area
Reconfig rableReconfigurable area{ }∈ ∈ ∈0 , 1 ; ,sg sY s S g G
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Systems in a PlatformyPlatform
Systemy
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Potential Platforms on a SiteDEPOT SITE (ex: Dubai)
Platform 1 Platform 2 Platform 3
Cost 1-Opening-Usage
Cost 2-Opening-Usage
Cost 3-Opening-Usage-Usage
-Closing
Capacity 1
-Usage-Closing
Capacity 2
-Usage-Closing
Capacity 3p y-Storage-Repair
-Handling
p y-Storage-Repair
-Handling
p y-Storage-Repair
-Handling
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Handling Handling Handling
Market Offers
Demand zones (ship-to-points) are associated to d t k t d fi d i product-markets defined using
product categories and sales regions
Categories:g• Pulp • Make
S kDomtar: Printing & Writing Papers
• Stock
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Number of ship-to-points: 14 160
Modeling Offers for a Product-MarketgOffers are modeled using Market Policies based on(Price Response time)
Production-distributionCenter1 32 s
(Price, Response time)=> Set of admissible delivery sites
Center
DistributionCenter
1 32
4s
47
Offers based on
Demand zone5 6
order winning and qualifying
criteria{ }1 1,2,3,4,5,6,7iS ={ }4 5 6 7iS =
criteria
A. Martel - Designing Robust Value-Creating Supply Chain Networks 18{ }3 5,6iS ={ }2 4,5,6,7S =
v V∈SupplySupplyStems
RMStorage
Logs
Platform Platform
h
Bucking
Logs Po
Platform Platform
…
ty G
raph Sawing
Rejects
RejectsGreen Lumber
otential ….Systems Systems
…
Act
ivit Drying
Planing/
ChippingRough Lumber
Sites
Planing/Grading
FPChips
s S∈
SupplyDemand
FPStorage
Spot market Potential Potential d D∈
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BoardsSpot market
zonesPotentialcontracts VMI agrem. …d
a A∈ l L V S D∈ = ∪ ∪
Potential Supply Chain Network Vendor 1 Vendor l
Node (l,1). . .s
V( ,1),l l L∈
pp y
inl L∈. . .( , )l a N∈ S V
(1, ) ( ,1)( , ) ( ) ( )a l p l np s P P S l L∀ ∈ ∩ × ∈
Sup
ply
arcs
T( , )n l a N= ∈
( , ')a ap P∀ ∈
alar
cs . . . ex'l L∈( , ') '( , ) a a pnnp s P S∀ ∈ ×
lc C∈
Inte
rna
C' ( ', ')n l a N= ∈ W' ( , ')n l a N= ∈
D( )l a l L∈ eman
dar
cs 'lc C∈( , ) '( , )( , ) ( ' , )k p l pn l a jplj s J S n s NS←∀ ∈ × ∈
. . .( , ), tl a l L∈
SupplyZone 1 SupplyZone lNode ( , )l a
. . .. . .. . . lkp P∈
K( )lk K∈
. . .
De
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Modeling Value
Consider a given SCN Design
g
Consider a given SCN Design
A. Martel - Designing Robust Value-Creating Supply Chain Networks 21DOMTAR CASE
Design ObjectiveEfficient-Frontier for a given P&C System
TotalDiscounted
Qualifying requirementsCosts requirements
Dominated Design
Failed Design
Efficient Design
Response Time (or other value attribute)
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Design ObjectiveDesign for Value
DesignDiscounted Total Revenue Maximize
Costs(Revenues)
MarginMargin
EconomicValueMarginMargin Value
AddedExpenditures
Design Response Time (or other value attribute)
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Type of Model ObtainedMax ∑Countries{(1-Tax) ∑Sites[Revenues
- (Platform costs(Platform costs+ System & flexible capacity costs+ Facilities operations costs+ Procurement and Inventory costs+ Procurement and Inventory costs+ Transportation costs + Duties...)]}
subjet toNetwork configuration constraintsNetwork configuration constraintsPlatform/system selection constraintsSupply / Capacity / Demand constraintsM t i l i t t i tMaterial requirement constraintsInventory accounting constraintsFlow conservation constraintsLocal content and transfer price constraints…
( h )A. Martel - Designing Robust Value-Creating Supply Chain Networks 24
=> Large MIP (with concave costs)
3. Taking Uncertainty into Accountg
Economy
Supply Chain Network
Society
Network
Environment
Historical path Evolutionary paths(Shell Global Scenarios to 2025)(Shell Global Scenarios to 2025)
The future is dominated by uncertainty
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Designing for the FutureDesigning for the Future• Planning horizon
• Capacity investment decisions => 10 years & +• Resource allocation decisions => 12 months
Planning horizon
C i i h d i b i lPlanning cyclePeriod
• Coping with randomness in a business as usual context• Stochastic demands, prices, exchange rates…• Static stochastic program with recourse for a typical planning cycle• Static stochastic program with recourse for a typical planning cycle• Multi-stage (cycles) stochastic program with recourse
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Hazardous/Catastrophic Events
Hurricane Katrina • Destruction of supply chains pp yand transportation infrastructures
• Demand surge for first aid and construction material
• Significant decrease of demand for luxury productsA i t l 58 000 t • Approximately 58,000 troops coming from all 50 US states assigned to the theaterassigned to the theater
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Supply Chain Network Risk AnalysisThree Fundamental Questionspp y y
• What can go wrong?• Vulnerability sources identification and filteringVulnerability sources identification and filteringWhat is the likelihood of that happening?
Multihazard zones risk exposure levelsMultihazard zones risk exposure levelsStochastic multihazard arrival processesA i b bili i Attenuation probabilities
What are the consequences?• Incidents damage on supply/resources /demand
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DSN Risk Analysis: What can go wrong?y g g
M ltih d
Natural disasters
Geopolitical failures× Multihazardsfailures
Market failures
Industrial accidents
×Vulnerability SourcesVulnerability Sources
Considered
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DSN Risk Analysis:What is the likelihood of that happening ?Exposure Levels for Natural Disasters
f pp g
Derived from data provided by the
A. Martel - Designing Robust Value-Creating Supply Chain Networks 30
Center for Research on the Epidemiologyof Disasters (CRED)
Vulnerability-Exposure Relationship
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Inter-arrival Time DistributionFor Natural Catastrophes’ Exposure Level 5
Exponential distribution with mean λ = 350 daysExponential distribution with mean λ = 350 days
DaysDays
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Intensity Distributionte s ty st but oFor Natural Catastrophes’ Exposure Level 5L N l di t ib tiLog-Normal distribution
Loss level in $
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Evolutionary Paths (Trends)
600
Pessimistic future
Disaster Frequency Trend
500As-is future
Optimistic future
mbe
r/ye
ar)
300
400
ency
(num
Leads to the definition of a
200
ster
freq
ue
set K of evolutionary paths
h b b l100D
isas
1983-2008 2009-2018
with probabilities, kp k K∈
01 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Years
Planning horizonHistorical path
,kp
A. Martel - Designing Robust Value-Creating Supply Chain Networks 34
gp
Attenuation Probabilities
• The occurrence of a multihazard in a zone does il l i k hinot necessarily translate into a network hit
• Canadian Forces Case:P b bili h i i i i i i d i h Probability that a mission is initiated in response to the occurrence of a multihazard in a given country
Hazard/Mission type Location Probability… …Natural disaster/Humanitarian assistance Belgium 0,023Natural disaster/Humanitarian assistance Botswana 0,023Natural disaster/Humanitarian assistance Chile 0,027… …Quarrel/Peacekeeping Greece 0,700Quarrel/Peacekeeping Herzegovina 0,700Quarrel/Peacekeeping Algeria 0,600… …War/Peace making Haiti 0,450War/Peace making Poland 0,400War/Peace making Cyprus 0,350… …
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What are the consequences?SCN Risk Analysis: consequences?
Multihazard Incidents Severity Profile
y
y
1) 2) 3) 4) 5) 6)
Suppliers Plants DCs First-aid Sustainment Luxury
Capacity-based Vulnerability Sources S c = {1, 2, 3} Demand-based Vulnerability Sources S d = {4, 5, 6}
Suppliers Plants DCs Product-markets Product-markets y
Product-markets a) Natural disasters
Unfilled supply rate
Capacity lossrate
Capacity lossrate
Demand inflation rate
Demand deflation rate
b) Market Unfilled supply Demand deflation Demand deflation
ihaz
ards
a, b
, c}
Impact i i failures rate rate rate
c) Industrial accidents
Capacity lossrate
a) Natural disasters
Time to restoring supplies
Time to restarting production
Time to restarting distribution
Surge duration Drop duration
Mul
tiH
= {
rds
c}
intensity
disasters supplies production distribution
b) Market failures
Time to restoring supplies
Drop duration Drop duration
c) Industrial accidents
Time to restarting production M
ultih
azar
H =
{a,
b, c
Time to recovery
p
A. Martel - Designing Robust Value-Creating Supply Chain Networks 36
What are the consequences?SCN Risk Analysis: qy
Recovery Function ExampleCapacity loss recovery function
A lifi ti A priori percentagesAmplification percentage
100%
A priori percentages
ρ
Recovery function
Amplitude based on β
Working periods1τ ξ+ −'τ ' τ
τρ
Time to recovery
c( )' , ',..., ' 1; ( , , ); , , h h h h
lp lp lp l lp sp g l l lp s sc c r s S p P l Lτ τρ τ τ τ ξ β ξ= = + − = ∈ ∈ ∈ρ ρ
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Plausible Future Scenarios• The superposition, over the planning horizon, of
• An instance of these multihazard processes • An instance of the business-as-usual random variables
Yields a probabilistic scenario
= Set of probabilistic scenarios
Pω∈ΩPΩ p
= Probability of occurrence of scenario• Deeply uncertain scenarios can also be considered
( )p ωΩ
Pω∈Ω• Deeply uncertain scenarios can also be considered
• Incorporate isolated, non repetitive, extreme events for which a likelihood of occurrence cannot be evaluatedwhich a likelihood of occurrence cannot be evaluated
= Set of deeply uncertain scenariosUΩ
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Aversion to Extreme Events Risk
Based on the number of network hits
0,25
Acceptable-risk scenarios Serious-risk scenariosos
PΩ
0 1
0,15
0,2
ion
of sc
enar
io
Worst-case
0
0,05
0,1
Prop
orti Worst case
scenarios
0 1 2 3 4 5 6 7 8 9 10 11 12Number of hitsHazard tolerance level
( 3)κ =
= Set of acceptable-risk scenariosAΩ Set of acceptable-risk scenarios= Set of serious-risk scenariosS
ΩΩ
A. Martel - Designing Robust Value-Creating Supply Chain Networks 39
Design ObjectiveRobust Design
EconomicValueValue Added
S t i bl Sustainable StakeholderValue
Time (Planning horizon)
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4. SCN Design Methodology
Plausible future (ω)
First planning cycle Second planning cycle
DesignDecisions
• Locations
Plausible future (ω)
UserDecisions User
Decisions• Locations• Platforms• Systems• Offers• Missions
• Demand management• Supply• Production• Inventory
…Decisions
• Demand management• Supply• Production
Planning horizon
• Missions
x1 2
Inventory• Transportation…
y1 4
Must beanticipated
oduct o• Inventory• Transportation y2• …
Planning horizon
Analysis Deployment 1 3
Deployment
Network designdecision point
Network availablefor operations
…Structural adaptation
decision point
Adapted network available
for operations
A. Martel - Designing Robust Value-Creating Supply Chain Networks 41
decision point
x2
Decision-Making Framework
Design Level
g
g• Investment• Deployment
Design
• Policy makingAnticipationof expected
User Response Level
Design
S h i ti f l d d d
prevenues and costs
Synchronization of supply and demandbased on Response Policies for:
• Business-as-usual eventsBusiness as usual events• Extreme events
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Decision Time Hierarchy yStructural adaptation
decision
Design decision
( )I δX
2 2 2( )x X I δ∈
δ Deployment
Deployment 1δ +Δδ
Designlevel
1 1 1( )I δ∈x X 2δ Deployment lead time
1 2ˆ ˆ ˆT T T= ∪
12 2∈ xx Xˆ
lead time 11δ Usage period 1T1x
1 1y Yˆ ∈2T
2x2 2y Y∈ ˆˆˆ
Usage periodΔ
1uT
U2uT
User responselevel τ
( ) ( )*xy Y n uIτ
τ τ τ∈
A. Martel - Designing Robust Value-Creating Supply Chain Networks 43
Illustrative Case: Multi-depot location-transportation problemMulti depot location transportation problem• Daily stochastic orders from customers
l bl• Depots vulnerable to extreme events• How many warehouses and where ?
Stochastic H d
PlantPlant
gn
el
Hazard Process
⇓Potential DC
locations lx lx
l L∈
Potential DClocations
lx lx lx lxl L∈ DCD
esig
Lev
Respond from
Demand zones
(d ? D)Ship to l ti
ax
ryry rDemand zones
(d ? D)Ship-topoints
ax
ryry rDemand zones
(d ? D)Ship to l ti
axax
ryryryry rrDemand zones
(d ? D)Ship-topoints
axax
ryryryry rrer
vel
back-up depot
Demand zones locations y ryDemand zones points y ry
p P∈
Demand zones locations yy ryryDemand zones points yy ryry
p P∈Use
Lev
A. Martel - Designing Robust Value-Creating Supply Chain Networks 44Compound Poisson Demand Process
Strategic Decision Framework g
Design ( ) ( ){ } ( ) ( ) ( )1 1max x x xx ˆ, , , ,, . , d duC C CC I ω ω ω ωδΩ = + ∈ΩRModel
( ) ( ){ } ( ) ( ) ( )1 1 11 1
1 1max x X
x x xx , , , ,, . , C C CC I ω ω ω ωδ∈
+ ∈ΩR
( )1I δΩ
1x( )1 , duC ω ω∈Ωxˆ , 1( )
( )( ) ( )( ) ( )
( )( ) ( )( ) ( )( )2 1
11
opt N n
du u d ut n t
nt T t T
C C C Cω ω ω ω>∈ ∈
⎡ ⎤= + +⎢ ⎥
⎢ ⎥⎣ ⎦∑ ∑ ∑
x xx y x y
ˆ ˆ. ,..., . ˆ ˆ
ˆ ˆ ˆ ˆˆ ˆ ˆ,Anticipated ( ) ( )
11
n
T
⎣ ⎦y y ˆˆ ˆ. ,..., .
( ) ( ) ( ) ( )1s.t 1 n tnn n t tn tω ω ω ω−∈ ∀ > ∈ ∀xxx X y Y ( )ˆ ˆˆ ˆ,
ω∈Ω
Anticipated Adaptation-Response Model
& non-anticipativity of ( )xn ω
U R( )uI τ( ) ( ){ }opt yu uC I τR
*1x
User Response Decisions
( )( )
( ) ( ){ }y Y
opt yx*
n
C Iτ
τ τ
τ τ∈
RuTτ ∈
A. Martel - Designing Robust Value-Creating Supply Chain Networks 45
Generic SCN Design ModelUsing Stochastic Programming (Shapiro, 2007), Robust Optimization(Kouvelis et al., 1997) and Risk Analysis (Haimes, 2004) concepts, the design problem can be formulated as follows:
( ){ } ( ){ } ( ){ }{ }1 1
1 1 1maxx X
x x x, . , , . , , .A S UA S UC C C
Ω Ω Ω∈R R R
Conditional dispersion measure
Conditional return measure
Conditional expected value measure
{ } { } { }( ){ } ( ){ } ( ){ } [ ]1 1 1 0 1x x x, . , . , . , ,A A AA AA A AC C Cϕ ϕ
Ω Ω Ω= + ∈ER D
( ){ } ( ){ } ( ){ } [ ]1 1 1 0 1x x x, . , . , . , ,S S SS SS S SC C Cϕ ϕ
Ω Ω Ω= + ∈ER D
Multiparametric program
Robustness criterion( ){ } ( ){ }1 1, . ,U U UUC Min C
ωω
Ω ∈Ω=x xR D
Multiparametric program( ){ } ( ){ } ( ){ }
1 11 1 1 1max ( ) (1 ) ,. ,. ,.A S UA SA S U
R w C w C Cψ ψΩ Ω Ω∈
⎡ ⎤= − + +⎣ ⎦x Xx x x xR R R
A. Martel - Designing Robust Value-Creating Supply Chain Networks 46
Complexity Reduction Approachp y pp
• Use approximate anticipations of adaptation-response decisions to simplify the combinatorial structure of the design model (based on spatio-temporal aggregations)U l i di l t d l d i k i• Use only primordial expected value and risk aversion criteria associated to probabilistic scenarios
• Assuming that the SCN design problem will be solved on a• Assuming that the SCN design problem will be solved on a rolling horizon basis, reduce the design model to a multi-cycle two-stage stochastic program with recourse y g p g
• Solve the design model for several small samples of scenarios generated using Monte Carlo methodsg g
• Evaluate the designs obtained using a user response model
A. Martel - Designing Robust Value-Creating Supply Chain Networks 47
SCN Design ApproachSCN Design Approach2Design Generation
( ) 1miSAA i IΩ =, , ..., Risk-attitude weights based on and A Sπ π
SCN design models- Resilience formulationA i i i
( , 1,..., ), ,Pmi i I P A SΩ = =
Small samples replications
… ( , 1,..., ), ,iPw i I P A S= =
- Anticipation - Solution method
1
1j j J=xPlausible Future
Scenario Generation
3
Status quo1 , 1,...,j J=x0
1x
Monte-Carlo Design Evaluation & SelectionAd t ti ti i ti
Hazard tolerance
, ,PM P A SΩ =*1x
evaluation samples•Adaptation-response optimization
• Performance measures
( ) ( ) ( )1 1 1 , , , ,d du Mj j jC C C ωω ω ω ∈+= Ωx x x Worst-case scenarios
Multi-criteria evaluation
Effective and Robust D i
level (κ) &Risk-aversionovershoot (Δ)
( )1 , ,du MjC ωω ∈Ωx
UMΩ
• Filteringand selection
( ){ }1 1,. , , , ; ( )MP
j jP C P A S U RΩ =x xR
Wo st case sce a os
Historical scenario
Design Ω
0( )ω
A. Martel - Designing Robust Value-Creating Supply Chain Networks 48
Design Generation for the Multi-depot location-transportation problemMulti depot location transportation problem
Several types of models and solution methods can b d l i d ibe used to generate alternative designs
• Exact solution to static deterministic location-transportation modelE t l ti t t ti d t i i ti l ti ll ti d l• Exact solution to static deterministic location-allocation model
• Multi-period versions of the previous models• Previous models with customers aggregated into demand zonesPrevious models with customers aggregated into demand zones• Stochastic versions of the previous models with different scenario
samples• Stochastic models with resilience structures (back-up depots…)• Heuristic solutions to the previous models• Solution with different expected value / dispersion weights• Solution with different expected value / dispersion weights• …
A. Martel - Designing Robust Value-Creating Supply Chain Networks 49
Design Evaluation/Selection for the Multi-depot location-transportation problemMulti depot location transportation problem• Based on a large sample of Monte-Carlo, worst-case and
historical scenarioshistorical scenarios• For each design x1 and each day τ of scenario ω :
• Assign customer orders to depots based on response policy • Assign customer orders to depots based on response policy • Request transportation at depots for truckloads• Solve depots routing problemsp g pTo get the design value
• Compute adequate performance measures1( , )R ω x
• Expected values• Dispersion measures (mean-semideviation, conditional value at risk… )
R ili• Resilience measures …
• Select the best design using multi-criteria decision methods
A. Martel - Designing Robust Value-Creating Supply Chain Networks 50
SCN St diSCN-Studio
1) Plausible Future Scenario Generation
2) Design Generation3) Design Evaluation and ) g
Selection
A. Martel - Designing Robust Value-Creating Supply Chain Networks 51
4- Research Challengesg
• SCN risk analysis• SCN multihazard modeling
• Scenario development and importance sampling• Scenario development and importance sampling
• Value based SCN design models• Dependence on value attributes & Financing
• Modeling for robustness• Modeling for robustness• Modeling resilience and responsiveness
• Solution methods
A. Martel - Designing Robust Value-Creating Supply Chain Networks 52
ReferencesReferences• Klibi Walid, Martel Alain, Guitouni Adel, The Design of Robust Value-Creating Supply Chain Networks: A
Critical Review, European Journal of Operational Research, 203(2), 283-293, 2010Klibi W lid L ll F i M l Al i I h S i Th h i l i i d l i• Klibi Walid, Lasalle Francis, Martel Alain, Ichoua Soumia, The stochastic multi-period location-transportation problem, Transportation Science, 2010 (v1-trsc.1090.0307 )
• Klibi Walid, Martel Alain, Designing resilient supply networks under disruptions, Document CIRRELT-2009-27, 2009
• Klibi Walid Martel Alain The design of effective and robust supply chain networks Document CIRRELT• Klibi Walid, Martel Alain, The design of effective and robust supply chain networks, Document CIRRELT-2009-28, 2009
• Martel Alain, The desing of production-distribution networks : a mathematical programming approach, Springer, in J. Geunes and P.M. Pardalos (eds.), Supply Chain Optimization, pp. 265-306, 2005
• Martel Alain, M'Barek W., D'Amours Sophie, L'influence des facteurs internationaux sur la compétitivité , , p , pdes réseaux de création de valeur multinationaux : le cas des compagnies canadiennes de pâtes et papiers, Revue Gestion, vol 31, no 3, pp. 85-96, 2006
• Martel Alain, Benmoussa A., Ezzedine I., Klibi Walid, Berger Jean, Boukhtouta A., Chouinard M., Girard S., Kettani Ossama, Military Missions Scenario Generation for the Design of Logistics Support Networks, I t ti l C f I f ti S t L i ti d S l Ch i (ILS) C bl M 2010International Conference on Information Systems, Logistics and Supply Chain (ILS), Casablanca, Maroc, 2010
• Vila D., Martel Alain, Beauregard Robert, Designing logistics networks in Divergent Process Industries: A Methodology and its Application to the Lumber Industry, International Journal of Production Economics, Vol. 102, pp. 358-378, 2006
• Vila D Martel Alain Beauregard Robert Taking market forces into account in the design of production-• Vila D., Martel Alain, Beauregard Robert, Taking market forces into account in the design of production-distribution networks: A positioning by anticipation approach, The Journal of Industrial and Management Optimization, Special edition on Supply Chain Optimization, Volume 3, No. 1, pp. 29-51, Février, 2007
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