designing robust value-creating supply chain...

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)

A. Martel - Designing Robust Value-Creating Supply Chain Networks 2

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


1. Generic Design ProblemgRaw material sources

Deployed Supply...

ManufacturingProcess

Chain Network

SC Risk ExposureFinished Products

DistributionChannels

V l C tiValue Destruction

DisruptionsMarkets

...

Value CreationRobustness ?


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


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


2. Modeling SCN Process and Structures Structures


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


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


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)


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)


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


Potential Theater Locations(Demand Zones ) (Demand Zones )


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


Systems in a PlatformyPlatform

Systemy


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


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


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∈


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


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)


Design ObjectiveDesign for Value

DesignDiscounted Total Revenue Maximize

Costs(Revenues)

MarginMargin

EconomicValueMarginMargin Value

AddedExpenditures

Design Response Time (or other value attribute)


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


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


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


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


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


DSN Risk Analysis:What is the likelihood of that happening ?Exposure Levels for Natural Disasters

f pp g

Derived from data provided by the


Center for Research on the Epidemiologyof Disasters (CRED)

Vulnerability-Exposure Relationship


Inter-arrival Time DistributionFor Natural Catastrophes’ Exposure Level 5

Exponential distribution with mean λ = 350 daysExponential distribution with mean λ = 350 days

DaysDays


Intensity Distributionte s ty st but oFor Natural Catastrophes’ Exposure Level 5L N l di t ib tiLog-Normal distribution

Loss level in $


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


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… …


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


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


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


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


Design ObjectiveRobust Design

EconomicValueValue Added

S t i bl Sustainable StakeholderValue

Time (Planning horizon)


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


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


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∈


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 ∈


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


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


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


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• …


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


SCN St diSCN-Studio

1) Plausible Future Scenario Generation

2) Design Generation3) Design Evaluation and ) g

Selection


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


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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