Pulp and Paper p pSupply Chain Managementpp y g

2009 Summer School2009 Summer School Supply Chain and Transportation Optimization within the Forest Industry

Mikael Rnnqvist

The Norwegian School of Economics and Business Administration, Bergen, NorwayThe Forestry Research Institute of Sweden (Skogforsk), Uppsala, Sweden

OutlineOutline

Introduction to pulp and paper supply chain Introduction to pulp and paper supply chainmanagement (SCM)

Strategic SCM Terminal location transport mode selectionTerminal location, transport mode selection

Tactical SCM Production planning, collaboration in transportation

Operational SCMOperational SCM Routing, process control

ReferencesReferences D. Carlsson, S. DAmours, A. Martel and M. Rnnqvist, Supply chain management in the pulp and paper

industry, to appear in INFOR.

M. Frisk, M. Gthe-Lundgren, K. Jrnsten and M. Rnnqvist, Cost allocation in collaborative forest transportation, accepted for publication in European Journal of Operational Research.

P. Flisberg, M. Rnnqvist and S. Nilsson, Billerud optimizes its bleaching process using online optimization, Interfaces,Vol. 39, No. 2, 119-132, 2009.

P. Flisberg, B. Liden and M. Rnnqvist, A hybrid method based on linear programming and tabu search for routing of logging trucks, Computers & Operations Research, Vol. 36, 1122-1144, 2009.

S. DAmours, M. Rnnqvist and A. Weintraub, Using Operational Research for supply chain planning in the forest product industry, INFOR, Vol. 46, No. 4, 47-64, 2008.

H. Gunnarsson and M. Rnnqvist, Solving a multi-period supply chain for a pulp company using heuristics An application to Sdra cell AB, International Journal of Production Economics, Vol. 116, 75-94, 2008.

G. Andersson, P. Flisberg, B. Liden and M. Rnnqvist, RuttOpt A decision support system for routing of logging trucks, Canadian Journal of Forest Research, Vol. 38, 1784-1796, 2008.

H. Gunnarsson, M. Rnnqvist and D. Carlsson, A combined terminal location and ship routing problem, Journal of the Operational Research Society, Vol. 57, 928-938, 2006.

M. Forsberg, M. Frisk, and M. Rnnqvist, FlowOpt a decision support tool for strategic and tactical transportation g, , q , p pp g pplanning in forestry, International Journal of Forest Engineering, Vol. 16, No. 2, pp. 101-114, July 2005.

D. Carlsson and M. Rnnqvist, Supply chain management in forestry case studies at Sdra Cell AB, European Journal of Operational Research, Vol 163, pp. 589-616, 2005.

D. Bredstrm, J. T. Lundgren, M. Rnnqvist, D. Carlsson and A. Mason, Supply chain optimization in the pulp mill , g , q , , pp y p p pindustry IP models, column generation and novel constraint branches, European Journal of Operational Research, Vol156, pp 2-22, 2004.

IntroductionIntroduction

A ti i t d d l t f th l b l fl f d fibAnticipated development of the global flow of wood fibres(Source: Axelsson, Sdra Cell AB)

Illustration of the global producers by country. (Source Jaako Pyry)

Procurement Production Distribution Sales Strategic Wood procurement strategy Location decisions Warehouse location Selection of markets

The Pulp and Paper Supply Chain Planning Matrix.

Strategic Wood procurement strategy (private vs public land)

Forest land acquisitions and harvesting contracts

Silvicultural regime and regeneration strategies

Harvesting and transportation

Location decisions Outsourcing decisions Technology and

capacity investments Product family

allocation to facilities Order penetration point

Warehouse location Allocation of

markets/customers to warehouse

Investment in logistics resources (e.g. warehouse, handling

Selection of markets (e.g. location, segment) customer segmentation

Product-solution portfolio

Pricing strategy Harvesting and transportation technology and capacity investment

Transportation strategy and investment (e.g. roads construction, trucks, wagons, terminal, vessels, etc.)

Order penetration point strategy

Investment in information technology and planning systems (e.g. advance planning and scheduling

technologies, vessels) Contracts with

logistics providers Investment in

information technology and

l i t (

Services strategy Contracts Investment in

information technology and planning systems (e.g.

planning systems (e.g. warehouse execution

On-line tracking system, CRM)

Tactical Sourcing plan (log class planning)

Harvesting aggregate planning

Campaigns duration Product sequencing in

campaign Lot-sizing

Warehouse management policies (e.g. dock management) S l i

Aggregate demand planning per segment

Customer contracts Demand forecasting,

Route definition and transshipment yard location and planning

Allocation of harvesting and transportation equipment to cutting blocs and All ti f d t/bl t

Outsourcing planning (e.g. external converting)

Seasonal inventory target

Parent roll assortment

Seasonal inventory target at DCs

Routing (Vessel, train and truck)

3PL contracts

safety stocks Available to promise

aggregate need and planning

Available to promise allocation rules (i l di ti i Allocation of product/bloc to

mills Yard layout design Log yard management

policies

optimization Temporary mill

shutdowns

(including rationing rules and substitution rules)

Allocation of product and customer to mills and distribution centers

Operational Detailed log supply planning

Forest to mills daily carrier selection and routing

Pulp mills/paper machines/winders/ sheeters daily production plans

Mills to

Warehouses/DCs inventory management.

DCs to customer daily carrier selection

Available to promise consumption

Rationing Online ordering Customer inventoryconverters/DCs/

customers daily carrier selection and routing

Roll-cutting Process control

and routing Vehicle loads

Customer inventory management and replenishment

Planning horizon and scope of the reviewed literature

Strategic planningStrategic planning

Supply chainSupply chaindesign at Sdra Cellg

The Sdra Group

Sdra Cell Processing wood into pulp

Sdra Skog Purchasing and trading Processing wood into pulp

Five mills Producing 2 million tons

P i 9 illi bi t

Purchasing and trading 12,5 million cubic meters Five regions, 51 districts

Processing 9 million cubic meters

Sdra Timber Processing logs into sawn timber

Six mills Producing 1 million cubic meters Processing 2 million cubic metersg

Case: Sdra Cell - millsSdra Cell in total

Capacity: ~2 million tons

Case: Sdra Cell millsCapacity: 2 million tonsWood: ~9 million cu.m.*

*cu.m. = cubic meters, solid under bark

CustomersSKOGNSKOGNSKOGNSKOGNSKOGNSKOGNSKOGNSKOGNSKOGN

Terminaler

Folla (4)Svenska (17)Svenska, Tofte (2)Tofte (7)

Terminals

HALDENHALDENHALDENHALDENHALDENHALDENHALDENHALDENHALDEN

HNEFOSSHNEFOSSHNEFOSSHNEFOSSHNEFOSSHNEFOSSHNEFOSSHNEFOSSHNEFOSS

GRANGEMOUTHGRANGEMOUTHGRANGEMOUTHGRANGEMOUTHGRANGEMOUTHGRANGEMOUTHGRANGEMOUTHGRANGEMOUTHGRANGEMOUTH

CORPACHCORPACHCORPACHCORPACHCORPACHCORPACHCORPACHCORPACHCORPACH

INVERNESSINVERNESSINVERNESSINVERNESSINVERNESSINVERNESSINVERNESSINVERNESSINVERNESS

ABERDEENABERDEENABERDEENABERDEENABERDEENABERDEENABERDEENABERDEENABERDEEN

DUNDEEDUNDEEDUNDEEDUNDEEDUNDEEDUNDEEDUNDEEDUNDEEDUNDEE

SUNDERLANDSUNDERLANDSUNDERLANDSUNDERLANDSUNDERLANDSUNDERLANDSUNDERLANDSUNDERLANDSUNDERLAND

TERNEUZENTERNEUZENTERNEUZENTERNEUZENTERNEUZENTERNEUZENTERNEUZENTERNEUZENTERNEUZEN

EMDENEMDENEMDENEMDENEMDENEMDENEMDENEMDENEMDEN

NORTHFLEETNORTHFLEETNORTHFLEETNORTHFLEETNORTHFLEETNORTHFLEETNORTHFLEETNORTHFLEETNORTHFLEET

CHATHAMCHATHAMCHATHAMCHATHAMCHATHAMCHATHAMCHATHAMCHATHAMCHATHAM

LOWESTOFTLOWESTOFTLOWESTOFTLOWESTOFTLOWESTOFTLOWESTOFTLOWESTOFTLOWESTOFTLOWESTOFT

HULLHULLHULLHULLHULLHULLHULLHULLHULL

ANTWERPENANTWERPENANTWERPENANTWERPENANTWERPENANTWERPENANTWERPENANTWERPENANTWERPEN

GRIMSBYGRIMSBYGRIMSBYGRIMSBYGRIMSBYGRIMSBYGRIMSBYGRIMSBYGRIMSBY

BREMENBREMENBREMENBREMENBREMENBREMENBREMENBREMENBREMENSTETTINSTETTINSTETTINSTETTINSTETTINSTETTINSTETTINSTETTINSTETTIN

WESELWESELWESELWESELWESELWESELWESELWESELWESEL

KIELKIELKIELKIELKIELKIELKIELKIELKIEL

GHENTGHENTGHENTGHENTGHENTGHENTGHENTGHENTGHENT

BOULOGNEBOULOGNEBOULOGNEBOULOGNEBOULOGNEBOULOGNEBOULOGNEBOULOGNEBOULOGNE

BASELBASELBASELBASELBASELBASELBASELBASELBASEL

GENUAGENUAGENUAGENUAGENUAGENUAGENUAGENUAGENUA

LA PALLICELA PALLICELA PALLICELA PALLICELA PALLICELA PALLICELA PALLICELA PALLICELA PALLICE

PASAJESPASAJESPASAJESPASAJESPASAJESPASAJESPASAJESPASAJESPASAJES

AVEIROAVEIROAVEIROAVEIROAVEIROAVEIROAVEIROAVEIROAVEIRO

GANDIAGANDIAGANDIAGANDIAGANDIAGANDIAGANDIAGANDIAGANDIA

Sdra Cell - Supply ChainWood supply Production Distribution

Sdra Cell Supply Chain

Woodterminal

port

al

Shi

pmen

t p

Term

ina

External

Procurement:Harvest ing Transport

Production:Pulp product ion

Distribution:Haulage of pulp

Sales:Supplying the customerHarvest ing, Transport Pulp product ion Haulage of pulp

Harvest calculation: (5yr)- Forest policy implicat ions

Strategic planning: (5yr)- Investments at mills - Time chartering of vessels - Geographic regions

Supplying the customer

p y p- Domest ic/Import- Transport capacity

Wood supply balancing: (1yr)

g g p g- Terminal st ructure - Segments

- Strategic customers

Budgeting/Prognosis: (1yr) Budgeting/Prognosis: (1yr)- Volume Domest ic/Import- Harvest volume per Region- Wood-exchange

Wood supply planning: (3m)

- Product ion capacity per product Customer demands perproduct

- Volumes per product per millTransport volumes (purchase)

Production planning: (3m) Inventory planning ( policies):

- Contracts regular customers- Est imat ion other customers

O d l tWood supply planning: (3m)- Requirement per assortment dist ributed per Region

Production planning: (3m)- Campaigns per mill

Inventory planning (-policies):- Vessel routes/t ime tables

- Orders regular customers- Est imat ion other customers- SMI* -forecasts medium

term

Catchment areas: (1m)- Region -> Mill per

assortment- Back-haulage- Chips-exchange

Change-overs: (1m)- Fine tuning of change-overs

(day/t ime)s.t . Customer orders, stocks

etc

Operative transport plan.: (1m)- Vessel rout ing- Load-planning at mills.t . Sea/Train/Truck - volumes

- Call-of fs regular customers- Orders other customers- SMI-forecasts short term

p g

Operative transportation- Vehicle rout ing (Daily)- Combinat ion of other

t ransports

Operative transportation- Vehicle rout ing (Daily)- Combinat ion of other

t ransports

Operative: (Daily)- Order/delivery process

t ransportst ransports

Inventories / Lead times

30 60

>15% of volume => 320 000 tonnes

30 60

>15% of volume => 320 000 tonnes 150 million

Storage cost (20%) 30 million

PulpSMI:pSupplier Managed Inventory

Sdra Cell responsibilityCustomer responsibility

Security stock(5-10 days)

TerminalsSdra Cell Folla

Sdra Cell Tofte

Sdra Cell MnstersSd C ll M

Sdra Cell Vr

G th Sdra Cell Mrrum

SwinoujscieBrake

Grangemouth

TerneuzenChatham

Basel

Genova

La Pallice

Pasajes

Delivery alternativesSdra Cell Folla

Sdra Cell Tofte

Sdra Cell MnstersSd C ll M

Sdra Cell Vr

Sdra Cell Mrrum

SwinoujscieBrake

TerneuzenTerneuzen

Basel

Distribution - ComplexityHALDEN

TofteDistribution Complexity

Vr Three vessels on long term contract

Additional vessels on spot marketp

Train and truck transports

EMDENHULLHULLHULLHULLHULLHULLHULLHULLHULLGRIMSBYGRIMSBYGRIMSBYGRIMSBYGRIMSBYGRIMSBYGRIMSBYGRIMSBYGRIMSBY STETTIN

KIEL Several different products

Supply Demand

NORTHFLEET

LOWESTOFTLOWESTOFTLOWESTOFTLOWESTOFTLOWESTOFTLOWESTOFTLOWESTOFTLOWESTOFTLOWESTOFTBREMEN

WESEL

Supp y e d

Alternative shipment ports

GHENT

TERNEUZENNORTHFLEET

CHATHAM ANTWERPEN

WESELshipment ports

Alternative terminals

Supply chain design atSupply chain design at Sveaskogg

Case study - Sveaskog Major Swedish forest

company (Sveaskog) withcompany (Sveaskog) with 16% of overall productiveforest area

Using one train system, Trtget

Study to use a new system, Bergslagspendeln, with a

b f i lnumber of potential terminals

Classical transportation problemp p

ixijk demand topoint supply from flow=kj assortment with point

Norra

Simple approach

NorraNorra

Simple approach

SalaHeby Hallstavik

SalaHeby Hallstavik

SalaHeby Hallstavik

stra

Sala

stra

Sala

stra

Sala

VstraVsters VstraVsters VstraVsters

Norra

Backhaulage tour

NorraNorra

Backhaulage tour

SalaHeby Hallstavik

SalaHeby Hallstavik

SalaHeby Hallstavik

straVst aV te

straVst aV te

straVst aV te VstraVsters VstraVsters VstraVsters

Catchment areas - comparisonCatc e t a eas co pa so

Case studyCase study

1,500 supply points, 220 industrial demands, 5 train routes 10 potential terminals5 train routes, 10 potential terminals, 12 products, 8 product groups, five scenarios. 3,000 constraints, 30 million variables Solution time 1 minute several hours Solution time 1 minute several hours Truck transports reduced by 35% and overall

energy 20%

Tactical planning integrated productionTactical planning integrated productionand transportation planning at Sdra Cell

Supply chainWood supply Production Distribution

Supp y c

Woodterminal Ship-

ment

Term-inals

port

External

Supply chain structureSupply chain structure

Forestry Districts M

ills Domestic

Customers

SawmillsSwedish Harbours

Districts

a P

ulp

M Customers

Sd

ra

Import International Customers

Production Plans

S90Z S90TZ S85Z S85SZ S70Z SBZ S90Z

Sdra Cell, Supply Chain - conditionsDaily variation due to campaign production

, pp y

Stockat mill

Usagein mill

Deliveriesto mill

campaign production

8 0 0

9 0 ,0

8 0

9 ,0

(0-90 000) (0-8 000)(0-9 000)

6 0 ,0

7 0 ,0

8 0 ,0

6 ,0

7 ,0

8 ,0

3 0 0

4 0 ,0

5 0 ,0

3 0

4 ,0

5 ,0

1 0 ,0

2 0 ,0

3 0 ,0

1 ,0

2 ,0

3 ,0

0 ,0 0 ,0January 1st to June 30th 2000

Production plansProduction plans Comparison with manual plans (campaign changesComparison with manual plans (campaign changes

35 million SEK, total 120 110 million SEK) Strategic implications regarding campaign scheduling Strategic implications regarding campaign scheduling

VARO VAS90Z 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3VARO VAS85RZ 3 3 3VARO VAS85TZ 2 2 2 3VARO VAS80TZ 3 3VARO VAS80TZ 3 3

M ONSTERAS M ONSBZ 2 2 2 2 2 2 2 3 3 3M ONSTERAS M ONS90Z 3 3 3 3M ONSTERAS M ONS85Z 2 2 2 2 2 2M ONSTERAS M ONS85S 2 3M ONSTERAS M ONS85S 2 3M ONSTERAS M ONS90S 2 3 3M ONSTERAS M STOP

M ORRUM M ORSBZ 3 3 3 3 3M ORRUM M ORS90TD 2 2 2 2 2 2 3M ORRUM M ORS90TD 2 2 2 2 2 2 3M ORRUM M ORS70TZ 2 2 2 2 2 2 2 2 2 3 3 3 3M ORRUM M ORS90RD

1 31 61

Collaboration in transportationCollaboration in transportation

Case studyCase study

St d f d t i 2004 ( thl l ) Study from data in 2004 (monthly volume) Analysis done by Skogforsk

(Th F R h I i f S d )(The Forestry Research Institute of Sweden) 8 participating companies in Sweden 898 supply points 101 demand points0 de d po s 39 assortments, 12 assortment groups Savings: Savings:

Approximately 8% by co-operation Total of 14% as compared to actual transportationTotal of 14% as compared to actual transportation Environment CO2: 5,330 tons to 4,350 tons.

Company VolumeProportion

(%)Company 1 77 361 8 76Company 1 77,361 8,76Company 2 301,660 34,16Company 3 94,769 10,73p y , ,Company 4 44,509 5,04Company 5 232,103 26,29Company 6 89,318 10,12Company 7 36,786 4,17Company 8 6,446 0,73

Total 882,952

opt1: direct flows (individual)opt2: backhaul flows (individual)

opt3: direct flows (collaboration)opt4: backhaul flows (collaboration)

Company Real opt 1 opt 2 opt 3 opt 4Company 1 3,894 3,778 3,640p y 3,89 3, 8 3,6 0Company 2 15,757 14,859 14,684Company 3 4 828 4 742 4 703Company 3 4,828 4,742 4,703Company 4 2,103 2,067 2,043Company 5 10 704 10 340 10 153Company 5 10,704 10,340 10,153Company 6 5,084 4,959 4,826Company 7 1 934 1 884 1 877Company 7 1,934 1,884 1,877Company 8 0,333 0,333 0,332

AllAll 39,253 38,315Total 44,637 42,963 42,257 39,253 38,315

Saving (%) 0,00 3,75 5,33 12,06 14,16

Cost allocation based on volumeCost allocation based on volume

Company Individual Volume %Company 1 3,778 3,439 9,0Company 2 14,859 13,411 9,7Company 3 4 742 4 213 11 2Company 3 4,742 4,213 11,2Company 4 2,067 1,979 4,3Company 5 10,340 10,318 0,2Company 6 4,959 3,971 19,9p y ,Company 7 1,884 1,635 13,2Company 8 0 333 0 287 14 0Company 8 0,333 0,287 14,0

Sum 42,963 39,253

Supply and demandSupply and demand

Game theory approach Basicsy ppplayers)(or tsparticipan ofsubset a :coalition NS

for ation transportfor thecost :tsparticipan all :coalition grand

Sc(S)NS =

p S(S)

{ }

:allocationEfficient c(N)yNj

j =

{ }:Core

)(:rationalIndividual

NSc(S)y

jcy

j

j

y)(efficienc

, :Core

c(N)y

NSc(S)ySj

j

=

y)(efficienc c(N)yNj

j =

Volume vs Shapley valueVolume vs Shapley valueCompany Volume % Shapley %

Company 1 3,439 9,0 3,586 5,1Company 2 13,411 9,7 13,528 9,0, ,Company 3 4,213 11,2 4,102 13,5Company 4 1,979 4,3 1,889 8,6p y , 4,3 , 8,6Company 5 10,318 0,2 9,747 5,7Company 6 3 971 19 9 4 503 9 2Company 6 3,971 19,9 4,503 9,2Company 7 1,635 13,2 1,587 15,8Company 8 0 287 14 0 0 310 6 9Company 8 0,287 14,0 0,310 6,9

Sum 39,253 39,253Stable No YesStable No Yes

Equal Profit Method (EPM)Equal Profit Method (EPM)

Company Volume Shapley Shadow Nucleo. EPMCompany 1 9 0 5 1 4 1 3 4 6 7Company 1 9,0 5,1 4,1 3,4 6,7Company 2 9,7 9,0 12,7 11,1 8,8Company 3 11,2 13,5 14,2 14,0 8,8Company 4 4,3 8,6 13,3 6,4 8,8Company 5 0,2 5,7 -1,8 4,8 8,8Company 6 19,9 9,2 11,7 8,3 8,8Company 7 13,2 15,8 15,6 11,5 8,8Company 8 14,0 6,9 9,1 4,6 8,8Company 8 14,0 6,9 9,1 4,6 8,8

Operational planning routingOperational planning routing

Standard vehicle routing problemg p

forest vehicle routing problemg p

542,000 km roads in Sweden-----------------------------------

102,000 government56,000 local communities

384 000 private384,000 private

Route selection with road database

LengthLengthRoad classSpeed limitS fSurface

Road widthRoad widthOwner

Gantt schedule for a dayGantt schedule for a day

Industrial demand

Accumulated demandmaxmin

max

i i dmin

Time periodDay 1 Day 2 Day 3 Day 4 Day 5

Gantt schedule for three daysGantt schedule for three days

Two phase methodTwo phase method Idea:

Use an efficient tabu search algorithm to form routes Phase 1:Phase 1:

Construct transport nodes mainly by solving a destination problem (LP) and form loads taken fromdestination problem (LP) and form loads taken from supply point(s) to demand point

Phase 2: Phase 2: Use an extended and modified tabu search to

construct routes where e g time windows and excessconstruct routes where e.g. time windows and excess supply is taken care of

Industries

Harvest areas

Home bases and change-over pointsg p

Real time planningReal time planningprocess control at Billerud

Plug flow D(EOP)DP sequencegKappa number

BrightnessKappa number

BrightnessBrightnessResidual

BrightnessResidual

D1a

EOP

D0

D1b

D2

EOP

Hydrogen peroxideOxygen

Chl i di idChlorine dioxide

(Chlorine dioxide)Hydrogen peroxide

Time: 0 h Time: 2.93 h

Time: 5.61 h

Brightness: 87.26 Time: 8.64 h

Chlorine dioxide

Brightness: 39.18 Brightness: 74.75 Brightness: 87.84Brightness: 90.61

State vector, control, and output, , p

Stage D0 Stage EOP Stage D1 Stage D2

D1a

x

2z 1z

x D1b

D2 3x 3y 4y 2y 1y

3z 4z EOP D0

4x 1x 2x D1b

3x :Variables

iii

ii

Stageafteroutputcharges) (chemical Stagein control

Stage before vector State :

===

yxzVariables

ii Stageafter output =y

P lParameter values

),,(sDefinition

= iiiii wxzFy TowerStage i

iz iy

spolynomial e thfor Parameters =iw

ix

solve we problem onoptimizati squareleast a

),,( s.t. )),,((min ][LSOP

2i

iiiii

iiiiibwxzAywxzF

Unknown- data Historical - ,,

i

iiiw

xzy

Master ProblemLinking the output with the

approximate functionsMaster Problem

Connect brightness between

Cost of chemicals

min [MP]4

1=i

iT

i xcConnect brightness between

stages

4,3,2,1)( 1,2,3,4),,( s.t.

1 ====

+ ii

iii

iiiii

yzGwxzFy

Relaxed and

1,2,3,4

1,2,3,4 maxmin

maxmin

==

ii

iii

iii

xxxyyypenalized in the

objectiveiii

Stagein chemicalsfor costs - where

i ic

Upper and lower limits of theoutput and control

numberkappaely with thequivalent and stages, ebetween th

brightness econnect th toused functions - )(][LSOP ofsolution theasfix -

gi

ii

i

i

zGw

number kappa

Support tool - information flowSupport tool information flow

Support tool

O ti l t l

Operators

OptimizationData storage

Plug flow

Optimal control values

Optimization

The bleaching process Real control l

Process

D1a

EOP

valuesdata

D0

D1b

D2

Brightness after Tower D1bBrightness after Tower D1b

90

88

84

86

Estimated Real

80

82

7816.jan 16.jan 17.jan 17.jan 18.jan 18.jan 19.jan 19.jan 20.jan

Cost of manual run

Kr/(ton o kappa)

13

14

15

Real cost

10

11

12

8

9

Opt cost

Period of three weeks, > 9 % difference

Concluding remarksConcluding remarks

There are many planning problems that can be There are many planning problems that can be coordinated and/or optimized in the pulp and papersupply chain and which provide large savingssupply chain and which provide large savings.

The models are a combination of nonlinear/ integerd l land large scale.

There are many open and challenging problems in the pulp and paper supply chain, e.g. robustness, uncertainty, collaboration