American Journal of Industrial and Business Management, 2013, 3, 631-643
Published Online November 2013 (http://www.scirp.org/journal/ajibm)
http://dx.doi.org/10.4236/ajibm.2013.37073
Open Access AJIBM
631
Supply Chain Network Optimization of the Canadian
Forest Products Industry: A Critical Review
Shashi Shahi, Reino Pulkki
Faculty of Natural Resources Management, Lakehead University, Thunder Bay, Canada.
Received September 25th, 2013; revised October 25th, 2013; accepted October 30th, 2013
Copyright © 2013 Shashi Shahi, Reino Pulkki. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
The Canadian forest products industry has failed to retain its competitiveness in the global markets because of the un-
der-utilization of its resources. Supply chain optimization models can identify the best possible fibre utilization strate-
gies from multiple options of value creation based on fluctuating market conditions in the forest industries. This paper
comprehensively reviews the literature related to supply chain models used both in gene ral and specifically in th e forest
products industry. The optimization models use information from multiple agents (market demand attributes, flexible
wood procurement and manufacturing processes, and resource characteristics), and share this information at each level
in the supply chain network. However, the modeling of two-way flow of information (market to forests and vice-versa)
for order promising and demand fulfillment through all facilities including manufacturing, processing, raw material
procurement and inventory control is missing. The studies that fo cus on optimization are mostly deterministic in nature
and do not account for uncertainty both in supply of raw materials and demand of forest products. Simulation and opti-
mization models have been independently used for supply chain management in the past. The literature lacks an inte-
grated approach that combines simulation and optimization models throughout the supply chain network of the Cana-
dian forest products industry. Further studies should focus on developing simulation-based optimization models that
will help in providing an operational plannin g tool that meets industrial exp ectations and provides much better solutio ns
than current industrial practice.
Keywords: Agent-Based Optimization; Discrete-Event Simulation; Forest Products Ind us t ry ; Two- W ay In fo rmation
Flow; Uncertainty
1. Introduction
One of the leading manufacturing and export sectors of
Canada, the forest products industry, has been in crisis
for the past few decades due to new trends in globa-
lization and recent economic challenges [1]. In the last
decade, the market value of Canada’s forest products
substantially declined as a result of the decrease in North
American housing starts, falling lumber prices and a
fluctuating Canadian dollar [2]. Demand has also de-
creased for paper and pulp due to global recession, and
for newsprint as a result of declining readership and ad-
vertising shifts to internet. It has been suggested that
competitiveness of the Canadian forest products industry
can be improved through diversification and aggressive
pursuit of new markets [3]. Although diversification of
forest resource-based industry presents many opportu-
nities in the emerging bio-economy, these opportunities
are dependent on coordinated involvement of the entire
supply chain network.
Supply chain networks are a system of distributed fa-
cilities/organizations, where material and information
flow in many directions within and across organizational
boundaries through complex business networks of sup-
pliers, manufacturers and distributors, to the final cus-
tomers [4]. The forest products supply chain is similar to
other industries, in the sense that the forest-based bio-
mass material flows from forests (usually collected by
forest contractors), to primary production facilities (lum-
ber and pulp industry), to secondary facilities (value-
added forest industry), and finally through a network of
distributors to individual customers. However, the forest
products supply chain network is characterized by dis-
assembly of the raw-material (tree), unlike the conven-
tional supply chains which have a convergent product

Supply Chain Network Optimization of the Canadian Forest Products Industry: A Critical Review
632
structure of assembly of different materials (Figure 1).
Different parts of the tree are utilized for making several
products along the production process in the forest
industry. It has been observed that from a mature tree,
17% of the tree material is utilized for production of saw
logs for lumber and specialty products, 74% of the tree
material is used for production of pulpwood, which in-
cludes 14% for production of engineered products and
60% for production of pulp and paper products, and the
remaining 9% of the tree is logging residue that can be
used for the production of bioenergy [5]. Moreover, the
properties of wood are highly varied within a tree and
between trees of the same species, which make the whole
production planning and management process very cum-
bersome. With the shifting forest management paradigm
from volume-based to value-based, op timal utilizatio n of
wood fibre has become important for value addition in
wood supply ch ains.
In this context, the one-way market push model of the
forest products industry in Canada cannot improve its
competitiveness, as it does not incorporate market de-
mand signals and the information flow is restricted.
Meanwhile, it does not flow in many directions along
and across the supply chain. The two-way modeling of a
series of value generating activities, both upstream
(market to mill to tree) and downstream (tree to mill to
market), with the available cost, quality, yield and value
data on each value chain level, can provide support
system with an improved decision and capitalize on the
comparative advantages of the Canadian forest products
industry. Further, there are potential constraints related to
inbound logistics (warehousing of raw materials and their
sequencing for manufacturing), operations management,
outbound logistics (warehousing and distribution of fini-
shed goods, marketing and sales), and post-sale service.
These constraints lead to uncerta inties both in future feed-
stock supply (due to changing global trade regulations
and environmental policies) and forest products demand
(due to prevailing volatilities in the business env ironment
with constantly changing customer expectations).
Operations management tools that optimize three ma-
jor activities: harvesting, transportation and production
(including inventory) have been used in primary and
secondary manufacturing industries. The focus of these
models has been to maximize profit margin for a given
level of market demand by changing production plans
and optimally overseeing the reactivity and contingency
involved. However, the modeling of supply chain
optimization for multiple agents in forest industries is a
complex problem, because it involves identifying the
best possible fibre utilization strategies from multiple
options of value creation based on fluctuating market
conditions. There are very few studies in optimization
modeling that consider uncertainties in demand and
supply in forest products industries, and none consider
uncertainties in both demand and supply.
Lack of quality data is one of the biggest limitations
for operational modeling of supply chain networks at
different stages of the value chain. Moreover, the supply
Figure 1. Forest pr oduc ts industries supply chain network.
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Supply Chain Network Optimization of the Canadian Forest Products Industry: A Critical Review 633
chain management data must be continuously re-eva-
luated and refined to match reality at each stage, thereby
requiring models that can handle larg e variability in data.
Simulation models have been used in some industries
that can accommodate a large amount of variability and
are usually easier to comprehend by endusers. Three ty-
pes of simulation models (discrete event, continuous and
Monte Carlo) have been used for understanding the dy-
namics of the supply chain and in determining the out-
come of different scenarios [6].
As supply chain networks are becoming more and
more global, process coordination is considered crucial
for successful business management. Information sharing
becomes a key-point at certain levels of the supply chain
network [7]. As there are several analogies between a
company in a business network and an agent, the multi-
agent system paradigm has been found to be the most
suitable approach for modeling supply chain networks
[1]. The agent-based modeling approach is, however,
lacking in the forest products industry. Moreov er, to han-
dle both nonlinear and stochastic ele ments the integration
of an agent-based simulation model with an optimization
model is required. Such an integrated dynamic environ-
ment allows for the evaluation of new technologies, like
collaborative planning, forecasting and replenishment,
and quantifies the demand variation from the point-of-
sale to the suppliers (called bullwhip effect), in the sup-
ply chain network management.
The purpose of this paper is to provide a comprehen-
sive review of the literature related to supply chain mo-
dels in general and forest products supply chain models
in particular. More specifically, the review focu ses on: (i)
supply chain networks optimization models; (ii) simu-
lation models; and (iii) integrated simulation-based op-
timization models.
2. Supply Chain Network Optimization
Models
Supply chains are networks that connect the raw material
sources to finished products consumers through manu-
facturing activities and distribution channels [8,9]. The
research literature on supply chain management is ra-
pidly growing, offering different classifications of sup-
ply chain models. Depending on the operational level of
the problem, supply chain models are broken down into
strategic, tactical or operational hierarchies [10,11].
Strategic planning is at the highest level and the supply
chain models at this level are concerned with broad-scale
decisions over long periods of time that give a firm
competitive advantage over its competitors [10]. The
strategic planning supply chain models identify the types
of actions that need to be taken, but do not plan the im-
plementation steps for those actions [11]. An example of
a strategic planning decision would be deciding the lo-
cation of a manufacturing facility in a production-dis-
tribution network. On the other hand of the spectrum is
operational planning, concerned with regular operations
of the supply ch ains, with time spans ranging from a day
to a few weeks. For example, schedu ling truck routes for
transporting logs from specific harvest sites to specific
destinations is an example of operational planning [10].
Tactical supply chain models can provide a link between
the two ends of the decision level spectrum. Tactical
models translate the strategies into appropriate opera-
tional level targets [11]. These models ensure that the
strategic goals are feasible at the operational level. For
example, harvest scheduling at the strategic level may
identify some area of a certain age class that needs to be
harvested on a land base [11]. A tactical supply chain
model then provides more spatial details about specific
stands that should be harvested in a specific order.
Supply chain models have also been classified into
centralized and decentralized models based on how de-
cisions are made [12]. In centralized supply chain models,
all procurement, production and distribution decisions
are made by a central unit, considering the state of the
entire system. This ensures a higher level of control and
collaboration among all supply chain members and a
globally optimum decision. Traditionally, many of the
models in the supply chain management literature have
utilized centralized decision making. However, some-
times it is not realistic to assume that all decisions can be
controlled centrally, especially if the supply chain mem-
bers do not belong to the same organization. Each firm
may aim to maximize its benefits without considering the
impact on the whole system. Moreover, different firms
may not be willing to share their cost and price infor-
mation with others. In such cases, decentralized models
are more appropriate [12]. Decentralized supply chain
models allow individual supply chain members to make
decisions based on their own goals, while still operating
in the same environment that inevitably affects all mem-
bers [12]. This reflects the decision making process in
many real world systems and simultaneously decreases
the model complexity, particularly in the case of larger
supply chains that may be very difficult to model with
centralized modeling techniques [12].
Finally, another approach to classify supply chain
models is based on the modeling approach and solution
method [12]. Under this classification scheme, supply
chain models can be broadly categorized into optimiza-
tion and simulation models. Optimization models use
mathematical programming approaches to find a feasible
and optimal solution to the supply chain problem such as
designing a transportation network, or locating a new
plant [13]. Alternatively, simulation models allow the
decision makers to see the performance of the supply
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Supply Chain Network Optimization of the Canadian Forest Products Industry: A Critical Review
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chain over time under various scenarios and help them
understand the inter-relationships between different mo-
del components [13]. Optimization models are mostly
centralized, while simulation models can more easily re-
present decentralized decision making [13]. Simulation
and optimization have also been combined for supply
chain management in the manufacturing industries. In
fact, simulation based optimization has become a popular
approach, mainly because of its ability to in corporate un-
certainty into optimization prob lems [14-16].
2.1. Supply Chain Network under Uncertainty
Supply chain networks have numerous sources of de-
mand and supply uncertainty at different levels. However,
most of the existing supply chain models are determinis-
tic and do not account for any uncertainty [17-19]. The
few supply chain models that account for uncertainty
follow different approaches. One part of the effort has
been oriented through control theory in which uncer-
tainty is modeled as disturbances in a dynamic model [20,
21]. Another approach deals with uncertainty through
fuzzy programming at the strategic level [22]. A third
group and the biggest one include statistical analy-
sis-based methods in which it is assumed that the uncer-
tain variable follows a particular probability distribution
[23]. Most research studies in the third group apply an
adaptive strategy in which the supply chain controls the
risk exposure of its assets by constantly adapting its op-
erations to unfolding demand realizations [24,25]. Lit-
erature also reveals that the most extensively studied
source of uncertainty has been demand [26-28]. However,
uncertainty could be related to many other factors such as
raw material supply, production capacity, transportation
and processing times, which are other important factors
that could seriously affect the planning decisions.
Uncertainty in supply chain models cannot be handled
by deterministic optimization, and stochastic program-
ming is one of the ways to address this challenge [29-31].
Although fast optimization algorithms exist, realistic
problems involving stochasticity with sample size of up
to 60 scenarios need several hours to be solved [8,32].
Sometimes, the stochastic programming problems are too
large to solve to optimality and the co nclusions are to be
based on near-optimal solutions [33,34]. Stochastic pro-
gramming models have been used for designing produc-
tion-distribution networks in the lumber industry also,
but these work efficiently only for moderate size prob-
lems, and are much more difficult to solve to optimality
for large scale problems [35,36].
2.2. Forest Products Industry Supply Chain
Models
There has been a lot of emphasis on supply chain man-
agement in the forest industry as a result of consolidation
of upstream and downstream companies [37,38]. The
studies focus on each of the operational areas in the for-
est industry separately, examining the effect of different
management scenarios on the performance of individual
companies as well as the entire sector in different regions
and countries. It is believed that the supply chain in the
forest industry can be substantially improved if the
analysis integrates all the different steps of wood flow
from the forest to the customer [39,40]. Although such an
analysis would be extremely complicated, even a small
improvement in efficiency could result in large financial
gains, considering the large volume of wood flowing in a
supply chain. For example, a study on Quebec mills,
showed that by effectively managing all nodes in a sup-
ply chain, the overall cost can decrease [37]. Another
study in the Chilean sawmill industry found that internal
supply chain management would increase the profitabil-
ity of the sawmills by approximately 15% [38]. Ron-
nqvist [39] condu cted an extensiv e review of literature to
define the set of decisions that need to be made in a
wood products supply chain, and concluded that opera-
tions research (OR) and especially optimization can be
used as decision support tools in forestry. A few other re-
view papers also emphasize the importance of incur-
porating uncertainty and enviro nmental issues in forestry
supply chain optimization models [39,41]. D’Amours et
al. [42] further argued that there is a need for more re-
search on integrating the forest management activities
with the forest products supply chains.
A summary of studies on forest product industries
supply chain network optimization models is shown in
Table 1. Optimization studies in forestry have mainly
focused on individual areas such as harvest scheduling
and forest planning [43-45], sawmill operations [46,47],
and transportation [48,49]. However, in recent years
modeling the entire supply chain that combines tactical
and operational level decisions has received more atten-
tion [39,41,42,50,51]. Most of the studies in the forest
products industry supply chain networks optimization
have used linear programming (LP) or mixed-integer
programming (MIP) models [9,38,40,52-56]. These
models have been used to minimize the net present value
of the total cost [57], for combined facility location and
shipping route problem for pulp mills [9,52,58], and for
modeling a network of biomass energy production facili-
ties [53]. However, it was found that in general the prob-
lems solved with LP and MIP models usually include
several over-simplifications in order to keep them solv-
able. These strategic models are useful only for the case
of vertically integrated companies that manage all supp ly
chains members in a centralized manner. However, if the
objective is to model independent firms that belong to the
same supply chain, then these centralized model struc-
tures are not sufficient. This modeling approach also
oes not include any uncertainty in the model to repre- d
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Table 1. Summary of studies on forest product industr ie s supply chain network optimization models.
Author Year-Region Forest product application Optimization model approach
Gaudreault et al. [1] 2010-Eastern Canada Drying and finishing operations in softwood
lumber facility
Process planning and scheduling bas ed o n
mixed integer programming (MIP) and on
constraint programming (CP)
Shabani et al. [5] 2013-General review Forest biomass for bioenergy production A review of determ in i st ic an d s to ch as t ic
mathematical models
Vila et al. [9] 2006- Quebec, Canada International production- d i st ribution
network for softwood lumber industry. Generic mathematical programming model based on MIP.
Hultqvist and Olsson [34] 2004-Sundsvall,
Sweden Roundwood supply chain for a pulp or paper mill
Deterministic equivalent of the stochastic
scenario optimization model, solved as a
convex mixed integer qu adratic model
Vila et al. [35] 2009-Eastern Canada Lumber industry production- distribution network
Two-stage stochastic programming model
using a sample average appr oximation
method based on Monte Carlo sampling
technique
Vila et al. 2007 [36] 2007-Quebec, Canada Lumber industry inte rnational
production-distribution networks Two-stage stochastic programming model based on Monte Carlo sampling technique
Singer and Donoso [38] 2007-Santiago, Chile Production and inventory
planning of sawmill industr y Combined production and inventory plan- ning optimization model
Ronnqvist [39] 2003-Canada Wood-flow in forest industry (saw mills and pulp
and paper mills) Linear and nonlinear optimization models covering wide-range of planning per i od s
Bredstrom et al. [40] 2004-Scandinavia Pulp mills supply chain management Mixed integer optim i za tio n models using
novel constraint branching he uristic
D’Amours et al. [42] 2008-Quebec Forest products industry supply chains An overview of different planning p rob-
lems
Weintraub et al. [43] 1994- G en eral Forest spatial planning Linear program with a
column generation approach
Borges et al. [44] 1999-Minnesota, USA Forest management spatially scheduling pr oblemDynamic programming
McDill et al. [45] 2002-USA Forest harvest scheduling Mixed integer linear programming
Maness and Adams [46] 1991-USA Optimal bucking and sawing policies Linear programm ing
Ronnqvist and Ryan [48] 1995-New Zealand Sawmills and pulpmills transportation schedulesCombination of heuristic, linear optimization relaxation, an d
branch and bound approaches
Weintraub et al. [49] 1995-Santiago, Chile For est harvest scheduling and transportation
planning Mathematical programming and heuristic models
Gunnarsson et al. [52] 2006-Sweden Pulp products terminal location and ship routingMixed integer programming model
Chauhan et al. [55] 2009-Quebec Timber procurement system Mixed integer optimizati on models
Troncoso and Garrido [57] 2005-Chille Saw mill strategic planning model
(forest facilities location and freight distribu t i o n )Mixed-integer dynamic optimization model
Gunnarsson et al. [58] 2007-Sweden Pulp mill integrated transportation, production
and distribution planning Mixed integer optimization model for the entire supply chain
Lonnstedt [71] 1986- Sw eden Forest management strat egic planning Mathematical long-term forest sector model
Beaudoin et al. [74] 2007-Quebec, Canada Forest products industry supply chain tactical
planning Mixed integer programming model

Supply Chain Network Optimization of the Canadian Forest Products Industry: A Critical Review
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sent the supply chains realistically.
3. Simulation Models
Simulation is the process of designing a computer model
of a real system and conduct experiments with this model
to understand its behaviour or to evaluate strategies to its
operations [59]. Simulation models give support to the
decision-making, allowing the reduction of risks and
costs involved in a process. Simulation models can ac-
commodate the variability in in put data more readily (e.g.
different log diameters in a saw mill) and are usually
easier to comprehend by end-users [60].The discovery of
computational modeling and simulation has become the
third pillar of Science, alongside theory and experiment-
tation [61]. Science turn s to simulation, when the models
become too complicated or exact mathematical solutions
are not possible [62]. The significance of simulation de-
pends on the validity of the data, the model and the
process [61]. Simulation models have also been used in
understanding the dynamics of supply chains and in de-
termining the outcome of different scenarios [63].
Within the area of supply chain management, the ear-
liest attempts to use dynamic simulation was reported by
Forrester [64], who strove to perform a dynamic simula-
tion of industrial systems by means of discrete time mass
balances and non-linear delays. However, due to the
complexity of the models and the co mputer limitations at
that time, the work only covers small academic examples.
Frayret et al. [3] presented a generic architecture to im-
plement distributed advanced planning and scheduling
(APS) systems with simulation capabilities. APS systems
provide companies with algorithms and models for plan-
ning their activities from raw material procurement to
distribution [12]. The performance of this APS tool under
different scenarios was further studied and validated by
Lemieux et al. [65]. Simulation models have also been
combined with genetic algorithms and MIP models to
consider strategic decisions regarding facility location
and partner selection for supply chain design problems
[66]. The literature on simulation tools and techniques
used for supply chains distinguishes between three dif-
ferent approaches: discrete-event simulations, system dy-
namics, and agent-based models [63].
3.1. Discrete Event Simulations
In discrete-event simulation (DES) models, the activities
within the supply chain are represented through individ-
ual events that are carried out at separate points in time
according to a schedule [63,67,68]. DES models are the
most powerful simulation tools to consider complex sto-
chastic systems. Numerous software packages for dis-
crete-event simulation are available, both very special-
ized ones for a specific part of the supply chain, and ge-
neral ones with a high functionality in modeling and
visualization of supply ch ains [56,69]. One such example
is the Supply Net Simulator, which allows simulating the
behavior of individual members in a supply chain net-
work [70].
DES models have been used to model supply chain
networks in the forest products industry [71-74]. While
many of these studies focus on individual stages of pro-
duction and distribution, some have included the entire
supply chain. For example, Lonnstedt [71] simulated the
forest sector in Sweden to study the dynamics of cost
competitiveness in the long term, and suggested policy
changes, such as lowering taxes or interest rate to in-
crease investment in the industry. Randhawa et al. [73]
developed a discrete-event object-oriented simulation en-
vironment that could be used to model sawmills with
various configurations. Lin et al. [75] studied the benefits
of producing green dimension parts directly from hard-
wood logs by comparing four mill designs using simula-
tion. Baesler et al. [76] used simulations to identify bot-
tlenecks and factors that affect productivity (number of
logs per day) in a Chilean sawmill, and concluded that
there is a potential for a 25% increase in production.
Beaudoin et al. [74] combined a deterministic MIP and
Monte Carlo sampling methods to support tactical wood
procurement decisions in a multi-facility company, and
showed that their proposed planning process achieved an
average profitability increase of 8.8% compared to an
approach based on a deterministic model using average
parameter values.
3.2. System Dynamics
System Dynamics (SD) modeling is mainly used for
simulating continuous systems (as opposed to discrete
event simulation) [77]. An SD model is characterised by
feedback mechanism and information delays to help ex-
plain the behaviour of complex systems [77]. In SD
modeling, real-world systems are represented in terms of
stock variables (e.g., profit, knowledge, number of peo-
ple), and the information flow between these stock vari-
ables. Interacting feedback loops link the stock and flow
variables. The resulting model is a system of differential
equations and its dynamic behaviour is due to the struc-
ture of feedback loops [77].
SD approach has been combined with OR techniques
to model supply chains and further refined to study its
dynamics [78,79]. Angerhofer and Angelides [80] have
reviewed the literature on SD modeling in supply chain
management, and concluded that SD can be used in
combination with different techniques to study inventory
management, demand amplification and international
supply chain design. Very few studies have used SD to
model the forest industry supply chains. Schwarzbauer
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Supply Chain Network Optimization of the Canadian Forest Products Industry: A Critical Review 637
and Rametsteiner [81] used SD to analyze the potential
impact of sustainable forest management (SFM) certifi-
cation on forest products in the Western European forest
sector. Fjeld [82] developed the “wood supply game”
based on the Sterman Beer Game [83,84] as educational
material for students in forest logistics courses. The game
included four stages in the supply chain from the forest
to the lumber or paper retailer. Demand on the end cus-
tomer was decided based on a random draw and the
game demonstrated the distortion of demand as it moved
upstream through the supply chain (the bullwhip effect).
Jones et al. [85] modeled the supply chain of the North-
eastern US lumber industry using the SD approach to
answer policy questions on its economic and environ-
mental sustainability. Jones et al . [85] showed the capac-
ity of the lumber mills could potentially exceed the
available timber resources; however, feedback mecha-
nisms are required to ensure the sustainability of lumber
mill operations. It should be noted that SD models are
better suited for getting an aggregate views of the system
and policy questions at a strategic level. The modeled
system is evolved as a result of equations that link stock
and flow variables together and it is not always possible
to identify individual behaviour of people or firms.
3.3. Agent-Based Models
Agent-based modeling (ABM) makes use of individual
behaviour and characteristics to create a bottom-up sys-
tem, where each member optimizes its own operations in
the sense of an advanced planning system [86]. ABM
aims to investigate how the players within the supply
chain interact under changeable po licies and rules to cre-
ate a stable state for all supply chain members [86].
ABM has attracted a great deal of attention during recent
years for the purpose of decentralized planning. Each
member of the supply chain, who is autonomous or
semi-autonomous, is considered as an agent. Each agent
uses predefined characteristics, decision rules and object-
tives in order to interact with each other, and tries to
maximize its own utility, but does so in an environment
where all other agents are present [2]. The main advan-
tages of multi-agent systems are their ability to model
decentralised complex systems easily, offering increased
flexibility without losing efficiency, and providing
learning systems that improve over time with better deci-
sions [16].
ABM is being increasingly used for supply chain
management in a number of manufacturing industries for
production planning [52,87-94]. The flexibility of ABM
allows for the incorporation of uncertainty through a
combination of statistical analysis methods in the mod-
eling approach [95]. These statistical methods assume
that the uncertain variables follow a particular probability
distribution and repetitive sampling from these distribu-
tions generates a set of possible realizations or scenarios.
The deterministic discrete-event simulator is then run for
each of these scenarios, providing a set of output vari-
ables. The probability distribution of the performance
measure, constructed form the output variables, is used to
assess different supply chain configurations.
ABM has also been used in forest industries supply
chain modeling [2,3,12,37 ,65,82,8 7,96 ]. Each entity (mill,
wholesaler or retailer) is represented as an intelligent
agent that has a specific behaviour (o rder ing sch eme) and
also the option of co llaborating with oth er agents in deci-
sion making [17]. The results of these studies show that
the lowest cost of the supply chain was associated with
highest collaboration of agents. Collaboration and infor-
mation sharing is not only good for the whole supply
chain, but it is also better for each individual entity.
Lumber industry supply chains have been analyzed using
multi-behaviour agents [2], where the agents are either
reactive (have a predefined action for every possible state
of the environment) or deliberative (use past knowledge
about the environment to make decisions). Comparing
the performance of single-behaviour and ad aptive (multi-
behaviour) agents under different business environments,
Forget et al. [96] found that performance gains are possi-
ble if agents adjust their behaviour in every situation in-
stead of using a single strategy over the entire time hori-
zon. Because of their flexibility and being less compli-
cated compared to large centralized stochastic program-
ming models, agent-based models are helpful tools for
both strategic and operational planning under uncertainty
[52,91,97]. Table 2 provides a summary of studies on
forest product industries supp ly chain netwo rk simulation
models.
4. Integrated Simulation-Based Optimization
Models
Simulation models do not prescribe an optimal design fo r
the supply chain, which necessitate the use of optimiza-
tion models [95]. The optimization model translates all
interdependencies of the supply chain members into a
mathematical program to identify improvements that can
be made in a supply chain with regards to a certain per-
formance measure (an objective function such as total
profits or order fulfillment rate) [95]. Supply chain opti-
mization models prescribe a plan for production and dis-
tribution activities of supply chain members that is opti-
mal, meaning that no alternative plan can further improve
the value of the objective function [66,98]. In this cate-
gory of supply chain models, the optimization problem
(either deterministic or stochastic) is constructed based
on all the constraints and variables of the problem.
However, as the size of this optimization problem grows
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Supply Chain Network Optimization of the Canadian Forest Products Industry: A Critical Review
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Table 2. Summary of studies on forest product industrie s supply chain network simulation models.
Author Year-region Forest product application Simulation model approach
Forget et al. [2] 2008-North Amer ica Lumber industry supply chains Agent-based planning simulation platform
Moyaux et al. [37] 2004-Quebec, Canada Sawmill wood supply Simulation model to study the impact of global
supply chain behaviour
Lemieux et al. [65] 2009-Eastern Canada Lumber industry integrated planning and
scheduling system Multi-agent based simulation model
Randhawa et al. [73] 1994-Oregon, USA Sawmill design and analysis Discrete-event object- oriented simulation model
Lin et al. [75] 1995-General Log-to-dimension manufacturing systemFortran-based simulation model
Schwarzbauer and
Rametsteiner [81] 2001-Western Europe Forest products markets and certificationSystem dynamics simulation model
Jones et al. [85] 2002-Northeastern United
States Sawmill industry Dynamic simulation model
under uncertainty, finding an exact optimal solution be-
comes difficult and in many cases, approximation tech-
niques and heuristics are needed to find a near-optimal
solution.
Integrated simulation-based optimization models are
an attractive combined strategy to address optimization
under uncertainty [14]. It deals with the situation in
which the analyst would lik e to find which o f many po s-
sible sets of input parameters lead to optimal perform-
ance of the represented system. Most of today’s simula-
tors include possibilities to do a black-b ox parameter o p-
timization of the simulation model. Opt Quest is one
such optimization toolbox containing different meta-
heuristics algorithms designed to optimize configuration
decisions from different simulation runs [99], where the
simulation model is only used for the evaluation of the
objective value under different scenarios. Many of the
simulation-based optimization processes, being compli-
cated, need a considerable amount of technical expertise
on the part of the user, as well as a substantial amount of
computation time. This is closely related to the fact that
some of these techniques are local search strategies and
may be strongly problem dependent [95]. In the context
of simulation-based optimization models, the ability to
find high quality solutions early in the search is of criti-
cal importance, as evaluating the objective function en-
tails repeatedly running the simulation model [100].
Evolutionary algorithms have been commonly used for
this purpose to optimize multi-modal, discontinuous and
differential functions. The main advantage of evolution-
ary approaches over other meta-heuristics approaches is
that these are capable of exploring a larger area of the
solution space with a smaller number of objective func-
tion evaluations [95].
The genetic algorithms of simulation-based optimiza-
tion models in the supply chain networks are supported
through mathematical programming [101-103]. However,
these studies mostly deal with strategic decisions, for
instance combinatorial operation research problems such
as multi-stage facility location, rather than tactical or
operational ones. Although, there are a few studies that
used simulation-based optimization techniques in differ-
ent industries [16,52,91,92] there are none to our knowl-
edge that combine agent-based modeling with optimiza-
tion techniques and also deal with uncertainty in the for-
est products industry. A summary of studies on forest
product industries supply chain network integrated simu-
lation-based optimization models is presented in Table 3.
5. Conclusions
The purpose of this review paper was to comprehend-
sively review the literature related to supply chain mod-
els, and identify those models which would best address
the needs for the Canadian forest products industry. It
was found that the supp ly chain models us ed in the forest
products industry mostly address either the production
planning/scheduling or inventory management problems.
Such supply chain models have been used to optimize
isolated harvesting, transportation, and production, plan-
ning and distribution in the sawmill and pulp and paper
industries. Not only do these optimization models focus
on a few operations, but also capture uncertainty in mar-
ket demand and raw material supply, as well as the lack
in the two-way flows of information and materials. The
second class of supply chain models uses simulation ap-
proaches and deals with stochastic natures existin g in the
forest products industry’s supply chain. However, these
simulation models only capture the system dynamics of
large-scale systems in the supply chain network, and do
not provide any optimized solu tions.
Therefore, there is a need for an integrated simula-
tion-based optimization modeling approach for the Ca-

Supply Chain Network Optimization of the Canadian Forest Products Industry: A Critical Review 639
Table 3. Summary of studies on forest product industries supply chain network integrated simulation-based optimization
models.
Author Year-region Forest product application Integrated model approach
Frayret et al. [3] 2007-Quebec, Canada Lumber industry distr ibuted planning
and scheduling systems Combined agent-based technology with constraint programming
Todoroki and Ronnqvist [47] 2002-New Zealand Sawmills. Dynamic optimization model with log sawing
simulation system, AUTOSAW
Daugherty et al. [53] 2007-Central Oregon and
Northern California Bioenergy production Forest vegetation simulator and mixed-integer optimization model
Baesler et al. [76] 2004-Chile Sawmills Discrete event simulation model
Forget et al. 2009 [96] 2009-Quebec, Canada Lumber production planning platformAgent-based sim ulation model and mixed
integer programming model
nadian forest products industry supply chain network that
considers uncertainty in both demand and supply. This
integrated model wou ld act as a supply chain template in
order to further develop operational decision support
tools for inventory management and production plan-
ning/scheduling. The integrated model of Canadian forest
products industries will further help in evaluating col-
laborative planning, forecasting and replenishment, and
the demand variations in the industry’s supply chain net-
works.
6. Acknowledgements
The authors would lik e to thank the Natural Sciences and
Engineering Council of Canada (NSERC) Strategic Net-
work on Value Chain Optimization (VCO) for providing
the funding for this research.
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