(240i) Strategic Investment Planning in the Pulp and Paper Industry Using Mixed Integer Linear Programming

   

 Abstract 


The Pulp and Paper industry in North Europe is
continuously making strategic decisions for investments. A modern paper mill is
a significant long-term investment, and companies are searching for methods for
making good investment decisions. In this paper, a method based in mixed
integer linear programming (MILP) for decision support in the Pulp and Paper
Industry is proposed. It is shown how an MILP formulation can be used for
optimising revenues on investments based on forecasts on demand, raw material
costs, transportation costs, labour costs and energy costs. In addition, a case
study illustrating how the methodology works is presented.


 

 Background 

One major decision for any
industrial activity is where to locate the production units. In the pulp and
paper industry, a production unit often is an integrated pulp mill and a paper
mill. Moreover, even paper-converting units or saw-mill activities may be run
on the same site. Large-scale pulp and paper mills are major investments, and
the life time for these facilities is long. The companies are hence looking for
reliable decision support mechanisms for their strategic planning. 

The main objective of any company
is to produce profits and return on investments (ROI). Using computational
tools, the optimal profit and ROI can be obtained for varying scenarios. The
profit of a pulp and paper mill is dependent on various factors, such as raw
material costs, labour costs, transportation costs, energy costs, demands,
paper prices, interest rates etc. Some of these factors vary regionally more,
some less. For instance, the labour costs have a strong relation to the local
economy, whereas interest rates do not. Hence, the investment decision is
actually a geographical decision, the big questions being where to invest, when
and how much.  


 

 Related work 

Even if the pulp and paper industry is a large industry, and
only in the Nordic European countries Norway, Sweden and Finland the turnover
is US$ 50 billion, and investments are around US$ 400-500 million for one pulp
line (Bergman et.al, 2002), there is not very much literature on the area. On a
general level  Heidenberger(1996) apply
MILP to project selection problem, which can be seen as a investment problem,
and show similar problem statements.  In
process synthesis there is a long trend in applying mathematical optimisation,
an overview given by Grossmann (1996). In the case of supply chain
optimisation, there are numerous examples of applying mathematical
optimisation. Applying optimisation to varying scheduling problems the chemical
industry has been rather popular (Floudas and Lin, 2004), and the methodology
is often reusable for other industries. The development of more sophisticated
decision support systems is a general trend shown by Shim et. al (2002). Here
has also mathematical  programming
approaches been used, and the proposed formulation here is a addition to this
work.

 

 Problem formulation 

In this paper we present a MILP
model for optimising the profits and return on investments based on given forecasts
for production costs, paper prices and demands. Even if most of the formulation
is LP, the actual investment decisions are discrete, and thus an MILP
formulation is used. 


 

 Illustrative example 

In the case study presented in
this paper, the task is to supply five geographical areas in Europe with
different paper qualities. Each area has a forecasted demand for five different
paper qualities. The market is supposed to increase according to a growth plan.
Costs for raw materials, labour, and logistics are given as forecasts. Using
the optimisation methods proposed in the paper, optimal solutions to support
the strategic planning are obtained. A solution provides information on where
to produce each paper qualities, where the investments should be done, and how
the logistic should be arranged. 


 

 Results and conclusion 

An MILP-formulation for
optimising investments in pulp and paper mills was presented. Using this
formulation and forecasts for costs, demands and prices, by optimising for
profit or return on investment, strategic investment plans can be generated.
This work is very

An obvious shortcoming of the
current formulation is however that forecasts, demands, and prices etc. are
uncertain. Hence, the formulations could be developed to also handle
optimisation under uncertainty. This could further help the decision makers,
not selecting strategic plans that easily fail due to high sensitivity to
uncertainty.



Many industries depend on the same key variables for the
profitability. The proposed MILP formulations can be easily tuned to meet the
requirements of other businesses than just the pulp and paper industry. 

   

 Literature 



Mats A. Bergman, Per Johansson and M.A. Bergman, Large investments in the pulp and paper industry: a count data regression analysis, Journal of Forest Economics, Volume 8, Issue 1, 2002, Pages 29-52.

Christodoulos A. Floudas and Xiaoxia Lin, Continuous-time versus
discrete-time approaches for scheduling of chemical processes: a review,
Computers & Chemical Engineering, Volume 28, Issue 11, 15 October 2004,
Pages 2109-2129.

Ignacio E. Grossmann and Mark M. Daichendt, New trends in
optimization-based approaches to process synthesis, Computers & Chemical
Engineering, Volume 20, Issues 6-7, June-July 1996, Pages 665-683.

Kurt Heidenberger, Dynamic project selection and funding under risk: A
decision tree based MILP approach, European Journal of Operational Research,
Volume 95, Issue 2, 6 December 1996, Pages 284-298.

J. P. Shim, Merrill Warkentin, James F. Courtney, Daniel J. Power,
Ramesh Sharda and Christer Carlsson, Past, present, and future of decision
support technology, Decision Support Systems, Volume 33, Issue 2, June 2002,
Pages 111-126.