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**Harvard**

Forsberg, P. (2006) *Optimisation of Long-Term Industrial Planning*. Göteborg : Chalmers University of Technology (Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie, nr: 2544).

** BibTeX **

@book{

Forsberg2006,

author={Forsberg, Peter},

title={Optimisation of Long-Term Industrial Planning},

isbn={91-7291-863-2},

abstract={In this thesis, long-term optimisation methods for industrial transition processes have been
developed, taking monetary and environmental considerations into account. Two different methods for
investment optimisation have been developed. First, an optimisation method comprising simultaneous
calculation of the long-term investment strategy and the short-term utilisation scheme for a
deterministic demand was developed. The method has been applied to the case of finding an investment
strategy for minimising the production cost for a single hydrogen refuelling station. The problem
was shown to be convex; thus the resulting solution is the global optimum. Second, an investment
optimisation method using stochastic demand scenarios and multi-objective optimal control to produce
the Pareto front of the two conflicting objectives \emph{expected production cost} and
\emph{expected unsatisfied demand} was developed. This method was applied to the case of finding the
optimal investment strategy for a combined hydrogen and hythane refuelling station. Depending on the
preferences of the decision-maker, many different feasible solutions can be found. However, it was
also found that, due to the uncertainty of the stochastic demand function, satisfying all the
estimated demands would require a production capacity well above the mean demand, which would be
very costly to maintain.
In addition to the two methods for investment optimisation, a modelling approach for systems
combining economic and environmental aspects
has been developed as well. This approach has been used for modelling cement production facilities,
taking both economic and environmental issues into consideration.
In order to deal with prediction uncertainties, time series prediction using genetic algorithms was
investigated as well. Discrete-time prediction networks, a novel type of recurrent neural networks,
were introduced, and were shown to provide one-step macro-economic time series
prediction with greater accuracy than several other methods.},

publisher={Institutionen för tillämpad mekanik, Fordonssäkerhet, Chalmers tekniska högskola,},

place={Göteborg},

year={2006},

series={Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie, no: 2544},

keywords={Transition strategy optimisation, Investment strategies, Multi-objective decision making, Optimisation under uncertainty},

}

** RefWorks **

RT Dissertation/Thesis

SR Print

ID 23157

A1 Forsberg, Peter

T1 Optimisation of Long-Term Industrial Planning

YR 2006

SN 91-7291-863-2

AB In this thesis, long-term optimisation methods for industrial transition processes have been
developed, taking monetary and environmental considerations into account. Two different methods for
investment optimisation have been developed. First, an optimisation method comprising simultaneous
calculation of the long-term investment strategy and the short-term utilisation scheme for a
deterministic demand was developed. The method has been applied to the case of finding an investment
strategy for minimising the production cost for a single hydrogen refuelling station. The problem
was shown to be convex; thus the resulting solution is the global optimum. Second, an investment
optimisation method using stochastic demand scenarios and multi-objective optimal control to produce
the Pareto front of the two conflicting objectives \emph{expected production cost} and
\emph{expected unsatisfied demand} was developed. This method was applied to the case of finding the
optimal investment strategy for a combined hydrogen and hythane refuelling station. Depending on the
preferences of the decision-maker, many different feasible solutions can be found. However, it was
also found that, due to the uncertainty of the stochastic demand function, satisfying all the
estimated demands would require a production capacity well above the mean demand, which would be
very costly to maintain.
In addition to the two methods for investment optimisation, a modelling approach for systems
combining economic and environmental aspects
has been developed as well. This approach has been used for modelling cement production facilities,
taking both economic and environmental issues into consideration.
In order to deal with prediction uncertainties, time series prediction using genetic algorithms was
investigated as well. Discrete-time prediction networks, a novel type of recurrent neural networks,
were introduced, and were shown to provide one-step macro-economic time series
prediction with greater accuracy than several other methods.

PB Institutionen för tillämpad mekanik, Fordonssäkerhet, Chalmers tekniska högskola,

T3 Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie, no: 2544

LA eng

OL 30