There is a considerable amount of research literature available for the optimisation of
supply chain management of the chemical process industry. The context of supply
chain considered in this thesis is the supply chain inside the chemical complex which
is the conversion of raw materials into intermediate chemicals and finished chemical
products through different chemical processes. Much of the research in the area of
planning and scheduling for the process sector has been focused on optimising an
individual chemical process within a larger network of a chemicals complex.
The objective of this thesis is to develop a multi-objective, multi-period stochastic
capacity planning model as a quantitative tool in determining an optimum investment
strategy while considering sustainability for an integrated multi-process chemicals
complex under future demand uncertainty using the development of inorganic
chemicals complex at Bamnet Narong, Thailand as the main case study.
Within this thesis, a number of discrete models were developed in phases towards the
completion of the final multi-objective optimisation model. The models were
formulated as mixed-integer linear programming (MILP) models.
The first phase was the development of a multi-period capacity planning optimisation
model with a deterministic demand. The model was able to provide an optimal
capacity planning strategy for the chemicals complex at Bamnet Narong, Thailand.
The numerical results show that based on the model assumptions, all the proposed
chemical process plants to be developed in the chemicals complex are financially
viable when the planning horizon is more than 8 years. The second phase was to build a multi-period stochastic capacity planning
optimisation model under demand uncertainty. A three-stage stochastic programming
approach was incorporated into the deterministic model developed in the first phase to
capture the uncertainty in demand of different chemical products throughout the
planning horizon. The expected net present value (eNPV) was used as the
performance measure. The results show that the model is highly demand driven.
The third phase was to provide an alternative demand forecasting method for capacity
planning problem under demand uncertainty. In the real-world, the annual increases in
demand will not be constant. A statistical analysis method named “Bootstrapping”
was used as a demand generator for the optimisation model. The method uses
historical data to create values for the future demands. Numerical results show that the
bootstrap demand forecasting method provides a more optimistic solution.
The fourth phase was to incorporate financial risk analysis as constraints to the
previously developed multi-period three-stage stochastic capacity planning
optimisation model. The risks associated with the different demand forecasting
methods were analysed. The financial risk measures considered in this phase were the
expected downside risk (EDR) and the mean absolute deviation (MAD). Furthermore,
as the eNPV has been used as the usual financial performance measure, a decisionmaking
method, named “Minimax Regret” was applied as part of the objective
function to provide an alternative performance measure to the developed models.
Minimax Regret is one kind of decision-making theory, which involves minimisation
of the difference between the perfect information case and the robust case. The results
show that the capacity planning strategies for both cases are identical
Finally, the last phase was the development of a multi-objective, multi-period three
stage stochastic capacity planning model aiming towards sustainability. Multiobjective
optimisation allows the investment criteria to be traded off against an
environmental impact measure. The model values the environmental factor as one of
the objectives for the optimisation instead of this only being a regulatory constraint.
The expected carbon dioxide emissions was used as the environmental impact
indicator. Both direct and indirect emissions of each chemical process in the chemicals complex were considered. From the results, the decision-makers will be
able to decide the most appropriate strategy for the capacity planning of the chemicals
complex.