This paper presents a new decision-making
framework for resource
and production planning in the petrochemical industry under price
uncertainty conditions. The framework consists of three main decision
models: price prediction, paper trading, and production planning.
Three different prediction models, system dynamics, multiple linear
regression, and artificial neural network, were examined and compared
for the precise forecasting of the price of the final products as
well as naphtha as a raw material. We then developed a new optimization
model using a mixed integer linear programming technique to establish
the optimal operational strategies with a given inventory capacity.
In the framework, the maximum profitability is ensured by solving
a series of decision-making problems on the petrochemical plant, including
timing and quantity of naphtha purchase and final product sales, inventory
management, financial risk management with paper trading, and operation
strategy of the involved processes. To illustrate the capability of
the proposed model, the resource and production planning problem of
a real petrochemical plant that produces six different major products,
ethylene, propylene, butadiene, benzene, toluene, and xylene, was
examined. As a result, the optimal production planning using the proposed
model enabled the improvement of the total sales by 5.50% and the
operating profit by 13.8% in comparison with the BAU case.