Vinsamlegast notið þetta auðkenni þegar þið vitnið til verksins eða tengið í það: http://hdl.handle.net/1946/19390
Many industries have applied methods of engineering and optimization research to improve their production planning and to maximize profit.
There are two objectives with this research. The first objective is to adopt a new cost model to determine which premium price is accepted for billets production at ISAL, owned by Rio Tinto Alcan. The second objective is to maximize the profit of billet production by developing a model that returns the optimal production plan for a given planning period.
In the casthouse at ISAL, 200 different products are manufactured on a yearly basis and they differ in length, diameter and characteristic. A cost model tool existed already at ISAL where fixed cost and variable cost were gathered and total production cost was calculated. New mathematical cost model was developed, a multiple regression model with five dummy variables. The purpose was to determine a more accurate premium price decision model. Historical data where used for the production cost and additional cost on effort producing certain alloys where added with the use of dummy variables. The goal was to find a model, with high coefficient of determination between premium price and production cost and the objective was to reach above 80%. The results were successful and the method chosen showed that the model could be solved with multiple regression and a significantly statistically results were found.
The results from the cost model were an input to the second model where a linear optimization model is presented to support decision making of finding the optimal production plan for billet production, by maximizing the profit. Fifty different products are used in the model and data from ISAL are applied. Eight different scenarios are presented and their profit are analysed. The model indicates that a linear optimization model is a good approach to solve production planning problems. Sensitivity analysis is also applied on certain products and it can be seen what products are important to emphasize to maximize the profit.
Keywords: Cost model, linear optimization model, multiple regression model, aluminium, billets.
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