Business Statistics
Problem 1: Optimization
Color Chips:
This was the first exercise on linear programming.
The target was to develop an objective function and find a solution for the problem the solution searched for was to maximize the profit of the factory fabricating the color chips.
The given were a set of variables A, N, P and a set of constraints which set the number of each chip to be produced. There were no limitations on the resources.
With the given data, the objective function was found to be MAX 3.15A + 2.65N + 3.85P.
After being introduced to the use of solver to solve a linear problem, the data was entered to the excel sheet and a data analysis tool called solver was used.
This exercise helped define the linear programming model, a basic introduction to the use of solver in excel and a simple problem of maximization. It also shed a light on the concepts of minimization/maximization depending on the target that the user is searching for. In this problem the target was to maximize the profit.
Computers S&P:
This exercise aimed to maximize the profit of a factory.
A typical example of production planning.
The variables used were S & P and the objective function was MAX 6S + 9P subject to some constraints.
The constraints restricted the resources as well as requirements of production.
This exercise helped understand the mixture of many constraints with a practical example on production planning. It helped understand more the linear programming model.
Make or Buy – Hamlet:
This exercise aimed to help the factory make a decision on what to produce and what to outsource in order to find the minimum requirements of the factory.
The target was to find the minimal amount of money required to produce a certain number of units.
The constraints included much more factors than of the previous exercises, it included cost, time of production and restriction on the resources.
The objective function was found to be MIN 3.15A1 + 33.75 A2 + …
The data was entered into excel and with the use of the solver tool the solution was found.
This exercise helped understand the minimization concept while introducing more constraints into the equation. The logical thinking moved from a straight forward problem, to a more complex real-life situation model.
Reallocating Bikes:
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