Operation Research

Operation Research

Operation research (OR) deals with problem-solving techniques for decision making. Operation research problems has following elements:
1. Decision Variables: Optimal value of the variables to be determined
2. Objective Function: Examples could be maximization of profit or revenue, minimization of cost.
3. Constraints: Imposed limits on variables due to other factors

Popular OR problems are as given below:
1. Linear Programming Problems
2. Integer Programming Problems
3. Mixed Integer Programming Problems
4. Non-Linear Programming Problems
5. Dynamic Programming Problems

Liner Programming

Linear Programming technique is a mathematical modeling to determining a way to achieve the best outcome for given set of decision variables, constraints and objective function.

Attached file gives an example of linear programming solution using Microsoft Excel solver.

Sensitivity Analysis

As in many scenario, the coefficients of objective function are not certain, and so sensitivity analysis is used to analyze the optimum solution to see how sensitive this solution is with respect to unit change in the coefficients. Sensitivity analysis is used to determine how "sensitive" a model is to changes in the value of the parameters of the model and to changes in the structure of the model. This technique is used to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions.

Typical Sensitivity Analyses:
1. Coefficient allowable range for objective function to stay optimal
2. Reducing cost sensitivity: Sensitivity analysis can help in figuring out the product prices to have the optimal product mix in the market. This may not be maximum profit product mix but it is optimal ensuring the mix in all.
3. Shadow price analysis: The shadow price is the instantaneous change per unit of the constraint in the objective value of the optimal solution of an optimization problem obtained by relaxing the constraint.

MCQ
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