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Unformatted text preview: LP: must be limited resources + objective(maximize or minimize) + linearity + homogeneity + divisibility Graphin: formulate mathematical equations then plot constraint equations then determine the area of feasibility then plot the objective function then find the optimum point (extreme point that gives most profit) Feasible region= when everything < or equal then its the area below the lines Answer report: shows final answer and the amount produced ( NEVER FORGET NONNEGATIVITY ) Sensitivity report: 1)adjustable cells with objective function coefficients 2) OFC change: There is a range for each OFC where the current optimal corner point remains optimal. If the OFC changes beyond that range a new corner point becomes optimal RHS change: The constraint line shifts, which could change the feasible region / Slope of constraint line does not change / Corner point locations can change / The optimal solution can change Shadow price: the change is the objective function value per one-unit increase in the RHS of the constraint / Beyond some RHS range the value of each painting hour will change. While the RHS stays within this range, the shadow price does not change. If the change in the RHS value is within the allowable range, then: The shadow price does not change + The change in objective function value =(shadow price) x (RHS change) + If the RHS change goes beyond the allowable range, then the shadow price will change. Objective function= x*profit x + y* profit y If the change in OFC is within the allowable range, then: The optimal solution does not change + The new objective function value can be calculated. Binding constraint: When trying to find an optimal solution, the binding constraint is the factor that the solution is more dependent on. If you change it, the optimal solution will have to change. The non-binding constraint doesn't affect the optimal solution and can be changed without changing it. Forecasting: for planning, for how much to stock, and how much staff to hire / types of forecasting: qualitative(when no data), times series, causal rel forecasting Qualitative: intuition, market research, history/ time series: moving average, exponential smoothing / causual relationship: multiple regression model Forecast charac: usually wrong, the longer the horizon the less accurate, group forecasts more accurate than individual, a good forecast says about likely error...
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- Spring '07