Lab Session 3 - ERGEE PDF

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Summary

Lab Session 3ObjectiveTo obtain graphical solution of given Minimization type Linear Programming Problems using TORA (software).Theory:The menu-driven TORA graphical LP module should prove helpful in reinforcing your understanding of how the LP constraints are graphed. Maximization problem should lo...

Description

Lab Session 3 Objective To obtain graphical solution of given Minimization type Linear Programming Problems using TORA (software).

Theory: The menu-driven TORA graphical LP module should prove helpful in reinforcing your understanding of how the LP constraints are graphed. Maximization problem should look like this.

Minimize z = 0.3x1 + 0.9x2 Constraints: x1 + x2 >= 800 0.21x1 – 0.3x2 = 0 x1, x2 >= 0 Procedure: A step wise procedure in TORA is given below 1. Run TORA in your operating system. 2. At main menu, select Linear Programming.

3. After clicking linear programming from main menu, new window will appear as shown below. Option is available to create new file or use an existing file. After selecting input mode and setting input format, click on “go to input screen”.

4. Input screen looks like as shown below. Type problem title, enter number of variables, number of constraints and press enter button.

5. Type the names of variables like x1, x2 etc. Enter the values of the coefficients in the objective function and constraints. The sign of inequality is altered using keyboard. Don’t change the values of lower and upper bound.

6. After that click on Solve menu, following dialogue box will appear. Save the file for future use.

7. New window will appear after saving the file having options of solve problem (graphical + algebraic), view/modify input data, go to main menu or exit TORA. Click on graphical options to find the graphical solution of LPP.

8. Output window will appear as shown below. Still there is an option available to view/modify input data. Click on “Go to Output Screen”.

9. Window having graphical area will appear. To graph LP below, click constraints one at a time, then click objective function.

10. Optimum solution is achieved. Objective function maximizes with a value of 437.65 at x1=470.59 & x2=329.41. The dotted area is feasible area under the given constraints....