Solve the MBI product-mix problem described in this chapter, using either Excel’s Solver or a student version of an LP solver, such as Lindo. Lindo is available from Lindo Systems

Solve the MBI product-mix problem described in this chapter, using either Excel’s Solver or a student version of an LP solver, such as Lindo. Lindo is available from Lindo Systems, Inc., at lindo.com; others are also available—search the Web. Examine the solution (output) reports for the answers and sensitivity report. Did you get the same results as reported in this chapter? Try the sensitivity analysis outlined in the chapter; that is, lower the right-hand side of the CC-8 marketing constraint by one unit, from 200 to 199. What happens to the solution when you solve this modified problem? Eliminate the CC-8 lower-bound constraint entirely (this can be done easily by either deleting it in Solver or setting the lower limit to zero) and re-solve the problem. What happens? Using the original formulation, try modifying the objective function coefficients and see what happens.

MBI Corporation, which manufactures special-purpose computers, needs to make a decision: How many computers should it produce next month at the Boston plant? MBI is considering two types of computers: the CC-7, which requires 300 days of labor and $10,000 in materials, and the CC-8, which requires 500 days of labor and $15,000 in materials. The profit contribution of each CC-7 is $8,000, whereas that of each CC-8 is $12,000. The plant has a capacity of 200,000 working days per month, and the material budget is $8 million per month. Marketing requires that at least 100 units of the CC-7 and at least 200 units of the CC-8 be produced each month. The problem is to maximize the company’s profits by determining how many units of the CC-7 and how many units of the CC-8 should be produced each month. Note that in a real-world environment, it could possibly take months to obtain the data in the problem statement, and while gathering the data the decision maker would no doubt uncover facts about how to structure the model to be solved. Web-based tools for gathering data can help.