This chapter addresses three special types of LP problems. The first, integer programming, examines LP problems that cannot have fractional answers. We also note that there are three types of integer programming problems: (1) pure or all-integer programs; (2) mixed problems, in which some solution variables need not be integers; and (3) 0–1 problems, in which all solutions are either 0 or 1. We also demonstrate how 0–1 variables can be used to model special situations such as fixed-charge problems. QM for Windows and Excel are used to illustrate computer approaches to these problems.

The next part of the chapter deals with goal programming. This extension of LP allows problems to have multiple goals. Again, software such as QM for Windows is a powerful tool in solving this offshoot of LP.

Finally, the advanced topic of NLP is introduced as a special mathematical programming problem. Excel is demonstrated as a useful tool in solving simple NLP models. However, it is important to remember that the solution found for an NLP problem might be a local optimum and not a global optimum.