The final exam will be essentially "cumulative".  It will cover all the material in the course except the graphical solution method for linear programs (the class of September 9) and discrete-event simulation (the class of December 7). 

Regarding the exam format:

• There will 4 or 5 questions, more likely 5
• At least two questions will be algebra formulations
• At least two questions will be spreadsheet-based
• At least one of the spreadsheet questions will be about simulation with multiple time periods.

The exam rules will be similar to the midterms, except that you are allowed two double-sided sheets of notes, still in your own handwriting only.  As before, you are allowed a calculator, although one should not be necessary. 

I suggest that you study the following material:

• All examples covered in class, except for September 9 and December7
• All homework assignments and their solutions posted on Sakai, except the graphical solution portion of homework 2
• Both midterm exams and the distributed solutions
• All final exam practice questions starting on page 297 of the coursepack
• All midterm practice questions on pages 239-294 of the coursepack, especially the midterm 2 practice questions that were skipped in the midterm 2 study suggestions.

The general format of the exam questions will be similar to the practice questions.

You will note that some of the final exam practice questions on linear and integer programming combine elements that we studied in different parts of the course.  For example, there might be a model with both blending (proportionality) constraints and binary variables.  The same thing could happen on some of the actual exam questions.

The following are topics that were not tested on either midterm, so you should consider as particularly likely (but not necessarily guaranteed) to be on the final:

• Project scheduling models
• Use of the central limit theorem to approximate the sum of many independent variables (remembering to use the square root when calculating the standard deviation of the sum of many independent random variables)
• Spreadsheet optimization models including integer variables.