OIDD 101 Midterm: OPIM Midterm Cheat Sheet

• School: UPenn
• Department: Operations, Information and Decisions
• Course: OIDD 101
• Professor: Lorin Hitt

Document Summary

Objective here is to maximize a weighted sum of the xs. Each of the j constraints requires that a weighted sum of the xs is less than a constant. P"s, r"s, and c"s are given data, solve for x"s. Optimal solutions are at the intersection of 2 constraints. Slide the isoprofit/contribution line up until you can"t anymore. Shadow price: how much the objective function changes after a change to a constant (within an allowable range) Reduced price: how much you can change the price w/out changing the solution (when a dv = 0) Define binary variables by x (x1, x2, ) Or: if x1 or x2 then y. That is, y=1 only if x1=1 or x2=1. That is, y=1 only if x1=1 and x2=1. If x1=0 then y=1, if x1=1 then y=0. Nlp: objective/constraints can be nonlinear functions of continuous decision variables. The covariance or returns with other securities.