In linear programming problems, as in most economic problems, the input data are often uncertain. So we haven't finished when we've obtained the optimal solution; we still need to ask, how would this ...

Start working toward program admission and requirements right away. Work you complete in the non-credit experience will transfer to the for-credit experience when you ...

A routine written in IML to solve this problem follows. The approach appends slack, surplus, and artificial variables to the model where needed. It then solves phase 1 to find a primal feasible ...

Roughly, we will cover the following topics (some of them may be skipped depending on the time available). Linear Programming: Basics, Simplex Algorithm, and Duality. Applications of Linear ...

Linear semi-infinite programming (LSIP) is a branch of optimisation that focuses on problems where a finite number of decision variables is subject to infinitely many linear constraints. This ...

Constraint Programming (CP) has been successful in a number of combinatorial search and discrete optimisation problems. Yet other more traditional approaches, such as Integer Programming (IP), can ...

Roth, A. E., U. G. Rothblum, and J. H. Vande Vate. "Stable Matchings, Optimal Assignments, and Linear Programming." Mathematics of Operations Research 18, no. 4 ...

George B. Dantzig, the mathematician who invented the field of linear programming, which revolutionized the way government and private enterprise planned, scheduled and generally conducted their ...

This paper presents a new method for the inclusion of nonlinear demand and supply relationships within a linear programming model. An existing method for this purpose is described first and its ...