Interval linear programming extends classical linear optimisation by allowing coefficients in the objective function and constraints to be specified as intervals rather than fixed values. This ...

Motivation for Research: Large combinatorial optimization problems involve an exponentially growing decision space, where finding a good solution often becomes extremely difficult using traditional ...

Rui Zhang, Saied Samiedaluie, and Dan Zhang. 2022. Product-based Approximate Linear Programs for Network Revenue Management. Operations Research, 70(5):2837-2850. The approximate linear programming ...

A first introduction to probability and statistics. This course will provide background to understand and produce rigorous statistical analysis including estimation, confidence intervals, hypothesis ...

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 ...

Survey of operations research techniques. Linear programming, decision theory, stochastic processes, game theory. This course is not open to Industrial Engineering majors. May not be taken ...

Two medical applications of linear programming are described in this paper. Specifically, linear programming-based machine learning techniques are used to increase the accuracy and objectivity of ...

Combinatorial optimization and integer programming form the backbone of decision-making models in which discrete choices must be made under constraints. At its core, integer programming specialises in ...