Explore a realistic approach to linear programming with random coefficients and risk analysis. Discover how to design a copula of objective function coefficients for business analytics and risk ...

The traditional linear programming model is deterministic. The way that uncertainty is handled is to compute the range of optimality. After the optimal solution is obtained, typically by the simplex ...

Bilevel linear programming (BLP) is a solution method for linear optimization problem with two sequential decision steps of the leader and the follower. In this paper, we assume that the follower's ...

Concave objective functions which are both piecewise linear and separable are often encountered in a wide variety of management science problems. Provided the constraints are linear, problems of this ...

Existing supervised learning models are generally built upon the basis of only one single objective function, through the minimizing of the square-loss (neural networks) or the minimizing of the ...

We formulate a risk-averse two-stage stochastic linear programming problem in which unresolved uncertainty remains after the second stage. The objective function is formulated as a composition of ...

Example 8.9: Linear Programming The two-phase method for linear programming can be used to solve the problem A routine written in IML to solve this problem follows. The approach appends slack, surplus ...