Applications of mixed integer programming can be found in many industries, such as transportation, healthcare, energy, and finance, their economic impact is significant. It also well known that programs (MIPs) very difficult to solve. Their challenge continues stimulate research the design implementation efficient effective techniques better solve them. In this study, we introduce a novel power...

Dispatching firefighting resources effectively plays a vital role in wildfire management. To control the fire timely manner, should be dispatched an effective and reasonable way. Moreover, relationship between various resource-dispatching processes intuitive for firefighters to make decisions. In this paper, we propose novel event-response tree-based model dispatch different kinds of based on s...

We present Undercover, a primal heuristic for nonconvex mixed-integer nonlinear programming (MINLP) that explores a mixed-integer linear subproblem (sub-MIP) of a given MINLP. We solve a vertex covering problem to identify a minimal set of variables that need to be fixed in order to linearize each constraint, a so-called cover. Subsequently, these variables are fixed to values obtained from a r...

In this study we consider the shortest path problem, where arc costs are subject to distributional uncertainty. Basically, decision-maker attempts minimize her worst-case expected loss over an ambiguity set (or a family) of candidate distributions that consistent with decision-maker’s initial information. The is formed by all satisfy prescribed linear first-order moment constraints respect subs...

W consider both branch-and-cut and column-generation approaches for the problem of finding a minimumcost assignment of jobs with release dates and deadlines to unrelated parallel machines. Results are presented for several variants both with and without constraint programming. Among the variants, the most effective strategy is to combine a tight and compact, but approximate, mixed integer progr...

In developing a classification model for assigning observations of unknown class to one of a number of specified classes using the values of a set of features associated with each observation, it is often desirable to the classifier on a limited number of features. Mathematical programming (MP) discriminant analysis methods for developing classification models can be extended for feature select...

We propose how Genetic Programming (GP) can be used for developing, in real time, problem-specific heuristics for Branch and Bound (B&B) search. A GP run, embedded into the B&B process, exploits the characteristics of the particular problem being solved, evolving a problem-specific heuristic expression. The evolved heuristic replaces the default one for the rest of the B&B search. The applicati...

Numerical experiments for a parallel implementation of a branch-and-bound mixed 0y1 integer programming code are presented. Among its features, the code includes cuttingplane generation at the root node, and employs a new branching-variable selection rule within the search tree. The code runs on a loosely-coupled cluster of workstations using TreadMarks as the parallel software platform. Numeri...