After getting the predicted importance score for each sentence in the given paper, we exploit the

integer linear programming method to generate well-structured slides by selecting and aligning key phrases and sentences.

Formulation of mixed

integer linear programming model

Shakeri and Logendran (2007) studied the problem of production scheduling model as a mixed

integer linear programming (binary) developed for scheduling tasks in multitasking environments, for which the number of completed tasks was not a good measure.

Consider the following fuzzy

integer linear programming (P), with trapezoidal fuzzy variables.

In this document, we proposed a mixed

integer linear programming model for the optimal assignation of the work shift applied to a real case of physiotherapists in the intensive and intermediate care area in a clinic.

3 O'Hara's algorithm as an

integer linear programming problem

These methods utilized biased sampling and

integer linear programming (ILP) in two phases for the tour scheduling of a PT workforce.

The

integer linear programming formulation of the model is shown in Appendix 1.

Sarper [14] modelled the two-machine flow shop scheduling problem as a mixed

integer linear programming formulation and suggested three constructive heuristics.

They present a methodology for achieving optimal deadlock prevention by converting a variety of problems under consideration into

integer linear programming models.

Samani and Mottaghi [26] used the

integer linear programming for obtaining the optimum pipe sizes and reservoir elevations in pipe networks.

They advance to multi-objective problems, including interactive multiple criteria decision-making approaches,

integer linear programming, networks, dynamic programming, modeling techniques, heuristics, nonlinear programming and discrete-event simulation, with a foray into inventory management.

Most significant, the new release delivers Mixed

Integer Linear Programming (MILP) - which is critically acclaimed in the optimization community.

Several methods such as mixed

integer linear programming [2], mixed integer nonlinear programming [3], dynamic programming [4] and methods involving simulated annealing [5], genetic algorithm (GA) [6], particle swarm optimization (PSO) [7], ant colony optimization [8], invasive weed optimization [9] and biogeography based optimization [10] have been suggested for solving DG placement problems.

Support for

integer linear programming, choice of solver methods, automatic plotting of objective function and constraints in the Optimization Assistant.