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
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
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  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
Samani and Mottaghi  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
, mixed integer nonlinear programming , dynamic programming  and methods involving simulated annealing , genetic algorithm (GA) , particle swarm optimization (PSO) , ant colony optimization , invasive weed optimization  and biogeography based optimization  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.