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Mixed integer convex programming method is applied in order to achieve the optimal operation.
This reformulation converts the original mixed integer nonlinear program (MINLP) with dimensions dependent upon the value of the integer decision variables into a mixed integer convex program (MICP) with fixed matrix dimensions.
To solve the model, first we present an exact solution method by casting the problem as a mixed integer convex program, and then we establish a heuristic method based on a hybridization of Tabu Search and Simulated Annealing.
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In this code, instead of solving continuous quadratic programs, the solution is approximated by a series of mixed-integer convex quadratic programming problems.
The resulting model is a non-convex mixed integer non-linear programming (MINLP) model.
Rigorous mathematical modeling leads to a large, non-convex, mixed integer nonlinear problem (MINLP).
FACTS allocation problem represents a formidable, mixed integer, nonlinear and non-convex optimisation problem in electrical power engineering.
The model is a bilinear non-convex mixed integer programming and is reduced to a pure linear mixed integer programming through proper linearization.
This paper presents a new method for handling removable discontinuities in non-convex mixed integer non-linear programming (MINLP) models for chemical process synthesis and design.
The simultaneous optimisation model of the proposed superstructure is formulated as a non-convex mixed integer non-linear programming (MINLP) problem for minimising the total annual network cost (TAC).
As the problem turns out to be a non-convex mixed integer programming problem, known to be NP-hard, we relax it first into a continuous problem, then transform the resultant into a convex problem and finally propose a distributed algorithm to solve it.
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