‎In this paper‎, ‎we propose a feasible interior-point method for‎ ‎convex quadratic programming over symmetric cones‎. ‎The proposed algorithm relaxes the‎ ‎accuracy requirements in the solution of the Newton equation system‎, ‎by using an inexact Newton direction‎. ‎Furthermore‎, ‎we obtain an‎ ‎acceptable level of error in the inexact algorithm on convex‎ ‎quadratic symmetric cone programmin...

Controlled tabular adjustment (CTA) is an emerging protection technique for tabular data protection. CTA formulates a mixed integer linear programming problem, which is tough for tables of moderate size. Finding a feasible initial solution may even be a challenging task for large instances. On the other hand, end users of tabular data protection techniques give priority to fast executions and a...

Positive and negative time-lags are general timing restrictions between the starting times of jobs which have been introduced in connection with the Metra Potential Method. Although very powerful, these relations have been considered only seldom in the literature since already for a single machine problem with positive and negative time-lags the problem of finding a feasible solution is NP-comp...

A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorial optimization. It is a multi-start or iterative process, in which each GRASP iteration consists of two phases, a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in the neighborhood of the constructed solution is sought. Since 1989, numerous p...

A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorial optimization. It is a multi-start or iterative process, in which each GRASP iteration consists of two phases, a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in the neighborhood of the constructed solution is sought. Since 1989, numerous p...

Branch-and-bound intelligently searches the set of feasible solutions to a combinatorial optimization problem: It, in effect, proves that the optimal solution is found without necessarily examining all feasible solutions. The feasible solutions are not given. They can be generated from the problem description. However, doing so usually is computationally infeasible: The number of feasible solut...

This paper presents a nonlinear mathematical programming model for a stochastic job shop scheduling problem. Due to the complexity of the proposed model, traditional algorithms have low capability in producing a feasible solution. Therefore, a hybrid method is proposed to obtain a near-optimal solution within a reasonable amount of time. This method uses a neural network approach to generate in...

Abstract The necessarily optimal solution is known as the most reasonable to linear programming problems with interval/fuzzy objective function coefficients. As it remains against certain fluctuations of coefficients, can be seen a robust solution. In this paper, we demonstrate that necessary optimality degree non-degenerate basic feasible obtained easily by utilizing tolerance approach. evalua...