We consider the optimal control problem of emptying a deterministic single server multiclass queuing system without arrivals. We assume that the server is able to serve several queues simultaneously, each at its own rate, independent of the number of queues being served. We show that the optimal sequence ofmodes is ordered by the rate of cost decrease. However, queues are not necessarily emptie...

This paper presents an approximate multi-parametric Nonlinear Programming (mp-NLP) approach to explicit solution of feedback min-max NMPC problems for constrained nonlinear systems in the presence of bounded disturbances and/or parameter uncertainties. It is based on an orthogonal search tree structure of the state space partition and consists in constructing a piecewise nonlinear (PWNL) approx...

This paper applies an approximate multi-parametric Nonlinear Programming approach to explicitly solve output-feedback Nonlinear Model Predictive Control (NMPC) problems for constrained nonlinear systems described by black-box models. In particular, neural network models are used and the optimal regulation problem is considered. A dual-mode control strategy is employed in order to achieve an off...

Multi-objective verification problems of parametric Markov decision processes under optimality criteria can be naturally expressed as nonlinear programs. We observe that many of these computationally demanding problems belong to the subclass of signomial programs. This insight allows for a sequential optimization algorithm to efficiently compute sound but possibly suboptimal solutions. Each sta...

This paper presents an approximate multi-parametric nonlinear programming approach to explicit solution of constrained nonlinear model predictive control (MPC) problems in the presence of model uncertainty. The case of time-invariant parameter uncertainty is considered. The explicit MPC controller is based on an orthogonal search tree structure of the state space partition and is designed by so...

This paper presents an approximate multi-parametric quadratic programming approach to explicit solution of constrained linear MPC problems in the presence of bounded additive uncertainties, which is based on an orthogonal search tree structure of the state space partition. The explicit MPC controller minimizes the nominal value of the performance index and it is robust in the sense that all con...

one of important problems in supply chains management is supplier selection. in a company, there are massive data from various departments so that extracting knowledge from the company’s data is too complicated. many researchers have solved this problem by some methods like fuzzy set theory, goal programming, multi objective programming, the liner programming, mixed integer programming, analyti...

A nonconvex generalized semi-infinite programming problem is considered involving parametric max-functions in both, the objective and the constraints. For a fixed vector of parameters, the values of these parametric max-functions are given as optimal values of convex quadratic programming problems. Assuming that for each parameter the parametric quadratic problems satisfy the strong duality rel...

In this article, we introduce a novel framework for the design of multi set-point nonlinear explicit controllers process systems engineering problems where set-points are treated as uncertain parameters simultaneously with initial state dynamical system at each sampling instance. To end, an algorithm special class multi-parametric programming on right-hand side constraints and cost coefficients...