A family of Hamilton type methods for congressional apportionments
A family of model predictive control algorithms with artificial neural networks
This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used...
A fast Lagrangian heuristic for large-scale capacitated lot-size problems with restricted cost structures
In this paper, we demonstrate the computational consequences of making a simple assumption on production cost structures in capacitated lot-size problems. Our results indicate that our cost assumption of increased productivity over time has dramatic effects on the problem sizes which are solvable. Our experiments indicate that problems with more than 1000 products in more than 1000 time periods may be solved within reasonable time. The Lagrangian decomposition algorithm we use does of course not...
A few remarks on vector optimization from the coalition-game theoretical point-of-view
A filled function approach for nonsmooth constrained global optimization.
A fine-grained arc-consistency algorithm for non-normalized constraint satisfaction problems
Constraint programming is a powerful software technology for solving numerous real-life problems. Many of these problems can be modeled as Constraint Satisfaction Problems (CSPs) and solved using constraint programming techniques. However, solving a CSP is NP-complete so filtering techniques to reduce the search space are still necessary. Arcconsistency algorithms are widely used to prune the search space. The concept of arc-consistency is bidirectional, i.e., it must be ensured in both directions...
A finite-buffer queue with a single vacation policy: an analytical study with evolutionary positioning
In this paper, application of an evolutionary strategy to positioning a GI/M/1/N-type finite-buffer queueing system with exhaustive service and a single vacation policy is presented. The examined object is modeled by a conditional joint transform of the first busy period, the first idle time and the number of packets completely served during the first busy period. A mathematical model is defined recursively by means of input distributions. In the paper, an analytical study and numerical experiments...
A formulation of combinatorial auction via reverse convex programming.
A fuzzy logic approach to assembly line balancing.
This paper deals with the use of fuzzy set theory as a viable alternative method for modelling and solving the stochastic assembly line balancing problem. Variability and uncertainty in the assembly line balancing problem has traditionally been modelled through the use of statistical distributions. This may not be feasible in cases where no historical data exists. Fuzzy set theory allows for the consideration of the ambiguity involved in assigning processing and cycle times and the uncertainty contained...
A game model referring to the control of independent discrete time stochastic processes
A general approach to dual characterizations of solvability of inequality systems with applications.
A General Convergence Theorem for the Reduced Gradient Method.
A general Hamilton-Jacobi framework for non-linear state-constrained control problems
The paper deals with deterministic optimal control problems with state constraints and non-linear dynamics. It is known for such problems that the value function is in general discontinuous and its characterization by means of a Hamilton-Jacobi equation requires some controllability assumptions involving the dynamics and the set of state constraints. Here, we first adopt the viability point of view and look at the value function as its epigraph. Then, we prove that this epigraph can always be described...
A general maximum principle for optimization problems
A generalization of Bellman's functional equation.
A generalization of dynamic programming for Pareto optimization in dynamic networks
A Generalization of Dynamic Programming for Pareto Optimization in Dynamic Networks
The Algorithm in this paper is designed to find the shortest path in a network given time-dependent cost functions. It has the following features: it is recursive; it takes place bath in a backward dynamic programming phase and in a forward evaluation phase; it does not need a time-grid such as in Cook and Halsey and Kostreva and Wiecek's "Algorithm One”; it requires only boundedness (above and below) of the cost functions; it reduces to backward multi-objective dynamic programming if there are...
A generalization of the convex-hull-and-line traveling salesman problem.
A Generalization of the Potential Method for Conditional Maxima on the Banach, Reflexive Spaces.
A generalization of the quasiconvex optimization problem.