Search results for " optimization."
showing 10 items of 2333 documents
The Multiple Multidimensional Knapsack with Family-Split Penalties
2021
Abstract The Multiple Multidimensional Knapsack Problem with Family-Split Penalties (MMdKFSP) is introduced as a new variant of both the more classical Multi-Knapsack and Multidimensional Knapsack Problems. It reckons with items categorized into families and where if an individual item is selected to maximize the profit, all the items of the same family must be selected as well. Items belonging to the same family can be assigned to different knapsacks; however, in this case, split penalties are incurred. This problem arises in resource management of distributed computing contexts and Service Oriented Architecture environments. An exact algorithm based on the exploitation of a specific combi…
A penalty-based finite element interface technology
2002
Abstract An effective and robust interface element technology able to connect independently modeled finite element subdomains is presented. This method has been developed using the penalty constraints and allows coupling of finite element models whose nodes do not coincide along their common interface. Additionally, the present formulation leads to a computational approach that is very efficient and completely compatible with existing commercial software. A significant effort has been directed toward identifying those model characteristics (element geometric properties, material properties and loads) that most strongly affect the required penalty parameter, and subsequently to developing si…
Inventory Control Under Parametric Uncertainty of Underlying Models
2013
A large number of problems in inventory control, production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty of underlying models. In the present paper we consider the case, where it is known that the underlying distribution belongs to a parametric family of distributions. The problem of determining an optimal decision rule in the absence of complete information about the underlying distribution, i.e., when we specify only the functional form of the distribution and leave some or all of its parameters unspecified, is seen to be a standard problem of statistical estimation. Unfortunately, the clas…
A New ESO-Based Method to Find the Optimal Topology of Structures Subject to Multiple Load Conditions
2014
In the field of topology optimization problems, the Evolutionary Structural Optimization (ESO) method is one of the most popular and easy to use. When dealing with problems of reasonable difficulty, the ESO method is able to give very good results in reduced times and with a limited request of computational resources. Generally, main applications of this method are addressed to the definition of the optimal topology of a component subjected to a single load condition. In this work, a new methodology, based on the ESO approach, is introduced for the study of the optimal topology of a component subjected to multiple load conditions. The new procedure, entirely developed in the APDL programmin…
Numerical model of macro-segregation during directional crystallization process
1998
Abstract In the paper the mathematical model of macro-segregation proceeding during the directional crystallization process is presented. The boundary-initial problem considered is discussed. Next the numerical approximation constructed on the basis of the boundary element method supplemented by a procedure called the artificial heat source method is described. The boundary condition on the solidification front resulting from the alloy component balance is introduced, while in finally the practical aspects of computations concerning the course of the process are discussed.
Resource allocation for OFDMA systems with multi-cell joint transmission
2012
This paper considers the downlink resource allocation of a coordinated multi-cell cluster in OFDMA systems with universal frequency reuse. Multi-cell joint transmission is considered via zero-forcing precoding. Furthermore, joint optimization of the user selection and power allocation across multiple subchannels and multiple cells is studied. The objective is to maximize the weighted sum rate under per-base-station power constraints. Based on general duality theory, two iterative resource allocation algorithms are proposed and compared with the optimal solution, which requires an exhaustive search of all possible combinations of users over all subchannels. Simulation results show that the t…
Data-Driven Pump Scheduling for Cost Minimization in Water Networks
2021
Pumps consume a significant amount of energy in a water distribution network (WDN). With the emergence of dynamic energy cost, the pump scheduling as per user demand is a computationally challenging task. Computing the decision variables of pump scheduling relies over mixed integer optimization (MIO) formulations. However, MIO formulations are NP-hard in general and solving such problems is inefficient in terms of computation time and memory. Moreover, the computational complexity of solving such MIO formulations increases exponentially with the size of the WDN. As an alternative, we propose a data-driven approach to estimate the decision variables of pump scheduling using deep neural netwo…
Combined K-Best sphere decoder based on the channel matrix condition number
2008
It is known that sphere decoding (SD) methods can provide maximum-likelihood (ML) detection over Gaussian MIMO channels with lower complexity than the exhaustive search. Channel matrix condition number represents an important influence on the performance of usual detectors. Throughout this paper, two particular cases of a SD method called K-Best carry out a combined detection in order to reduce the computational complexity with predictable performance degradation. Algorithm selection is based on channel matrix condition number thresholding. K-Best is a suboptimal SD algorithm for finding the ML solution of a detection problem. It is based on a fixed complexity tree search, set by a paramete…
Explicit Recursive and Adaptive Filtering in Reproducing Kernel Hilbert Spaces
2014
This brief presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces. Unlike previous approaches that exploit the kernel trick on filtered and then mapped samples, we explicitly define the model recursivity in the Hilbert space. For that, we exploit some properties of functional analysis and recursive computation of dot products without the need of preimaging or a training dataset. We illustrate the feasibility of the methodology in the particular case of the $\gamma$ -filter, which is an infinite impulse response filter with controlled stability and memory depth. Different algorithmic formulations emerge from the signal model. Experiments in chaotic and elect…
Iterative momentum relaxation for fast lattice-Boltzmann simulations
2001
Abstract Lattice-Boltzmann simulations are often used for studying steady-state hydrodynamics. In these simulations, however, the complete time evolution starting from some initial condition is redundantly computed due to the transient nature of the scheme. In this article we present a refinement of body-force driven lattice-Boltzmann simulations that may reduce the simulation time significantly. This new technique is based on an iterative adjustment of the local body-force. We validate this technique on three test cases, namely fluid flow around a spherical obstacle, flow in random fiber mats and flow in a static mixer reactor.