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- Computerunterstütztes Verfahren (289) (remove)
Die Liquiditätsplanung von Bauunternehmen XE "Liquiditätsplanung" gilt als ein wesentliches Steuerungs-, Kontroll- sowie Informationsinstrument für interne und externe Adressaten und übt eine Entscheidungsunterstützungsfunktion aus. Da die einzelnen Bauprojekte einen wesentlichen Anteil an den Gesamtkosten des Unternehmens ausmachen, besitzen diese auch einen erheblichen Einfluß auf die Liquidität und die Zahlungsfähigkeit der Bauunternehmung. Dem folgend ist es in der Baupraxis eine übliche Verfahrensweise, die Liquiditätsplanung zuerst projektbezogen zu erstellen und anschließend auf Unternehmensebene zu verdichten. Ziel der Ausführungen ist es, die Zusammenhänge von Arbeitskalkulation XE "Arbeitskalkulation" , Ergebnisrechnung XE "Ergebnisrechnung" und Finanzrechnung XE "Finanzrechnung" in Form eines deterministischen XE "Erklärungsmodells" Planungsmodells auf Projektebene darzustellen. Hierbei soll das Verständnis und die Bedeutung der Verknüpfungen zwischen dem technisch-orientierten Bauablauf und dessen Darstellung im Rechnungs- und Finanzwesen herausgestellt werden. Die Vorgänge aus der Bauabwicklung, das heißt die Abarbeitung der Bauleistungsverzeichnispositionen und deren zeitliche Darstellung in einem Bauzeitenplan sind periodisiert in Größen der Betriebsbuchhaltung (Leistung, Kosten) zu transformieren und anschließend in der Finanzrechnung (Einzahlungen., Auszahlungen) nach Kreditoren und Debitoren aufzuschlüsseln.
We consider efficient numerical methods for the solution of partial differential equations with stochastic coefficients or right hand side. The discretization is performed by the stochastic finite element method (SFEM). Separation of spatial and stochastic variables in the random input data is achieved via a Karhunen-Loève expansion or Wiener's polynomial chaos expansion. We discuss solution strategies for the Galerkin system that take advantage of the special structure of the system matrix. For stochastic coefficients linear in a set of independent random variables we employ Krylov subspace recycling techniques after having decoupled the large SFEM stiffness matrix.
Available construction time-cost trade-off analysis models can be used to generate trade-offs between these two important objectives, however, their application is limited in large-scale construction projects due to their impractical computational requirements. This paper presents the development of a scalable and multi-objective genetic algorithm that provides the capability of simultaneously optimizing construction time and cost large-scale construction projects. The genetic algorithm was implemented in a distributed computing environment that utilizes a recent standard for parallel and distributed programming called the message passing interface (MPI). The performance of the model is evaluated using a set of measures of performance and the results demonstrate the capability of the present model in significantly reducing the computational time required to optimize large-scale construction projects.
In many branches companies often lose the visibility of their human and technical resources of their field service. On the one hand the people in the fieldservice are often free like kings on the other hand they do not take part of the daily communication in the central office and suffer under the lacking involvement in the decisions inside the central office. The result is inefficiency. Reproaches in both directions follow. With the radio systems and then mobile phones the ditch began to dry up. But the solutions are far from being productive.
We study the Weinstein equation u on the upper half space R3+. The Weinstein equation is connected to the axially symmetric potentials. We compute solutions of the Weinstein equation depending on the hyperbolic distance and x2. These results imply the explicit mean value properties. We also compute the fundamental solution. The main tools are the hyperbolic metric and its invariance properties.
HYPERMONOGENIC POLYNOMIALS
(2006)
It is well know that the power function is not monogenic. There are basically two ways to include the power function into the set of solutions: The hypermonogenic functions or holomorphic Cliffordian functions. L. Pernas has found out the dimension of the space of homogenous holomorphic Cliffordian polynomials of degree m, but his approach did not include a basis. It is known that the hypermonogenic functions are included in the space of holomorphic Cliffordian functions. As our main result we show that we can construct a basis for the right module of homogeneous holomorphic Cliffordian polynomials of degree m using hypermonogenic polynomials and their derivatives. To that end we first recall the function spaces of monogenic, hypermonogenic and holomorphic Cliffordian functions and give the results needed in the proof of our main theorem. We list some basic polynomials and their properties for the various function spaces. In particular, we consider recursive formulas, rules of differentiation and properties of linear independency for the polynomials.
Traffic simulation is a valuable tool for the design and evaluation of road networks. Over the years, the level of detail to which urban and freeway traffic can be simulated has increased steadily, shifting from a merely qualitative macroscopic perspective to a very detailed microscopic view, where the behavior of individual vehicles is emulated realistically. With the improvement of behavioral models, however, the computational complexity has also steadily increased, as more and more aspects of real-life traffic have to be considered by the simulation environment. Despite the constant increase in computing power of modern personal computers, microscopic simulation stays computationally expensive, limiting the maximum network size than can be simulated on a single-processor computer in reasonable time. Parallelization can distribute the computing load from a single computer system to a cluster of several computing nodes. To this end, the exisiting simulation framework had to be adapted to allow for a distributed approach. As the simulation is ultimately targeted to be executed in real-time, incorporating real traffic data, only a spatial partition of the simulation was considered, meaning the road network has to be partitioned into subnets of comparable complexity, to ensure a homogenous load balancing. The partition process must also ensure, that the division between subnets does only occur in regions, where no strong interaction between the separated road segments occurs (i.e. not in the direct vicinity of junctions). In this paper, we describe a new microscopic reasoning voting strategy, and discuss in how far the increasing computational costs of these more complex behaviors lend themselves to a parallelized approach. We show the parallel architecture employed, the communication between computing units using MPIJava, and the benefits and pitfalls of adapting a single computer application to be used on a multi-node computing cluster.
The use of process models in the analysis, optimization and simulation of processes has proven to be extremely beneficial in the instances where they could be applied appropriately. However, the Architecture/Engineering/Construction (AEC) industries present unique challenges that complicate the modeling of their processes. A simple Engineering process model, based on the specification of Tasks, Datasets, Persons and Tools, and certain relations between them, have been developed, and its advantages over conventional techniques have been illustrated. Graph theory is used as the mathematical foundation mapping Tasks, Datasets, Persons and Tools to vertices and the relations between them to edges forming a directed graph. The acceptance of process modeling in AEC industries not only depends on the results it can provide, but the ease at which these results can be attained. Specifying a complex AEC process model is a dynamic exercise that is characterized by many modifications over the process model's lifespan. This article looks at reducing specification complexity, reducing the probability for erroneous input and allowing consistent model modification. Furthermore, the problem of resource leveling is discussed. Engineering projects are often executed with limited resources and determining the impact of such restrictions on the sequence of Tasks is important. Resource Leveling concerns itself with these restrictions caused by limited resources. This article looks at using Task shifting strategies to find a near-optimal sequence of Tasks that guarantees consistent Dataset evolution while resolving resource restrictions.
In this paper we consider three different methods for generating monogenic functions. The first one is related to Fueter's well known approach to the generation of monogenic quaternion-valued functions by means of holomorphic functions, the second one is based on the solution of hypercomplex differential equations and finally the third one is a direct series approach, based on the use of special homogeneous polynomials. We illustrate the theory by generating three different exponential functions and discuss some of their properties. Formula que se usa em preprints e artigos da nossa UI&D (acho demasiado completo): Partially supported by the R\&D unit \emph{Matem\'atica a Aplica\c\~es} (UIMA) of the University of Aveiro, through the Portuguese Foundation for Science and Technology (FCT), co-financed by the European Community fund FEDER.
We establish the basis of a discrete function theory starting with a Fischer decomposition for difference Dirac operators. Discrete versions of homogeneous polynomials, Euler and Gamma operators are obtained. As a consequence we obtain a Fischer decomposition for the discrete Laplacian. For the sake of simplicity we consider in the first part only Dirac operators which contain only forward or backward finite differences. Of course, these Dirac operators do not factorize the classic discrete Laplacian. Therefore, we will consider a different definition of a difference Dirac operator in the quaternionic case which do factorizes the discrete Laplacian.