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The Discrete Ordered Median Problem (DOMP) generalizes classical discrete location problems, such as the N-median, N-center and Uncapacitated Facility Location problems. It was introduced by Nickel [16], who formulated it as both a nonlinear and a linear integer program. We propose an alternative integer linear programming formulation for the DOMP, discuss relationships between both integer linear programming formulations, and show how properties of optimal solutions can be used to strengthen these formulations. Moreover, we present a specific branch and bound procedure to solve the DOMP more efficiently. We test the integer linear programming formulations and this branch and bound method computationally on randomly generated test problems.

Radiation therapy planning is always a tight rope walk between dangerous insufficient dose in the target volume and life threatening overdosing of organs at risk. Finding ideal balances between these inherently contradictory goals challenges dosimetrists and physicians in their daily practice. Today’s planning systems are typically based on a single evaluation function that measures the quality of a radiation treatment plan. Unfortunately, such a one dimensional approach cannot satisfactorily map the different backgrounds of physicians and the patient dependent necessities. So, too often a time consuming iteration process between evaluation of dose distribution and redefinition of the evaluation function is needed. In this paper we propose a generic multi-criteria approach based on Pareto’s solution concept. For each entity of interest - target volume or organ at risk a structure dependent evaluation function is defined measuring deviations from ideal doses that are calculated from statistical functions. A reasonable bunch of clinically meaningful Pareto optimal solutions are stored in a data base, which can be interactively searched by physicians. The system guarantees dynamical planning as well as the discussion of tradeoffs between different entities. Mathematically, we model the upcoming inverse problem as a multi-criteria linear programming problem. Because of the large scale nature of the problem it is not possible to solve the problem in a 3D-setting without adaptive reduction by appropriate approximation schemes. Our approach is twofold: First, the discretization of the continuous problem is based on an adaptive hierarchical clustering process which is used for a local refinement of constraints during the optimization procedure. Second, the set of Pareto optimal solutions is approximated by an adaptive grid of representatives that are found by a hybrid process of calculating extreme compromises and interpolation methods.

This publication tries to develop mathematical subjects for school from realistic problems. The center of this report are business planning and decision problems which occur in almost all companies. The main topics are: Calculation of raw material demand for given orders, consumption of existing stock and the lot sizing.

We present two heuristic methods for solving the Discrete Ordered Median Problem (DOMP), for which no such approaches have been developed so far. The DOMP generalizes classical discrete facility location problems, such as the p-median, p-center and Uncapacitated Facility Location problems. The first procedure proposed in this paper is based on a genetic algorithm developed by Moreno Vega [MV96] for p-median and p-center problems. Additionally, a second heuristic approach based on the Variable Neighborhood Search metaheuristic (VNS) proposed by Hansen & Mladenovic [HM97] for the p-median problem is described. An extensive numerical study is presented to show the efficiency of both heuristics and compare them.

On a Multigrid Adaptive Refinement Solver for Saturated Non-Newtonian Flow in Porous Media A multigrid adaptive refinement algorithm for non-Newtonian flow in porous media is presented. The saturated flow of a non-Newtonian fluid is described by the continuity equation and the generalized Darcy law. The resulting second order nonlinear elliptic equation is discretized by a finite volume method on a cell-centered grid. A nonlinear full-multigrid, full-approximation-storage algorithm is implemented. As a smoother, a single grid solver based on Picard linearization and Gauss-Seidel relaxation is used. Further, a local refinement multigrid algorithm on a composite grid is developed. A residual based error indicator is used in the adaptive refinement criterion. A special implementation approach is used, which allows us to perform unstructured local refinement in conjunction with the finite volume discretization. Several results from numerical experiments are presented in order to examine the performance of the solver.

Objective: In some surgical specialties, e.g. orthopedics, robots are already used in the operating room for bony milling work. Oto- and otoneurosurgery may also greatly benefit by robotic enhanced precision. Study Design: Experimental study on robotic milling on oak wood and human temporal bone specimen. Methods: A standard industrial robot with a 6 degrees-of-freedom serial kinematics was used with force feedback to proportionally control the robot speed. Different milling modes and characteristic path parameters were evaluated to generate milling paths based on CAD geometry data of a cochlear implant and an implantable hearing system. Results: The best suited strategy proofed to be the spiral horizontal milling mode with the burr held perpendicularly to the temporal bone surface. In order to avoid high grooves, the distance in between paths should equal half the radius of the cutting burr head. Due to the vibration of the robot’s own motors, a rather high oscillation of the standard deviation of forces was encountered. This oscillation dropped drastically to nearly 0 N, when the burr head reached contact with the dura mater due to its damping characteristics. The cutting burr could be moved a long time on the dura without damaging it, because of its rather blunt head. The robot moved the burr very smoothly according to the encountered resistances. Conclusion: This is the first development of an functioning robotic milling procedure for otoneurosurgery with force-based speed control. It is planned to implement ultrasound-based local navigation and to perform robotic mastoidectomy.

A new stability preserving model reduction algorithm for discrete linear SISO-systems based on their impulse response is proposed. Similar to the Padé approximation, an equation system for the Markov parameters involving the Hankel matrix is considered, that here however is chosen to be of very high dimension. Although this equation system therefore in general cannot be solved exactly, it is proved that the approximate solution, computed via the Moore-Penrose inverse, gives rise to a stability preserving reduction scheme, a property that cannot be guaranteed for the Padé approach. Furthermore, the proposed algorithm is compared to another stability preserving reduction approach, namely the balanced truncation method, showing comparable performance of the reduced systems. The balanced truncation method however starts from a state space description of the systems and in general is expected to be more computational demanding.

This report explains basic notions and concepts of Abstract State Machines (ASM) as well as notation for defining ASM models. The objective here is to provide an intuitive understanding of the formalism; for a rigorous definition of the mathematical foundations of ASM, the reader is referred to [2] and [3]. Further references on ASM-related material can be found on the ASM Web Pages [1].

UML and SDL are languages for the development of software systems that have different origins, and have evolved separately for many years. Recently, it can be observed that OMG and ITU, the standardisation bodies responsible for UML and SDL, respectively, are making efforts to harmonise these languages. So far, harmonisation takes place mainly on a conceptual level, by extending and aligning the set of language concepts. In this paper, we argue that harmonisation of languages can be approached both from a syntactic and semantic perspective. We show how a common syntactical basis can be derived from the analysis of the UML meta-model
and the SDL abstract grammar. For this purpose, conceptually sound and well-founded mappings from meta-models to abstract grammars and vice versa are defined and applied. On the semantic level, a comparison between corresponding language constructs is performed.

Industrial analog circuits are usually designed using numerical simulation tools. To obtain a deeper circuit understanding, symbolic analysis techniques can additionally be applied. Approximation methods which reduce the complexity of symbolic expressions are needed in order to handle industrial-sized problems. This paper will give an overview to the field of symbolic analog circuit analysis. Starting with a motivation, the state-of-the-art simplification algorithms for linear as well as for nonlinear circuits are presented. The basic ideas behind the different techniques are described, whereas the technical details can be found in the cited references. Finally, the application of linear and nonlinear symbolic analysis will be shown on two example circuits.