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Es ist schon lange bekannt, dass es ein Nachwuchsproblem im Versicherungs-vertrieb gibt. Die Versicherer haben mit Imageproblemen zu kämpfen – Schlagzeilen über Lustreisen, Falschberatungen oder Provisionsexzesse machen die Runde. Auch der demografische Wandel macht es den Versicherern schwer, geeigneten Nachwuchs für den Vertrieb zu rekrutieren. Da liegt es nahe, dass die Branche sehr daran interessiert ist, mehr Frauen für den Vertrieb zu ge- winnen, zumal sie die erforderlichen Vertriebskompetenzen besitzen. Für Frauen ist die Tätigkeit im Vertrieb allerdings nicht besonders attraktiv; die geringe Sicherheit und die Arbeit am Abend sind die größten Hindernisse, die Frauen nennen. Um die Tätigkeit für Frauen attraktiver zu machen, müssten die Ver- sicherungsunternehmen Anreizsysteme, Strukturen bis hin zur Vertriebskultur verändern. Die Frage ist, ob sich das lohnt. In diesem Zusammenhang ist es für die Unternehmen von besonderer Bedeutung zu wissen, was die Kunden dazu sagen, wenn mehr Frauen im Vertrieb tätig wären.
Benchmark experiments are required to test, compare, tune, and understand optimization algorithms. Ideally, benchmark problems closely reflect real-world problem behavior. Yet, real-world problems are not always readily available for benchmarking. For example, evaluation costs may be too high, or resources are unavailable (e.g., software or equipment). As a solution, data from previous evaluations can be used to train surrogate models which are then used for benchmarking. The goal is to generate test functions on which the performance of an algorithm is similar to that on the real-world objective function. However, predictions from data-driven models tend to be smoother than the ground-truth from which the training data is derived. This is especially problematic when the training data becomes sparse. The resulting benchmarks may not reflect the landscape features of the ground-truth, are too easy, and may lead to biased conclusions.
To resolve this, we use simulation of Gaussian processes instead of estimation (or prediction). This retains the covariance properties estimated during model training. While previous research suggested a decomposition-based approach for a small-scale, discrete problem, we show that the spectral simulation method enables simulation for continuous optimization problems. In a set of experiments with an artificial ground-truth, we demonstrate that this yields more accurate benchmarks than simply predicting with the Gaussian process model.
Multi-criteria optimization has gained increasing attention during the last decades. This article exemplifies multi-criteria features, which are implemented in the statistical software package SPOT. It describes related software packages such as mco and emoa and gives a comprehensive introduction to simple multi criteria optimization tasks. Several hands-on examples are used for illustration. The article is well-suited as a starting point for performing multi-criteria optimization tasks with SPOT.
An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. Accurate simulation models are mandatory for this optimization, because the underlying chemical processes are very slow. The simulation models themselves may be time-consuming to evaluate, hence we show how to use surrogate-model-based approaches to optimize biogas plants efficiently. In detail, a Kriging surrogate is employed. To improve model quality of this surrogate, we integrate cheaply available data into the optimization process. Doing so, Multi-fidelity modeling methods like Co-Kriging are employed. Furthermore, a two-layered modeling approach is employed to avoid deterioration of model quality due to discontinuities in the search space. At the same time, the cheaply available data is shown to be very useful for initialization of the employed optimization algorithms. Overall, we show how biogas plants can be efficiently modeled using data-driven methods, avoiding discontinuities as well as including cheaply available data. The application of the derived surrogate models to an optimization process is shown to be very difficult, yet successful for a lower problem dimension.
When designing or developing optimization algorithms, test functions are crucial to evaluate
performance. Often, test functions are not sufficiently difficult, diverse, flexible or relevant to real-world
applications. Previously,
test functions with real-world relevance were generated by training a machine learning model based on
real-world data. The model estimation is used as a test function.
We propose a more principled approach using simulation instead of estimation.
Thus, relevant and varied test functions
are created which represent the behavior of real-world fitness landscapes.
Importantly, estimation can lead to excessively smooth test functions
while simulation may avoid this pitfall. Moreover, the simulation
can be conditioned by the data, so that the simulation reproduces the training data
but features diverse behavior in unobserved regions of the search space.
The proposed test function generator is illustrated with an intuitive, one-dimensional
example. To demonstrate the utility of this approach it
is applied to a protein sequence optimization problem.
This application demonstrates the advantages as well as practical limits of simulation-based
test functions.
Formerly, multi-criteria optimization algorithms were often tested using tens of thousands function evaluations. In many real-world settings function evaluations are very costly or the available budget is very limited. Several methods were developed to solve these cost-extensive multi-criteria optimization problems by reducing the number of function evaluations by means of surrogate optimization. In this study, we apply different multi-criteria surrogate optimization methods to improve (tune) an event-detection software for water-quality monitoring. For tuning two important parameters of this software, four state-of-the-art methods are compared: S-Metric-Selection Efficient Global Optimization (SMS-EGO), S-Metric-Expected Improvement for Efficient Global Optimization SExI-EGO, Euclidean Distance based Expected Improvement Euclid-EI (here referred to as MEI-SPOT due to its implementation in the Sequential Parameter Optimization Toolbox SPOT) and a multi-criteria approach based on SPO (MSPOT). Analyzing the performance of the different methods provides insight into the working-mechanisms of cutting-edge multi-criteria solvers. As one of the approaches, namely MSPOT, does not consider the prediction variance of the surrogate model, it is of interest whether this can lead to premature convergence on the practical tuning problem. Furthermore, all four approaches will be compared to a simple SMS-EMOA to validate that the use of surrogate models is justified on this problem.
The fashion industry is one of the most harmful industries in the world. Many garments are produced and dyed on a petroleum base; vast amounts of water are used in the production of textiles and garments; and environmentally harmful chemicals are released into the environment during production. Working towards sustainability in fashion is more relevant than ever. One way of doing this is to design garments with nature. To do so, I am presenting an example of biotechnology in the field of dyeing techniques. The project is innovative and future-driven in that it offers alternatives to industrial, petroleum-based dyeing techniques. A number of bacteria produce different colored pigments. The bacteria strain Janthinobacterium lividum, for example, is considered nontoxic and safe to handle. It is found in nature on the skin of the red-backed salamander. The bacteria produce dark purple pigments called violacein. With the pigment’s antifungal and antibacterial characteristics, it protects itself and its host from intruders. Can the pigment, however, be applied to dye textiles?
Ein Wiki ist eine Form von Social Software, die sich insbesondere zur Unterstützung der kollaborativen Wissensverarbeitung eignet und zunehmend im Unternehmenskontext eingesetzt wird. Ein Wiki lebt vom Prinzip der kollektiven Intelligenz. Somit wird die Schaffung einer kritischen Masse an Nutzern zum Erfolgsfaktor. Erkennt der Nutzer einen echten Mehrwert, so ist er gewillt, die Anwendung zu nutzen. Um diesen Mehrwert zu generieren, erfordert es eine funktionale Integration in die Arbeitsprozesse und die Systemlandschaft des Unternehmens. Um dies zu gewährleisten, wird in der vorliegenden Veröffentlichung eine idealtypische Vorgehensweise zur Einführung eines Wikis entwickelt. Es wird gezeigt, wie sich der Grundgedanke des prozessorientierten Wissensmanagements auf die Konzeption eines Wikis übertragen lässt. Die erarbeiteten theoretischen Grundlagen werden an der beispielhaften fachlichen Konzeption eines Wikis zur Unterstützung des Wissensmanagements im Produktentwicklungsprozess eines Lebens-versicherers angewendet. Zu diesem Zweck werden Anwendungsszenarien entwickelt, in denen die Nutzer durch den Einsatz des Wikis profitieren.
Die Frage, ob Big Data und Künstliche Intelligenz (KI) die Versicherungswirtschaft revolutionieren, beschäftigt schon seit einiger Zeit unsere Gesellschaft sowie im Besonderen die Versicherungsbranche. Die Fortschritte in jüngster Vergangenheit in der KI und bei der Auswertung großer Datenmengen sowie die große mediale Aufmerksamkeit sind immens. Somit waren Big Data und Künstliche Intelligenz auch die diesjährigen vielversprechenden Themen des 24. Kölner Versicherungs-
symposiums der TH Köln am 14. November 2019: Das ivwKöln hatte zum fachlichen Austausch eingeladen, ein attraktives Vortragsprogramm zusammengestellt und Networking-Gelegenheiten für die Gäste aus Forschung und Praxis vorbereitet. Der vorliegende Proceedings-Band umfasst die Vortragsinhalte der verschiedenen Referenten.