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This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking is its main goal. The article discusses eight essential topics in benchmarking: clearly stated goals, well- specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility. The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers. As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic updates.
The use of surrogate models is a standard method to deal with complex, realworld
optimization problems. The first surrogate models were applied to continuous
optimization problems. In recent years, surrogate models gained importance
for discrete optimization problems. This article, which consists of three
parts, takes care of this development. The first part presents a survey of modelbased
methods, focusing on continuous optimization. It introduces a taxonomy,
which is useful as a guideline for selecting adequate model-based optimization
tools. The second part provides details for the case of discrete optimization
problems. Here, six strategies for dealing with discrete data structures are introduced.
A new approach for combining surrogate information via stacking
is proposed in the third part. The implementation of this approach will be
available in the open source R package SPOT2. The article concludes with a
discussion of recent developments and challenges in both application domains.
Data pre-processing is a key research topic in data mining because it plays a
crucial role in improving the accuracy of any data mining algorithm. In most
real world cases, a significant amount of the recorded data is found missing
due to most diverse errors. This loss of data is nearly always unavoidable.
Recovery of missing data plays a vital role in avoiding inaccurate data
mining decisions. Most multivariate imputation methods are not compatible
to univariate datasets and the traditional univariate imputation techniques
become highly biased as the missing data gap increases. With the current
technological advancements abundant data is being captured every second.
Hence, we intend to develop a new algorithm that enables maximum
utilization of the available big datasets for imputation. In this paper, we
present a Seasonal and Trend decomposition using Loess (STL) based
Seasonal Moving Window Algorithm, which is capable of handling patterns
with trend as well as cyclic characteristics. We show that the algorithm is
highly suitable for pre-processing of large datasets.
This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes declarative goals of the user, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and varying use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case.
When researchers and practitioners in the field of
computational intelligence are confronted with real-world
problems, the question arises which method is the best to
apply. Nowadays, there are several, well established test
suites and well known artificial benchmark functions
available.
However, relevance and applicability of these methods to
real-world problems remains an open question in many
situations. Furthermore, the generalizability of these
methods cannot be taken for granted.
This paper describes a data-driven approach for the
generation of test instances, which is based on
real-world data. The test instance generation uses
data-preprocessing, feature extraction, modeling, and
parameterization. We apply this methodology on a classical
design of experiment real-world project and generate test
instances for benchmarking, e.g. design methods, surrogate
techniques, and optimization algorithms. While most
available results of methods applied on real-world
problems lack availability of the data for comparison,
our future goal is to create a toolbox covering multiple
data sets of real-world projects to provide a test
function generator to the research community.
A pension system is resilient if it able to absorb external (temporal) shocks and if it is able to adapt to (longterm) shifts of the socio-economic environment. Defined benefit (DB) and defined contribution pension plans behave contrastingly with respect to capital market shocks and shifts: while DB-plan benefits are not affected by external shocks they totally lack adaptability with respect to fundamental changes; DC-plans automatically adjust to a changing environment but any external shock has a direct impact on the (expected) pensions. By adding a collective component to DC-plans one can make these collective DC (CDC)-plans shock absorbing - at least to a certain degree. In our CDC pension model we build a collective reserve of assets that serves as a buffer to capital market shocks, e.g. stock market crashes. The idea is to transfer money from the collective reserve to the individual pension accounts whenever capital markets slump and to feed the collective reserve whenever capital market are booming. This mechanism is particular valuable for age cohorts that are close to retirement. It is clear that withdrawing assets from or adding assets to the collective reserve is essentially a transfer of assets between the age cohorts. In our near reality model we investigate the effect of stock market shocks and interest rate (and mortality) shifts on a CDC- pension system. We are particularly interested in the question, to what extend a CDC-pension system is actually able to absorb shocks and whether the intergenerational transfer of assets via the collective reserve can be regarded as fair.
Collective Defined Contribution Plans – Backtesting Based on German Capital Market Data 1950 - 2022
(2022)
Using historical capital market data for Germany (1950-2022) we analyze and compare (individual) defined contribution (IDC-) and collective defined contribution (CDC) pension plans. To this end we define simple asset liability management rules that govern a CDC pension plan and compare these to IDC-plans with the same asset allovation. Our main result is, that the CDC pension plans allow for a significant improvement of the risk return profile compared to individual pension plans. Hereby we consider different risk measures. This empirical study affirms the theoretical results based on stochastic CDC-models.
To maximize the throughput of a hot rolling mill,
the number of passes has to be reduced. This can be achieved by maximizing the thickness reduction in each pass. For this purpose, exact predictions of roll force and torque are required. Hence, the predictive models that describe the physical behavior of the product have to be accurate and cover a wide range of different materials.
Due to market requirements a lot of new materials are tested and rolled. If these materials are chosen to be rolled more often, a suitable flow curve has to be established. It is not reasonable to determine those flow curves in laboratory, because of costs and time. A strong demand for quick parameter determination and the optimization of flow curve parameter with minimum costs is the logical consequence. Therefore parameter estimation and the optimization with real data, which were collected during previous runs, is a promising idea. Producers benefit from this data-driven approach and receive a huge gain in flexibility when rolling new
materials, optimizing current production, and increasing quality. This concept would also allow to optimize flow curve parameters, which have already been treated by standard methods. In this article, a new data-driven approach for predicting the physical behavior of the product and setting important parameters is presented.
We demonstrate how the prediction quality of the roll force and roll torque can be optimized sustainably. This offers the opportunity to continuously increase the workload in each pass to the theoretical maximum while product quality and process stability can also be improved.
Architecural aproaches are considered to simplify the generation of re-usable building blocks in the field of data warehousing. While SAP’s Layer Scalable Architecure (LSA) offers a reference model for creating data warehousing infrastructure based on SAP software, extented reference models are needed to guide the integration of SAP and non-SAP tools. Therefore, SAP’s LSA is compared to the Data Warehouse Architectural Reference Model (DWARM), which aims to cover the classical data warehouse topologies.
In the present paper a calculation tool for the lifetime prediction of composite materials with focus on local multiaxial
stress states and different local stress ratios within each lamina is developed. The approach is based on repetitiv, progressive in-plane stress calculations using classical laminate theory with subsequent analysis of the material stressing effort and use of appropriate material degradation models. Therefore experimentally data of S-N curves are
used to generate anistropic constant life diagrams for a closer examination of critical fracture planes under any given combination of local stress ratios. The model is verified against various balanced angle plies and multi-directional
laminates with arbitrary stacking sequences and varying stress ratios throughout the analysis. Different sections of the
model, such as residual strength and residual stiffness, are examined and verified over a wide range of load cycles. The obtained results agree very well with the analyzed experimental data.