A Framework for Online Detection and Reaction to Disturbances on the Shop Floor Using Process Mining and Machine Learning

The shop floor is a dynamic environment, where deviations to the production plan frequently occur. While there are many tools to support production planning, production control is left unsupported in handling disruptions. The production controller evaluates the deviations and selects the most suitable countermeasures based on his experience. The transparency should be increased in order to improve the decision quality of the production controller by providing meaningful information during his decision process. In this paper, we propose a framework in which an interactive production control system supports the controller in the identification of and reaction to disturbances on the shop floor. At the same time, the system is being improved and updated by the domain knowledge of the controller. The reference architecture consists of three main parts. The first part is the process mining platform, the second part is the machine learning subsystem that consists of a part for the classification of the disturbances and one part for recommending countermeasures to identified disturbances. The third part is the interactive user interface. Integrating the user’s feedback will enable an adaptation to the constantly changing constraints of production control. As an outlook for a technical realization, the design of the user interface and the way of interaction is presented. For the evaluation of our framework, we will use simulated event data of a sample production line. The implementation and test should result in higher production performance by reducing the downtime of the production and increase in its productivity

Differential diagnostic challenge of chronic neutrophilic leukemia in a patient with prolonged leukocytosis

Our interesting case deals with the clinical and morphological aspects of a chronic neutrophilic leukemia and the critical evaluation of differential diagnosis of leukemoid reaction in bone marrow biopsies

Automatic Adaptation to Generated Content Via Car Setup Optimization in TORCS

Abstract — The car setup optimization problem as employed for a recent competition is a real-valued, 22 variable gray box problem (some dependencies are known) which challenges optimization algorithms in many ways. Runs must be short, there is a considerable amount of noise, and evaluation times for each solution candidate have to be determined by the algorithm itself. We take this as example problem and ask what happens if a user or a procedural content generator provides new tracks on which the standard cars are almost undriveable. Consequently, we suggest to use an optimization algorithm to adapt the cars to the track in almost real-time (minutes). We investigate how the CMA-ES, a modern evolutionary strategy, fares in this context and suggest some means to adapt it to the requirements. Attempts to improve the results using special noise handling methods unfortunately fail, most likely due to the very hard time constraints. Additionally, we perform a basic test with humans driving the optimized cars and have a short look at the properties of the cars changed for improving their performance. I

On the Integration of Theoretical Single-Objective Scheduling Results for Multi-objective Problems

to appearInternational audienceWe present a modular and flexible algorithmic framework to enable a fusion of scheduling theory and evolutionary multi-objective combinatorial optimization. For single-objective scheduling problems, that is the optimization of task assignments to sparse resources over time, a variety of optimal algorithms or heuristic rules are available. However, in the multi-objective domain it is often impossible to provide specific and theoretically well founded algorithmic solutions. In that situation, multi-objective evolutionary algorithms are commonly used. Although several standard heuristics from this domain exist, most of them hardly allow the integration of available single-objective problem knowledge without complex redesign of the algorithms structure itself. The redesign and tuned application of common evolutionary multi-objective optimizers is far beyond the scope of scheduling research. We therefore describe a framework based on a cellular and agent-based approach which allows the straightforward construction ofmulti-objective optimizers by compositing single-objective scheduling heuristics. In a case study, we address strongly NP-hard parallel machine scheduling problems and compose optimizers combining the known single-objective results. We eventually show that this approach can bridge between scheduling theory and evolutionary multi-objective search

On the Integration of Theoretical Single-Objective Scheduling Results for Multi-objective Problems

to appearInternational audienceWe present a modular and flexible algorithmic framework to enable a fusion of scheduling theory and evolutionary multi-objective combinatorial optimization. For single-objective scheduling problems, that is the optimization of task assignments to sparse resources over time, a variety of optimal algorithms or heuristic rules are available. However, in the multi-objective domain it is often impossible to provide specific and theoretically well founded algorithmic solutions. In that situation, multi-objective evolutionary algorithms are commonly used. Although several standard heuristics from this domain exist, most of them hardly allow the integration of available single-objective problem knowledge without complex redesign of the algorithms structure itself. The redesign and tuned application of common evolutionary multi-objective optimizers is far beyond the scope of scheduling research. We therefore describe a framework based on a cellular and agent-based approach which allows the straightforward construction ofmulti-objective optimizers by compositing single-objective scheduling heuristics. In a case study, we address strongly NP-hard parallel machine scheduling problems and compose optimizers combining the known single-objective results. We eventually show that this approach can bridge between scheduling theory and evolutionary multi-objective search