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Automated storage and retrieval systems (ASRS) are important in distribution centers and warehouses. To decrease cost or CO2emissions it is natural to optimize various aspects of an ASRS. In this work, we provide a concept for a two-phase optimization combining two important optimization tasks in ASRS: Given multiple rearrangement jobs, we first sequence these jobs to minimize the total travelling distance of the cranes. We continue the optimization by computing optimal trajectories for the sequence to guarantee energy efficient driving of the cranes. We describe our algorithms for a complex ASRS architecture with two cranes on parallel rails in one aisle. Additionally, we describe how to use our results for parallelization of crane movements in the considered warehouse architecture.

We consider the problem of scheduling production jobs on a single machine with sequence dependent family setup times and individual job deadlines. Given a set of jobs, the goal is to minimize the total time to process all jobs while every job meets its deadline. We study algorithms that compute an exact solution to the problem. Motivated by one example use case, we exploit a natural structural observation that occurs in many production settings: the number of product configurations may be significantly smaller than the total number of jobs. We identify an algorithm that is efficient in this setting in terms of performance. We experimentally evaluate its running time and compare it with two other natural approaches of exact job scheduling.

Decision support systems have become a critical component in the planning processes of companies needing to solve difficult optimization problems. Multi-stage, stochastic optimization problems pose a particular challenge for decision makers, as the uncertainty in the input data makes it hard to determine the correct decisions. The scalable stochastic optimization (SSO) technique proposes a way of solving these problems, but is not able to provide feedback to a decision maker regarding why it makes its decisions. We suggest a mechanism for explaining the feedback of SSO to help decision makers better understand a decision support system’s recommendations.

Data-driven algorithms can be used to derive new information from data. In modern production plants, this can be used to reduce manual effort, e.g. to create a behavior model. In this work, one offline and one online algorithm are introduced that can determine the production cycles automatically. The algorithms use learned automaton to detect production cycles. A first evaluation is presented, which points out differences of the algorithms. However, overall the results are promising.

This paper introduces a novel approach to Model-Based Diagnosis (MBD) for hybrid technical systems. Unlike existing approaches which normally rely on qualitative diagnosis models expressed in logic, our approach applies a learned quantitative model that is used to derive residuals. Based on these residuals a diagnosis model is generated and used for a root cause identification. The new solution has several advantages such as the easy integration of new machine learning algorithms into MBD, a seamless integration of qualitative models, and a significant speed-up of the diagnosis runtime. The paper at hand formally defines the new approach, outlines its advantages and drawbacks, and presents an evaluation with real-world use cases.

Cyber-physical production systems (CPPS) integrate physical and computational resources due to increasingly available sensors and processing power. This enables the usage of data, to create additional benefit, such as condition monitoring or optimization. These capabilities can lead to cognition, such that the system is able to adapt independently to changing circumstances by learning from additional sensors information. Developing a reference architecture for the design of CPPS and standardization of machines and software interfaces is crucial to enable compatibility of data usage between different machine models and vendors. This paper analysis existing reference architecture regarding their cognitive abilities, based on requirements that are derived from three different use cases. The results from the evaluation of the reference architectures, which include two instances that stem from the field of cognitive science, reveal a gap in the applicability of the architectures regarding the generalizability and the level of abstraction. While reference architectures from the field of automation are suitable to address use case specific requirements, and do not address the general requirements, especially w.r.t. adaptability, the examples from the field of cognitive science are well usable to reach a high level of adaption and cognition. It is desirable to merge advantages of both classes of architectures to address challenges in the field of CPPS in Industrie 4.0.

The vision of smart factories are self-diagnosing, self-optimizing and self-adapting Cyber-Physical Production Systems (CPPS). Self-adaption, on which this paper focuses on, means that the CPPS can adapt itself to a changing environment, so that the downtime costs can be reduced by using the system modules most efficient. An architecture is introduced and demonstrated on a concrete use case to show how this capability can be achieved by using different Artificial Intelligence (AI) techniques. For each technique, we define challenges that have to be solved to use it in a real world environment. Additionally, we illustrate the symbolic and subsymbolic AI and argue why symbolic AI is an important aspect in the context of CPPS.

This work shows how OWL ontologies can be represented into the automation standards AutomationML and OPC UA. It is often asserted that an integration is possible, but no detailed review could be found. The integration of OWL into the standards is relevant, because it enables the collection and usage of data through the whole life cycle in OWL. We show that it is possible, but we identified some restriction regarding the representation in OPC UA.

Machine learning techniques have a huge potential to take some tasks of humans, e.g. anomaly detection or predictive maintenance, and thus support operators of cyber physical systems (CPSs). One challenge is to communicate algorithms results to machines or humans, because they are on a sub-symbolical level and thus hard to interpret. To simplify the communication and thereby the usage of the results, they have to be transferred to a symbolic representation. Today, the transformation is typically static which does not satisfy the needs for fast changing CPSs and prohibit the usage of the full machine learning potential. This work introduces a knowledge based approach of an automatic mapping between the sub-symbolic results of algorithms and their symbolic representation. Clustering is used to detect groups of similar data points which are interpreted as concepts. The information of clusters are extracted and further classified with the help of an ontology which infers the current operational state. Data from wind turbines is used to evaluate the approach. The achieved results are promising, the system can identify its operational state without an explicit mapping.

Trends in novel manufacturing systems lead to an increased level of data availability and smart usage of these data. Nowadays, many approaches are available to use the data, but because of an increased flexibility of the systems the interaction between machines and humans has become a challenge. Humans have to browse through a huge amount of data, need knowledge about the machine and underlying algorithms to interpret the results; they cannot use their known terms for communication, we call it the conceptual gap. The user should be enabled to communicate with the machine on a more abstract level and in a more natural way. Therefore, a natural language layer is introduced to provide users with a familiar interaction interface. Underlying layers contain knowledge about the domain, the machines and how data can be accessed and processed. This enables users' questions such as “Are there any anomalies in the system?” to be answered. Answers are provided in natural language and evaluated with a test set of 204 questions.