Bio-inspired and pheromone-based shop-floor control

Scholz‐Reiter, Bernd; Beer, Christoph de; Freitag, Michael; Jagalski, Thomas

doi:10.1080/09511920701607840

Cited by 33 publications

(13 citation statements)

References 5 publications

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“…From a logistic process expert's point of view, the behavior of an overall system becomes more and more nondeterministic with a growing number of interactions and processes. Additionally, the non-determinism of some autonomous control strategies (see for example [15]) reinforces this effect. In the context of testing and analyzing the model, nondeterminism leads to difficulties in recreating particular situations of interest for the purpose of simulation.…”

Section: Model Testingmentioning

confidence: 86%

Scalability Effects in Modeling Autonomously Controlled Logistic Processes: Challenges and Solutions in Business Process Modeling

Scholz‐Reiter

Rippel

Sowade

2011

Autonomous Cooperation and Control in Logistics

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Modeling of autonomous logistic processes requires detailed knowledge about logistic systems and about the design principles of autonomous control. The Autonomous Logistic Engineering Methodology incorporates both types of knowledge in varying degree in order to guide logistic process experts through the modeling process. Although the methodology enables modeling of any autonomous logistic process, two important challenges affect the methodology's scalability in case of large logistic scenarios that consist of several organizational independent companies. This article analyzes both consequential challenges, namely the increase of a system's complexity and the lack of obtainable information. It presents two logistic scenarios of different scale and discusses the selected challenges in detail. In addition, the article suggests a new type of model visualization and a set of collaboration mechanisms allowing to overcome of these challenges. IntroductionToday's logistic systems become more and more complex. In this situation, high fluctuations in customer demand and unforeseen events decrease the predictability of their behavior and increase their dynamics and vulnerability. While classical production planning and control systems are reaching their limits in order to deal with these effects, autonomously controlled logistic processes are a possible solution [6]. This concept aims to increase a logistic system's robustness and flexibility by distributing planning and control competencies to logistic objects, e. g. to commodities, half-finished goods, resources, and orders. Autonomously controlled logistic processes rely on the logistic objects' local decisions and lead to a positive emergence of the overall system's behavior [6].Logistic process experts face the tasks to design, model, and evaluate autonomous processes in order to apply autonomous control in logistic systems. This development process includes the specification of logistic processes, logistic objects' abilities, decision-making strategies, as well as a definition of an overall system in form of a logistic scenario. In order to guide logistic process experts through the development process, a modeling methodology called Autonomous Logistic Engipreliminary version 2 neering Methodology (ALEM) is being developed [20]. The methodology's applicability and its advantages have been demonstrated at the example of production logistic scenarios like shop-floor manufacturing systems [7]. Beyond that, the advantages of autonomous control obviously increase with the growing size of a logistic system, due to an increasing number of decision alternatives in its running processes as well as during the system's design process. Consequently, a decentralization of planning and control mechanisms in larger scaled logistic systems results in an increase of flexibility and robustness. For example, supply chains, production networks, or virtual enterprises constitute more complex scenarios than simple manufacturing scenarios. They cover a variety of logistic objects, each ha...

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Section: Model Testingmentioning

confidence: 86%

Scalability Effects in Modeling Autonomously Controlled Logistic Processes: Challenges and Solutions in Business Process Modeling

Scholz‐Reiter

Rippel

Sowade

2011

Autonomous Cooperation and Control in Logistics

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“…This is achievable with local agents. Besides, the logistic structure must supply distributed information on local states and different options for enabling decisions generally [51], which is possible with the use of a coordinating agent. Therefore, at the same time that distributed cooperation among agents and autonomous decision can enable self-x characteristics and high flexibility, the coordinating agent centralizes the feedback of the various system agents to achieve high global efficiency.…”

Section: Multi-agent Systemsmentioning

confidence: 99%

Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

et al. 2018

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Technological developments along with the emergence of Industry 4.0 allow for new approaches to solve industrial problems, such as the Job-shop Scheduling Problem (JSP). In this sense, embedding Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) is a highly promising approach to handle complex and dynamic JSPs. This paper proposes a data exchange framework in order to deal with the JSP considering the state-of-the-art technology regarding MAS, CPS and industrial standards. The proposed framework has self-configuring features to deal with disturbances in the production line. This is possible through the development of an intelligent system based on the use of agents and the Internet of Things (IoT) to achieve real-time data exchange and decision making in the job-shop. The performance of the proposed framework is tested in a simulation study based on a real industrial case. The results substantiate gains in flexibility, scalability and efficiency through the data exchange between factory layers. Finally, the paper presents insights regarding industrial applications in the Industry 4.0 era in general and in particular with regard to the framework implementation in the analyzed industrial case.

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“…They answer and report their availability to the former instance. Based on the implemented computated-action-algorithm the workpiece chooses a machine and registers [13]. The machine detects that the appropriate tool is not equipped yet.…”

Section: Autonomous Technologiesmentioning

confidence: 99%

Productivity evaluation of autonomous production objects

Gronau

Theuer

Lass

et al. 2010

2010 8th IEEE International Conference on Industrial Informatics

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The LUPO project's objective is the rapid evaluation of autonomous technologies in production, assembling and logistics in order to make a solid statement about the best mix of centralized and decentralized control in enterprises. For this purpose a hybrid simulator is developed. The hybrid simulator consists of physical work-piece demonstrators and machine-center demonstrators which are equipped with various autonomous communication techniques. Processes can be quickly built, varied, simulated and analyzed by using a modern production management system. At the end of the project it should be possible to receive reliable results on the benefits of autonomous production objects within a week. Potsdam

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Bio-inspired and pheromone-based shop-floor control

Cited by 33 publications

References 5 publications

Scalability Effects in Modeling Autonomously Controlled Logistic Processes: Challenges and Solutions in Business Process Modeling

Scalability Effects in Modeling Autonomously Controlled Logistic Processes: Challenges and Solutions in Business Process Modeling

Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

Productivity evaluation of autonomous production objects

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