Modeling of orders in autonomously controlled logistic systems

Scholz‐Reiter, Bernd; Sowade, Steffen; Hildebrandt, Torsten; Rippel, Daniel

doi:10.1007/s11740-010-0253-2

Cited by 4 publications

(1 citation statement)

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“…In this case, these objects are at least commodities and resources, i. e. machines. Orders may be included as intelligent objects if desired [23]. Following the ALEM bottom up modeling approach, the experts model the objects' abilities, processes, and knowledge, as well the data exchanges between them.…”

Section: Scenariosmentioning

confidence: 99%

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: Scenariosmentioning

confidence: 99%