Evaluation System for Autonomous Control Methods in Coupled Planning and Control Systems

Grundstein, Sebastian; Schukraft, Susanne; Scholz‐Reiter, Bernd; Freitag, Michael

doi:10.1016/j.procir.2015.06.023

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…In this context, the coupling of autonomous control with commonly used central planning methods aims at the usage of the advantages of both approaches -the high logistic performance of autonomous control in dynamic environments and the high planning accuracy and optimality of central planning in rather static environments [5]. Thereby, a challenge is the logical target conflict between the logistic efficiency of autonomous control and the high planning accuracy of central planning.…”

Section: Introductionmentioning

confidence: 99%

“…The concept of coupling is based on the idea of a combined application of central planning and autonomous control. Thereby, coupling aims at a high logistic efficiency in dynamic production environments and simultaneously, at the provision of a sufficiently high planning accuracy so far provided by central planning only in rather static environments [5]. The coupling of central planning and autonomous control requires the methods' ability to exchange decision relevant information and to consider and execute necessary adaptions due to occurring dynamic influences and resulting deviations of predefined planning results.…”

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confidence: 99%

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Evaluation of Strategies for the Coupling of Central Planning and Autonomous Control in Dynamic Job Shop Environments

Schukraft

Grundstein

Freitag

et al. 2016

AMR

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Autonomous control is a promising approach to cope with increasing dynamics in production environments. However, an important issue for the acceptance of autonomous control is the provision of a sufficient planning accuracy, e.g., regarding order sequences or workstation assignments. Therefore, transferring autonomous control into practical application requires the coupling of autonomous control with commonly used central planning methods. This paper introduces coupling strategies with different degrees of planning adherence. The introduced strategies are applied to the example of a job shop environment based on real data. The simulation-based comparison considers performance indicators concerning the logistic performance and planning adherence. Besides varying the level of dynamic influences, the simulation study also analyses the impact of different method combinations onto the coupled methods’ performance.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Evaluation of Strategies for the Coupling of Central Planning and Autonomous Control in Dynamic Job Shop Environments

Schukraft

Grundstein

Freitag

et al. 2016

AMR

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“…Therefore, Philipp et al (2007) introduced a vectorial approach that considers these basic objectives with weighting factors to adjust the influence of each objective onto the total logistics target achievement. Grundstein et al (2015b) enhanced the vectorial approach to additionally consider measurements of planning adherence.…”

Section: Evaluation Of Autonomously Controlled Systemsmentioning

confidence: 99%

“…The measurement contains criteria within the categories "decision-making," "information processing," and "decision execution" and, thus, covers the fundamental characteristics of autonomous control. Grundstein et al (2015b) expanded the morphological pattern to consider both the planning and the control level. In detail, the expansion focused on criteria to describe coupling strategies used for a combined application of conventional planning and autonomous control (see Sect.…”

Section: Evaluation Of Autonomously Controlled Systemsmentioning

confidence: 99%

Autonomous Control of Logistic Processes: A Retrospective

Schukraft

Teucke

Freitag

et al. 2021

Dynamics in Logistics

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Manufacturing and logistic service companies are increasingly confronted with high dynamics and complexity. Due to its particular suitability for short-term and situation-dependent decision-making, autonomous control can improve planning and control of production and related transportation processes. This chapter gives an overview of the research that the BIBA—Bremer Institut für Produktion und Logistik GmbH has performed over the past years in the field of autonomously controlled production and transportation networks. The chapter focuses on the modeling approaches and the autonomous control methods that have been developed. These methods have been evaluated using both theoretical and real-world scenarios. The results show the applicability and suitability of autonomous control in complex and dynamic production and transportation environments. In addition, influences on the methods’ performance and the integration of autonomous control into conventional planning and control systems are discussed. Finally, the chapter looks at the significance of autonomous control in the context of Industry 4.0 and shows the relations between both concepts.

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Complexity-Oriented Evaluation of Production Systems for Online-Switching of Autonomous Control Methods

Mueller

Graefenstein

Scholz

et al. 2019

Advances in Production, Logistics and Traffic

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Evaluation System for Autonomous Control Methods in Coupled Planning and Control Systems

Cited by 7 publications

References 20 publications

Evaluation of Strategies for the Coupling of Central Planning and Autonomous Control in Dynamic Job Shop Environments

Evaluation of Strategies for the Coupling of Central Planning and Autonomous Control in Dynamic Job Shop Environments

Autonomous Control of Logistic Processes: A Retrospective

Complexity-Oriented Evaluation of Production Systems for Online-Switching of Autonomous Control Methods

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