Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environment

Luo, Hao; Fang, Jianxin; Huang, George Q.

doi:10.1016/j.cie.2014.09.019

Cited by 55 publications

(12 citation statements)

References 44 publications

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“…Fourth, our research highlighted the presence of models, frameworks and architectures related to the implementation of the Smart Factory (Wang et al 2016b, Luo et al 2015, Gaughran et al 2007, along with highlevel "landscape" analyses (Davis et al 2012). It should be noted that these scientific contributions propose conceptual works and experiments, and rarely actual test-beds and lessons learned from the practice are described and discussed (Zuehlke, 2010).…”

Section: Discussion Of the Findings And Research Agendamentioning

confidence: 89%

Literature review on the ‘Smart Factory’ concept using bibliometric tools

Strozzi

Colicchia

Creazza

et al. 2017

International Journal of Production Research

392

292

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Section: Discussion Of the Findings And Research Agendamentioning

confidence: 89%

Literature review on the ‘Smart Factory’ concept using bibliometric tools

Strozzi

Colicchia

Creazza

et al. 2017

International Journal of Production Research

392

292

View full text Add to dashboard Cite

show abstract

“…It can be generalized to other permutation-based optimization problems, e.g., vehicle routing [37,38], scheduling [39][40][41] and path planning [42,43], as long as a suitable closeness measure could be designed. Note that, in practice, some of these problems may need to be solved either in real-time [44,45] or in dynamic environments [46], which means efficiency is even more important for these problems than for CARP. The idea of hierarchical decomposition is expected to benefit the development of scalable approaches in those domains.…”

Section: Discussionmentioning

confidence: 99%

A Scalable Approach to Capacitated Arc Routing Problems Based on Hierarchical Decomposition

Tang

Wang

et al. 2017

IEEE Trans. Cybern.

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The capacitated arc routing problem (CARP) is a challenging optimization problem with lots of applications in the real world. Numerous approaches have been proposed to tackle this problem. Most of these methods, albeit showing good performance on CARP instances of small and median sizes, do not scale well to large-scale CARPs, e.g., taking at least a few hours to achieve a satisfactory solution on a CARP instance with thousands of tasks. In this paper, an efficient and scalable approach is proposed for CARPs. The key idea of the proposed approach is to hierarchically decompose the tasks involved in a CARP instance into subgroups and solve the induced subproblems recursively. The output of the subproblems at the lower layer in the hierarchy is treated as virtual tasks and new subproblems are formulated based on these virtual tasks using clustering techniques. By this means, the number of tasks (or virtual tasks) decreases rapidly from the bottom to the top layers of the hierarchy, and the sizes of all subproblems at each layer can be kept tractable even for very large-scale CARPs. Empirical studies are conducted on CARP instances with up to 3584 tasks, which are an order of magnitude larger than the number of tasks involved in all CARP instances investigated in the literature. The results show that the proposed approach significantly outperforms existing methods in terms of scalability. Since the proposed hierarchical decomposition scheme is designed to obtain a good permutation of tasks in a CARP instance, it may also be generalized to other hard optimization problems that can be formulated as permutation-based optimization problems.

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“…In addition to displaying the planning alternatives in an overview, the system must offer functionalities to show the individual alternatives in detail (FR rtd 12). In the respective detailed view of an alternative, the user should also be able to edit the alternative (FR rtd 13) [43]. In a final step of reacting to deviations, the decision maker has to select a planning alternative, which then becomes operative (FR rtd 14) [21].…”

Section: Requirementmentioning

confidence: 99%

Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems

Freier¹,

Schümann²

2020

WI2020 Zentrale Tracks

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The use of cyber-physical systems in production promises great potential for production scheduling since a larger information base is available for the scheduling of production orders. However, the mere acquisition of realtime data does not inherently lead to improvements. On the contrary, a targeted preparation of the data is required in order to prevent an information overload. Decision support systems that support decision makers in production scheduling can perform this task. However, the design of such systems in combination with cyber-physical systems has hardly been investigated so far. In this paper, we therefore design and implement a corresponding decision support system in a design science approach. For this, we identify meta-requirements based on a literature analysis and an interview study. Finally, we evaluate the created meta-artifact in a laboratory setting in order to obtain generalizable knowledge about building such a decision support system.

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Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environment

Cited by 55 publications

References 44 publications

Literature review on the ‘Smart Factory’ concept using bibliometric tools

Literature review on the ‘Smart Factory’ concept using bibliometric tools

A Scalable Approach to Capacitated Arc Routing Problems Based on Hierarchical Decomposition

Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems

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