Robust production scheduling under machine failures - A DES based evaluation approach

Himmiche, Sara; Marangé, Pascale; Aubry, Alexis; Pétin, Jean-François

doi:10.1016/j.ifacol.2018.06.312

Cited by 8 publications

(9 citation statements)

References 12 publications

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“…These indicators measure robustness against the probability that the performance of a plan or system stays above a certain threshold in each modelled scenario (Efthymiou et al, 2018). Himmiche et al (2018), for example, assess the likelihood of different machine capacity plans to still meet a required deadline despite certain disturbances and uncertainties. Yet, determining these likelihoods is not always trivial, and so this kind of indicator frequently lacks practicability.…”

Section: Robustness Indicators In Literaturementioning

confidence: 99%

“…A typical example for such an indicator-based approach is a scheduling problem under potential machine failure in which a time-related performance measure is used to assess robustness (e.g. Himmiche et al, 2018;Xiong et al, 2013;Al-Fawzan and Haouari, 2005). As a consequence, other determinants potentially impacting robustness, as well as other sources of disturbances, are neglected.…”

Section: Robustness Indicators In Literaturementioning

confidence: 99%

“…Instead of focusing solely on certain aspects of robustness or of the underlying production system (e.g. Himmiche et al, 2018;Benderbal et al, 2015;Hu et al, 2003), the indicator applies a holistic input-throughput-output perspective on a production system. In this way, it considers deviations in the deployed resources and in the resulting output next to the actual behavior of the system's throughput performance.…”

Section: Introductionmentioning

confidence: 99%

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Robustness assessment in production systems

Stockmann

Winkler

Kunath

2021

JMTM

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PurposeThe concept of robustness in manufacturing is not easy to capture and even harder to quantify. This paper elaborates an approach to assess robustness in production systems from a holistic input-throughput-output perspective using a pragmatic robustness indicator.Design/methodology/approachFirst, in order to have a precise understanding of what needs to be measured, a concept of robustness in production systems is defined based on a literature overview. Three different aspects are considered to be essential to comprehensively describe robustness in production: the deviations of input resources, of performance and of output. These aspects are translated into an aggregated indicator based on developments of production costs, order delays and output volumes. The indicator-based assessment approach is eventually applied to a flow-shop scheduling case study in the chipboard industry.FindingsThe study shows that an assessment of robustness should not solely focus on a single aspect of a production system. Instead, a holistic view is required addressing the tradeoffs that robustness must balance, such as the one between the realized performance, the corresponding resource requirements and the resulting output. Furthermore, the study emphasizes that robustness can be interpreted as a superior system capability that builds upon flexibility, agility, resilience and resistance.Research limitations/implicationsFirst, the paper is a call to further test and validate the proposed approach in industry case studies. Second, the paper suggests a modified understanding of robustness in production systems in which not only the deviation of one single variable is of interest but also the behavior of the whole system.Practical implicationsThe approach allows practitioners to pragmatically evaluate a production system’s robustness level while quickly identifying drivers, barriers and tradeoffs.Originality/valueCompared to existing assessment approaches the proposed methodology is one of the first that evaluates robustness in production systems from a holistic input-throughput-output perspective highlighting the different tradeoffs that have to be balanced. It is based upon a comprehensive concept of robustness which also links robustness to adjacent capabilities that were otherwise only treated separately.

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Section: Robustness Indicators In Literaturementioning

confidence: 99%

Section: Robustness Indicators In Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robustness assessment in production systems

Stockmann

Winkler

Kunath

2021

JMTM

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“…Xu et al 26 proposed an intelligent method to provide accurate prediction of scheduling performance indexes, which is capable of being used in the real world shop floor. Himmiche et al 21 proposed a discrete event systems (DES)-based evaluation approach for robust production scheduling under machine failures.…”

Section: Introductionmentioning

confidence: 99%

“…17 It should also address quality, effectiveness, profit and loss and stability performance indicators. On average, the research of production scheduling management mainly focuses on the production scheduling management information system 18 and the establishment, optimization 19 simulation 20 and robust processing of uncertainty [21][22][23] of the production scheduling model, which greatly improves the ability of production scheduling in enterprise. In the evaluation of production scheduling capability, Guinery's 24 research showed that effective production scheduling is critical to any competing manufacturing business.…”

Section: Introductionmentioning

confidence: 99%

A method for evaluating management capability of workshop production scheduling

Dong

Rao

Liu

et al. 2019

Advances in Mechanical Engineering

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The management capability of workshop production scheduling has been recognized as one of the important factors for the resource efficiency, delivery time and customer satisfaction of manufacturing enterprises. Therefore, a method for evaluating the management capability of production scheduling is proposed to optimize the production management. The accuracy, timeliness and effectiveness of production scheduling are measured by the formulation, execution, change and completion of the management process of production. The index model for management capability of production scheduling is established, which was driven by the data of actual production. Then, (1) scheduling generation time index, (2) scheduling execution deviation index, (3) scheduling change index and (4) scheduling completion index are built to quantitatively evaluate the capacity of production scheduling management. Finally, the usefulness and feasibility have been proved in an automobile parts-manufacturing enterprise. This article quantitatively evaluates the management capability of production scheduling. Both theoretical and results are demonstrated that the method is highly effective. The evaluation can help decision makers to better organize the production scheduling.

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A robust scheduling algorithm for space telescopes with unpredictable tasks

Jaubert

et al. 2020

Sci. China Technol. Sci.

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Robust production scheduling under machine failures - A DES based evaluation approach

Cited by 8 publications

References 12 publications

Robustness assessment in production systems

Robustness assessment in production systems

A method for evaluating management capability of workshop production scheduling

A robust scheduling algorithm for space telescopes with unpredictable tasks

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