Discrete event simulation as an ergonomic tool to predict workload exposures during systems design

Pérez, Juan Ignacio Asensio; Looze, M.P. de; Bösch, T.; Neumann, Patrick

doi:10.1016/j.ergon.2013.04.007

Cited by 56 publications

(28 citation statements)

References 48 publications

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“…Perez et al (2014) used a 6:1 difference in ET for static versus dynamic loading, indicating the limitation of models derived by data from solely static load study results. Perez et al found that for dynamic work, perceived fatigue data had the best match with the static load model of Rose et al (1992) compared with other models (e.g.…”

Section: Discussion On Some Methodological Aspectsmentioning

confidence: 99%

Fatigue and recovery during and after static loading

Rose¹,

Neumann²,

Hägg³

et al. 2014

Ergonomics

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Subjectively assessed endurance time (ET), resumption time (RT) and perceived discomfort, pain or fatigue (PD), and objectively measured maximum force-exerting capacity were investigated for varying loads and durations of a pushing task with two repeated trials. Beyond the main results quantifying how the load scenario affected ET, RT and PD, three additional results are of note: (1) although the maximum pushing force did not change between trials, shorter ET, longer RT and higher PD indicated accumulation of fatigue in Trial 2; (2) the PD ratings showed a trend with a linear increase during loading and a curvilinear decrease during recovery; and (3) the RT and the load level for different relative loading times were found to have an unexpected U-shaped relationship, indicating lowest fatigue at the intermediate load level. These results can be used to model a more sustainable and productive work-recovery ratio.

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Section: Discussion On Some Methodological Aspectsmentioning

confidence: 99%

Fatigue and recovery during and after static loading

Rose¹,

Neumann²,

Hägg³

et al. 2014

Ergonomics

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“…The results presented in this article could help corporate managers to realise the impact of human error on production costs as well as accidents and occupational health hazards in uncertain manufacturing environments. This paper contributes to a stream of research that emphasises integrating human aspects into operations models as called for by [29] and as exemplified by the integration of human aspects like biomechanical loading and fatigue into discrete event simulation [33,34], connecting learning and forgetting into mathematical models of Dual Resource Constrained (DRC) systems [35,36,37], modelling production costs in ways that include employee health hazards [38], and incorporating human aspects into industrial engineering design tools to support the production system design process [39]. This current study contributes to this agenda by providing further examples of novel approaches to integrating human aspects into engineering design and decision making tools that can support better design choices for both improved system safety and long term system performance.…”

Section: Discussionmentioning

confidence: 99%

Considering human error in optimizing production and corrective and preventive maintenance policies for manufacturing systems

Emami-Mehrgani

Neumann

Nadeau

et al. 2016

Applied Mathematical Modelling

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This article extends previous work on co-optimization of production and corrective and preventive maintenance including lockout/tagout. We study the impact of human error on repairable manufacturing systems subject to random failure over an infinite planning horizon and its implications for system capacity and inventory policies, and we derive an optimal policy for minimizing production cost based on machinery maintenance and inventory management while meeting market demand over an infinite horizon. A numerical example is provided to demonstrate the usefulness of the proposed approach and a sensitivity analysis is presented to confirm the efficiency of the control policy.

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“…Reported insufficiencies of existing endurance and recovery models (e.g. El ahrache, Imbeau, and Farbos 2006;Perez et al 2014;Rose et al 2014;Nussbaum 2015b, 2017) include: (i) Models use the time to regain maximum force generating capacity in defining the recovery time (e.g. Frey Law and Avin 2010), although no tentative relationship between the maximum force generating capacity and MSDs has been supported by any study results.…”

Section: Introductionmentioning

confidence: 97%

Modelling endurance and resumption times for repetitive one-hand pushing

2018

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This study's objective was to develop models of endurance time (ET), as a function of load level (LL), and of resumption time (RT) after loading as a function of both LL and loading time (LT) for repeated loadings. Ten male participants with experience in construction work each performed 15 different one-handed repetaed pushing tasks at shoulder height with varied exerted force and duration. These data were used to create regression models predicting ET and RT. It is concluded that power law relationships are most appropriate to use when modelling ET and RT. While the data the equations are based on are limited regarding number of participants, gender, postures, magnitude and type of exerted force, the paper suggests how this kind of modelling can be used in job design and in further research. Practitioner Summary: Adequate muscular recovery during work-shifts is important to create sustainable jobs. This paper describes mathematical modelling and presents models for endurance times and resumption times (an aspect of recovery need), based on data from an empirical study. The models can be used to help manage fatigue levels in job design.

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Discrete event simulation as an ergonomic tool to predict workload exposures during systems design

Cited by 56 publications

References 48 publications

Fatigue and recovery during and after static loading

Fatigue and recovery during and after static loading

Considering human error in optimizing production and corrective and preventive maintenance policies for manufacturing systems

Modelling endurance and resumption times for repetitive one-hand pushing

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