Jobs scheduling within Industry 4.0 with consideration of worker’s fatigue and reliability using Greedy Randomized Adaptive Search Procedure

Mouayni, Ismail El; Demesure, Guillaume; Haouzi, Hind Bril-El; Charpentier, Patrick; Siadat, Ali

doi:10.1016/j.ifacol.2019.12.114

Cited by 13 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It maximizes late tasks at one site to earn revenue. The author adapted this approach for m machines [50]. The janitor cleans each floor's toilets using scheduling.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling and Predictive Maintenance for Smart Toilet

2023

View full text Add to dashboard Cite

Modern society needs bathrooms. Poor sanitation is caused by worn-out appliances and expensive cleaning. The technique also requires an inexpensive, dependable sensor. This study had three goals. Creating an IoT administration platform is the main goal. Literature evaluations assess the merits and downsides of existing systems. Second, we suggest predictive maintenance to assist predict bathroom equipment breakdowns. Finally, a scheduling algorithm was used to determine how many janitors to hire. We'll measure the model's effectiveness and make future recommendations. Infrared, temperature and humidity sensors create an IoT bathroom. Sensors have been studied to understand how to adapt them to the hygienic and private toilet environment. Sensor accuracy and cost-effectiveness could be enhanced with more development and testing. The Auto-Regressive Integrated Moving Average (ARIMA) model accurately predicts time series lags, making it a good candidate for predictive maintenance. Long Short-Term Memory (LSTM) is good in time series predictions, therefore it's fair to compare the two. We use the ARIMA model to handle Remaining Useful Life (RUL) prediction techniques by altering Moving Average (MA) and Auto-Regressive (AR). A genetic algorithm is used to create a janitorial cleaning schedule. The genetic algorithm was proposed to schedule cleaning workers. This approach improves the genetic algorithm by studying soft and hard scheduling restrictions. The Greedy algorithm is used to compare. Experimental evaluations reveal that the suggested model ARIGA meets both goals.

show abstract

“…It maximizes late tasks at one site to earn revenue. The author adapted this approach for m machines [50]. The janitor cleans each floor's toilets using scheduling.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling and Predictive Maintenance for Smart Toilet

2023

View full text Add to dashboard Cite

show abstract

“…Personalization of a work system to human characteristics, preferences, and behaviour is discussed in (Cohen et al 2018) within a framework for human-machine interaction at the workstation level. On a work organization level adaptations due to worker's fatigue and reliability (El Mouayni et al 2019), different performance levels and human limitations (d'Avella/Tripicchio 2020) are considered. User-centric and self-configured workstations that adapt to anthropometric characteristics of individual workers are described on a framework level in (Bortolini et al 2017), regarding specific personalization options in (Rupprecht/Schlund 2021), and by the example of an assembly system for aircraft parts in (Mayrhofer et al 2019).…”

Section: Research Backgroundmentioning

confidence: 99%

Towards Designing Adaptive and Personalized Work Systems in Manufacturing

Schlund¹,

Kostolani²

2022

Digitization of the Work Environment for Sustainable Production

View full text Add to dashboard Cite

Adaptation of work to humans instead of humans constantly adapting to work systems and work environments has been a goal of ergonomics since the 19th century. To date, an actual adaptation of work to the specific features, conditions and requirements of individual users has so far only taken place in rudimentary form. The possibilities have now opened up by the advances of Cyber Physical Production Systems (CPPS). This enables a renewed attempt to implement the goal of adapting work systems to workers. Integration of advanced sensory skills into work systems within manufacturing allows better recognition of the work environment and the actual state of the system. Together with actuator capabilities, it enables adaptive work systems to adjust their structure and behaviour to the anthropometric and the cognitive features and requirements of the systems’ users. However, a framework that integrates the concepts of adaptivity and personalization into the work system model and work system design is still missing. This paper explores the roots of adaptive and personalized human-machine systems and shows a framework for integrating these concepts into work systems and manufacturing. Based on this, exemplary works and implementations of individual systems are presented and placed in the overall context. The focus is set on implementations within the TU Wien Pilot Factory that cover adaptability concepts on workplace, process, and system level.

show abstract

“…Moreover, it is suggested that the change induced by adopting Industry 4.0 technologies is not ordinary but rather a radical change in culture, structure and operations of organizations [139,[141][142][143]. Thus, only organizations which develop a nascent capability of agility will be better off by adopting Industry 4.0 technologies [107,[144][145][146]. Therefore, it can be concluded that organizations that have agility will be able to adopt Industry 4.0 technologies, and once adopted, the capability of agility will become highly dynamic [144,147,148].…”

Section: Aspect Of Agility In Industry 40mentioning

confidence: 99%

Organizational Agility in Industry 4.0: A Systematic Literature Review

Mrugalska

Ahmed

2021

Sustainability

View full text Add to dashboard Cite

Agility is the dynamic capability of an organization which helps it to manage a change and uncertainties in the environment. The purpose of this research is to review the literature from the perspective of agility in Industry 4.0. This paper systematically reviews 381 relevant articles from peer-reviewed academic journals in the period of the last five years. The results show that agility is important for an organization to adopt Industry 4.0 technologies as it helps companies to cope with the changes that arise along with the adoption of Industry 4.0 technologies. Further, it also indicates that by adopting Industry 4.0 technologies, companies can significantly enhance their agility capability into various aspects with different technologies. The technologies which enhance the agility are: smart manufacturing, internet of things, cyber-physical system, big data and analytics and cloud computing. On the other hand, important aspects of agility include supply chain, workforce, information system, facilities, management, manufacturing and technology agility.

show abstract

Jobs scheduling within Industry 4.0 with consideration of worker’s fatigue and reliability using Greedy Randomized Adaptive Search Procedure

Cited by 13 publications

References 23 publications

Scheduling and Predictive Maintenance for Smart Toilet

Scheduling and Predictive Maintenance for Smart Toilet

Towards Designing Adaptive and Personalized Work Systems in Manufacturing

Organizational Agility in Industry 4.0: A Systematic Literature Review

Contact Info

Product

Resources

About