Scheduling predictive maintenance in flow-shop

Varnier, Christophe; Zerhouni, Noureddine

doi:10.1109/phm.2012.6228964

Cited by 14 publications

(7 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers investigate integrated scheduling of production and maintenance with a single objective. 40 In this paper, parameters are adjusted according to features of the problem, and are set as follows.…”

Section: Numerical Examplementioning

confidence: 99%

Novel integrated scheduling of production and preventive maintenance for serial production systems with equipment degradations

Zhou

Shi

2019

IRATJ

View full text Add to dashboard Cite

This paper considers a joint optimization problem of production and preventive maintenance for a serial production system subject to equipment degradations constrains.The system processes different job types to meet different customer demands. First, a joint optimization problem domain of the system with equipment degradations is formally introduced. A mathematic programming model is established with an optimal bi-objective function of minimizing the system makespan and preventive maintenance cost. Together with introducing an artificial bee colony theory, a novel bi-objective artificial bee colony algorithm is developed to pursue short production time and low preventive maintenance cost. To guarantee the algorithm convergence performance, a sorting rule of non-dominated sorting genetic algorithm II (NSGAII) is introduced into the proposed algorithm. Local Tabu search technology and probability criteria are involved. Finally, numerous experiments are conducted to evaluate the performance of the proposed algorithm. In comparison with the well-known NSGAII, the proposed algorithm performs significantly better in terms of finding the spread compromise solutions.

show abstract

Section: Numerical Examplementioning

confidence: 99%

Novel integrated scheduling of production and preventive maintenance for serial production systems with equipment degradations

Zhou

Shi

2019

IRATJ

View full text Add to dashboard Cite

show abstract

“…This paper builds upon research into maintenance scheduling. Although some works treated failure predictions as a specified input parameter, , other works used degradation data to optimize operations and maintenance. ,, Research efforts have involved the use of mathematical optimization ,, and Markov chain modeling . Wiebe and co-workers used a degradation signal and a memoryless Markov chain to estimate the equipment failure probability for robust optimization of production and maintenance schedules.…”

Section: Introductionmentioning

confidence: 99%

“…This paper builds upon research into maintenance scheduling. Although some works treated failure predictions as a specified input parameter, 31,32 other works used degradation data to optimize operations and maintenance. 6,33,34 Research efforts have involved the use of mathematical optimization 4,28,29 and Markov chain modeling.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Prescriptive Maintenance: Failure Prediction Using Ensemble Support Vector Classification for Optimal Process and Maintenance Scheduling

Gordon

Burnak

Onel

et al. 2020

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Maintenance can improve the availability of aging production systems and prevent process safety incidents. However, because of system complexity, resource allocation is nontrivial. This research developed and applied a framework to obtain optimal future-failure aware and safety-conscious production and maintenance schedules. Ensembles of nonlinear support vector machine classification models were leveraged to predict the time and probability of future equipment failure from equipment condition data. Multiobjective optimization of expected profit and a safety metric was then used to determine optimal process and maintenance schedules. The results of this research were that the ensemble models had an average accuracy and an F1-score of 0.987, that the ensemble models were more accurate and sensitive than the individual classifiers by 3 percentage points, and that the Pareto-optimal process and maintenance schedules were obtained, providing alternative solutions to the decision maker. This research described optimal resource allocation to help improve safety and system effectiveness.

show abstract

“…Pan et al presented in [16], a mathematical programming formulation to minimize the maximum tardiness of a single machine by simultaneously scheduling production and predictive maintenance based on it's RUL. Varnier and Zerhouni in [17], solved the joint scheduling problem for a flow-shop workshop where the machines have two production modes that influences their degradation by optimizing the aggregated sum of makespan and maintenance delays. Fitouri et al [18], proposed a heuristic to solve the problem of job shop production and predictive maintenance scheduling for a job shop based on prognostic information.…”

Section: Introductionmentioning

confidence: 99%

Integrated Production and Predictive Maintenance Planning based on Prognostic Information

Bougacha

Varnier

Zerhouni

et al. 2019

2019 International Conference on Advanced Systems and Emergent Technologies (IC_ASET)

Self Cite

View full text Add to dashboard Cite

This paper address the problem of scheduling production and maintenance operation in predictive maintenance context. It proposes a contribution in the decision making phase of the prognostic and health management framework. The prognostics and decision processes are merged and an ant colony optimization approach for finding the sequence of decisions that optimizes the benefits of a production system is developed. A case study on a single machine composed of several components where machine can have several usage profiles. The results show that our approach surpasses classical condition based maintenance policy.

show abstract

Scheduling predictive maintenance in flow-shop

Cited by 14 publications

References 3 publications

Novel integrated scheduling of production and preventive maintenance for serial production systems with equipment degradations

Novel integrated scheduling of production and preventive maintenance for serial production systems with equipment degradations

Data-Driven Prescriptive Maintenance: Failure Prediction Using Ensemble Support Vector Classification for Optimal Process and Maintenance Scheduling

Integrated Production and Predictive Maintenance Planning based on Prognostic Information

Contact Info

Product

Resources

About