Integrated maintenance and production decision for<i>k</i>-out-of-<i>n</i>system equipment with attenuation of product quality

Shao, Xiaofeng; Chen, Zhixiang; Sarker, Bhaba R.

doi:10.1108/ijqrm-07-2017-0138

Cited by 8 publications

(5 citation statements)

References 35 publications

(33 reference statements)

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“…For the 2018–19 harvest season, machine availability has shown a decrease of 51.02% from 0.49 to 0.24 in the years 2017–2019 which found that the sugarcane harvester machine will possess high downtimes and unscheduled hours due to operation-stopping breakdowns. However, the machine availability reduction would have greatly decreased the operating performance and production capacity (Du, 2008; Xiao et al , 2019).…”

Section: Resultsmentioning

confidence: 99%

Preventive maintenance optimization of sugarcane harvester machine based on FT-Bayesian network reliability

Afsharnia¹,

Marzban²,

Asoodar³

et al. 2020

IJQRM

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PurposeThe purpose of this paper is to optimize the preventive maintenance based on fault tree (FT)–Bayesian network (BN) reliability for sugarcane harvester machine as a fundamental machine in the sugar industry that must be operated failure-free during a given period of the harvesting process.Design/methodology/approachTo determine machine reliability using the algorithm developed based on mapping FTs into BNs, the common failures of 168 machines were carefully investigated over 12 years (2007–2019). This algorithm was then used to predict the harvester reliability, estimate delays by machine downtimes and their consequences on white sugar production losses that can be reduced by optimizing the preventive maintenance scheduling.FindingsThe optimization of preventive maintenance scheduling based on estimated reliability of sugarcane harvester machines using FT–BNs can reduce white sugar production losses, the operation-stopping breakdowns and the downtime costs as a crisis that the sugar industry is facing.Practical implicationsMachine reliability gradually decreased by 31.08% approximately, which resulted in a working time loss of 26% in the 2018–19 harvesting season. In total, the white sugar losses were estimated as 204.17 tons for burnt canes and 114.53 tons for green canes. The losses of the 2018–19 harvesting season have been 11.85 times greater than the first harvesting season. The proposed maintenance interval for critical subsystems including the hydraulic, chopper and base cutter were obtained as 1.815, 1.12 and 1.05 h, respectively.Originality/valueIn this study, a new approach was used to optimize preventive maintenance to reduce delays and their implications upon costs in time, inconvenience and white sugar losses. The FT–BNs algorithm was found a useful tool that was over-fitting of failure occurrence probabilities data for sugarcane harvester machine.

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Section: Resultsmentioning

confidence: 99%

Preventive maintenance optimization of sugarcane harvester machine based on FT-Bayesian network reliability

Afsharnia¹,

Marzban²,

Asoodar³

et al. 2020

IJQRM

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show abstract

“…Equipment reliability has a significant impact on efficiency, so to attain, a decision about maintenance and production must be taken simultaneously (Xiao, 2019). Such costs have been analyzed, such as maintenance, adjustment, inventory holding, shortage, production, quality with demand uncertainty and quality assurance of products resulting from equipment breakdown.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This paper has presented an integrated decision Dynamic production and maintenance planning model coordinating the maintenance of the predictive decision based on predictive information with the timing decisions of a machine to minimize the total expected cost. Equipment reliability has a significant impact on efficiency, so to attain, a decision about maintenance and production must be taken simultaneously (Xiao, 2019). Such costs have been analyzed, such as maintenance, adjustment, inventory holding, shortage, production, quality with demand uncertainty and quality assurance of products resulting from equipment breakdown.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated mathematical model of dynamic production and maintenance planning in pumped-storage hydroelectricity

Asgari

Jolai

Movahedisobhani

2021

JM2

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Purpose Pumped-storage hydroelectricity (PSH) is considered as an effective method to moderate the difference in demand and supply of electricity. This study aims to understanding of the high capacity of energy production, storage and permanent exploitation has been the prominent feature of pumped-storage hydroelectricity. Design/methodology/approach In this paper, the optimization of energy production and maintenance costs in one of the large Iranian PSH has been discussed. Hence, a mathematical model mixed integer nonlinear programming developed in this area. Minimizing the difference in supply and demand in the energy production network to multiple energies has been exploited to optimal attainment scheme. To evaluate the model, exact solution CPLEX and to solve the proposed programming model, the efficient metaheuristics are utilized by the tuned parameters achieved from the Taguchi approach. Further analysis of the parameters of the problem is conducted to verify the model behavior in various test problems. Findings The results of this paper have shown that the meta-heuristic algorithm has been done in a suitable time, despite the approximation of the optimal answer, and the consequences of research indicate that the model proposed in the studied power plant is applicable. Originality/value In pumped-storage hydroelectricity plants, one of the main challenges in energy production issues is the development of production, maintenance and repair scheduling concepts that improves plant efficiency. To evaluate the mathematical model presented, exact solution CPLEX and to solve the proposed bi-objective mixed-integer linear programming model, set of efficient metaheuristics are used. Therefore, according to the level of optimization performed in the case study, it has caused the improvement of planning by 7%–12% and effective optimization processes.

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“…Yaghoubi et al [ 11 ] introduced quantitative tools using the Markov method and improved the quality of the oil product line. The Markov model applications also include battery production systems [ 12 ], machine tool modeling [ 13 ], k-out-of-n systems [ 14 , 15 ], reconfigurable production systems [ 16 ], maintenance policy [ 17 ], re-entrant production systems [ 18 ], primary care [ 19 ], rolling bearing monitoring [ 20 ], industrial network models [ 21 ], assembly assistance systems [ 22 ], laser-based manufacturing process [ 23 ], and the steel production process [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Analytical Modeling Approach for Quality Propagation of Transient Analysis of Serial Production Systems

Liu

et al. 2022

Sensors

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Production system modeling (PSM) for quality propagation involves mapping the principles between components and systems. While most existing studies focus on the steady-state analysis, the transient quality analysis remains largely unexplored. It is of significance to fully understand quality propagation, especially during transients, to shorten product changeover time, decrease quality loss, and improve quality. In this paper, a novel analytical PSM approach is established based on the Markov model, to explore product quality propagation for transient analysis of serial multi-stage production systems. The cascade property for quality propagation among correlated sequential stages was investigated, taking into account both the status of the current stage and the quality of the outputs from upstream stages. Closed-form formulae to evaluate transient quality performances of multi-stage systems were formulated, including the dynamics of system quality, settling time, and quality loss. An iterative procedure utilizing the aggregation technique is presented to approximate transient quality performance with computational efficiency and high accuracy. Moreover, system theoretic properties of quality measures were analyzed and the quality bottleneck identification method was investigated. In the case study, the modeling error was 0.36% and the calculation could clearly track system dynamics; quality bottleneck was identified to decrease the quality loss and facilitate continuous improvement. The experimental results illustrate the applicability of the proposed PSM approach.

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Integrated maintenance and production decision fork-out-of-nsystem equipment with attenuation of product quality

Cited by 8 publications

References 35 publications

Preventive maintenance optimization of sugarcane harvester machine based on FT-Bayesian network reliability

Preventive maintenance optimization of sugarcane harvester machine based on FT-Bayesian network reliability

An integrated mathematical model of dynamic production and maintenance planning in pumped-storage hydroelectricity

A Novel Analytical Modeling Approach for Quality Propagation of Transient Analysis of Serial Production Systems

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