Joint production, maintenance and quality control chart optimization for a degrading manufacturing–reworking system with assignable causes

Kammoun, Mohamed Ali; Hajej, Zied; Dellagi, Sofiene; Mejri, Marwen; Bennour, Sami

doi:10.1007/s00170-023-12688-0

Cited by 2 publications

(2 citation statements)

References 49 publications

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“…According to Equations ( 21) and ( 22), among the seven orders, orders 1 and 2 are high-level orders, orders 3 and 4 are intermediate-level orders, and orders 5, 6 and 7 are basic-level orders. Among the 36 machines, machines 1, 2, 3,4,10,11,12,13,19,20,21,22,28,29,30 and 31 belong to the high-health group machine set, machines 5,6,7,14,15,16,23,24,25,32,33 and 34 belong to the medium-health group machine set, and machines 8, 9, 17, 18, 26, 27, 35 and 36 belong to the sub-health group machine set.…”

Section: Production Line Configuration and Random Cases Generation 1)...mentioning

confidence: 99%

“…They utilised the branch and bound algorithm to reduce machine maintenance costs. Motahare [11] designed a joint optimization method for maintenance, quality and buffer inventory strategies based on P control charts for the single-machine scheduling problem. They adopted periodic preventive maintenance (PM) operations to reduce the probability of machine failures.…”

Section: Introductionmentioning

confidence: 99%

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Research on joint scheduling method of order grading and machine maintenance

Zeng,

Li,

Jiang

et al. 2024

IET Collab Intel Manufact

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In the multi‐variety and large‐scale order production mode, enterprises must balance delivery deadlines and maintain customer satisfaction while also considering the health status of machines. Therefore, the authors propose a method for jointly optimising production scheduling and machine maintenance. Before machine processing, an order value grading and sorting model and a machine health‐status group partitioning model are constructed to classify orders into different production value levels and machines into different health‐status groups, respectively. During machine processing, based on the Weibull distribution theory, a ‘health evaluation function value’ constraint machine preventive maintenance (PM) model and PM strategy are proposed to account for the changing health status of machines; these are integrated with the order allocation machine strategy as decision‐making elements in the production schedule. Finally, two case studies are used to verify the effectiveness of this proposed model and method. The results show that compared to general scheduling schemes, the proposed method can reduce total delay and improve customer satisfaction. Additionally, the PM plan proposed in this method can improve production efficiency and line stability compared to periodic maintenance.

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