Productivity prediction in aircraft final assembly lines: Comparisons and insights in different productivity ranges

Long, Tengfei; Yuan, Li; Chen, Jun

doi:10.1016/j.jmsy.2021.12.010

Cited by 15 publications

(5 citation statements)

References 70 publications

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“…Equation ( 4) demonstrates that the sum of the number of MO and AO cannot exceed the upper limit number of workers in the workshop. Equation (5) shows that the number of workstations in area i is less than or equal to the maximum number of workstations limited in that area. Equation ( 6) means that the number of AO is non-negative.…”

Section: Metamodel-based Multi-response Optimization Methodsmentioning

confidence: 99%

“…Considering the resource factor level and the sample size comprehensively, a complete 3 5 factorial design with two centers is chosen to study the overall factor influence of the general assembly line. This would require a simulation running for a total of 243 different design points.…”

Section: Sensitivity Analysis and Doe Of Resource Factorsmentioning

confidence: 99%

“…But the time spent on iterative simulation becomes longer and longer as the complexity of the model increases [4]. Metamodel methods are favorited by scholars and practitioners for their rapidity and accuracy in solving resource allocation problems [5]. Digital Twin is also a popular method to solve production resource allocation problems [6].…”

Section: Introductionmentioning

confidence: 99%

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Order-Driven Dynamic Resource Configuration Based on a Metamodel for an Unbalanced Assembly Line

2022

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Resource-constrained product general assembly lines with complex processes face significant challenges in delivering orders on time. Accurate and efficient resources allocation of assembly lines remain a critical factor for punctual order delivery, full use of resources and associated customer satisfaction in complex production systems. In order to quickly solve the order-based dynamic resource allocation problem, in this paper a metamodel-based, multi-response optimization method is proposed for a complex product assembly line, which has the characteristics of order-based production, long working time of processes, multiple work area re-entry and restricted operator quantity. Considering the complexity of the assembly line and the uncertainty of orders, the correlation between system performance indicators and resource parameters is investigated. Multiple metamodels are constructed by the Response Surface Methodology to predict and optimize the system performance. The adequacy of the constructed metamodels is verified and validated based on the bootstrap resampling method. Under the condition of ensuring the throughput demand of the assembly line, the desirability function is applied to simultaneously optimize the multi-response, and the resource allocation solution is generated. The method in this paper can be used to rapidly adjust the resource configuration of the assembly line when considering the order changes.

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Section: Metamodel-based Multi-response Optimization Methodsmentioning

confidence: 99%

Section: Sensitivity Analysis and Doe Of Resource Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Order-Driven Dynamic Resource Configuration Based on a Metamodel for an Unbalanced Assembly Line

2022

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“…Anomaly detection [54] Root cause analysis of production disturbances or casual-relationship discovery [55] Production performance prediction [56], Human behavior control [57] Predictive scheduling [58], Adaptive production control [59] Figure 1. Four-Level and Four-Know categorization of ML applications.…”

Section: Systemmentioning

confidence: 99%

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

et al. 2023

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While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments.

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“…Este aspecto tiene especial incidencia en algunas empresas como las pertenecientes al sector de las confecciones [1], donde las técnicas basadas en inteligencia artificial permiten lograr resultados bastante buenos. Igualmente, en algunas otras empresas [2] como las aeronáuticas los modelos predictivos, de simulación y aprendizaje automático han permitido optimizar su sistema de producción. En este mismo sentido, estas técnicas pueden ser usadas en campos tan diversos como la predicción de la productividad de petrolero [3].…”

Section: Introductionunclassified

Data Mining in Labor Productivity

Gómez¹,

Marin²,

Rosero³

2022

Proceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research A

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The fundamental objective of this article is to determine, through data mining analysis, the most influential variables in the labor productivity of a person. Parting from a previously designed database and with use of the Weka platform, it is determined through a statistical selection process, with an effectiveness greater than 82.15% , the most important variables. They are: Incentives, standard minute value, production target and number of workers.

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Productivity prediction in aircraft final assembly lines: Comparisons and insights in different productivity ranges

Cited by 15 publications

References 70 publications

Order-Driven Dynamic Resource Configuration Based on a Metamodel for an Unbalanced Assembly Line

Order-Driven Dynamic Resource Configuration Based on a Metamodel for an Unbalanced Assembly Line

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

Data Mining in Labor Productivity

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