An assembly timing planning method based on knowledge and mixed integer linear programming

Qian, Jiahui; Zhang, Zhijing; Shi, Lingling; Song, Dan

doi:10.1007/s10845-021-01819-7

Cited by 9 publications

(5 citation statements)

References 38 publications

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“…Fourth, we explicitly omitted sources of interference in the representations of the assembly workplace with IAS to investigate its pure effect on MWC. However, interferences, such as time delays or modeling errors (Tao et al, 2022 ; Xu et al, 2021 ), can be expected to occur in operational practice in assembly process planning (Qian et al, 2023 ) when working with IAS, which could alter the experiences of workers with such systems. Depending on whether assembly workers need to fix certain malfunctions on their own, knowledge characteristics could increase by using IAS, for example, because programming skills are needed to fix interferences but are not necessary for the majority of daily assembly processes.…”

Section: Discussionmentioning

confidence: 99%

Investigating the effect of intelligent assistance systems on motivational work characteristics in assembly

Walczok¹,

Bipp²

2023

J Intell Manuf

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Intelligent assistance systems (IAS) are designed to counteract rising cognitive demands caused by increasingly individualized manufacturing processes in assembly. How IAS affect work characteristics which are crucial for promoting work motivation of employees is yet unclear. Based on the cyber-physical systems transformation framework, the model of routine-biased technological change, and a comprehensive model of work design, we expected in- and decreases in motivational work characteristics (MWC) when working with IAS. Furthermore, we posited a buffering effect of the option of voluntary use on decreasing knowledge characteristics. Applying an online case study with experimental vignette methodology (EVM) allowed us to identify effects of the IAS on MWC before it is widely implemented. 203 German and British blue-collar workers evaluated an assembly workplace according to three experimental conditions (work without IAS, work with IAS, work with voluntary use of IAS). We identified enhanced feedback from the job and information processing in work with IAS in contrast to a traditional assembly workplace but found no restrictions (or elevations) in terms of other task (i.e., autonomy) or knowledge characteristics (i.e., job complexity, problem solving, specialization, skill variety). Thus, our results indicate that the IAS improves some motivational work characteristics of the assembly workplace, although it misses the primary goal of cognitive relief. Our study highlights the need for work design theories that specify the effect of IAS on motivational work characteristics and the potential benefit of IAS in assembly of the future.

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Section: Discussionmentioning

confidence: 99%

Investigating the effect of intelligent assistance systems on motivational work characteristics in assembly

Walczok¹,

Bipp²

2023

J Intell Manuf

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“…Automation and optimization approach: Zheng et al [82] developed an automated robot programming system enhanced by knowledge-driven methodologies for efficient code generation. Qian et al [83] fused knowledge bases with optimization techniques to streamline assembly scheduling. Yang et al [84] utilized CAD data to inform ontology models for intelligent assembly planning.…”

Section: Build Different Granularity Informationmentioning

confidence: 99%

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

Wu,

Liang

2024

Machines

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The evolution of knowledge acquisition and representation in manufacturing technologies is vital for translating complex manufacturing data into actionable insights and advancing a comprehensive knowledge framework. This framework is pivotal in driving innovation and efficiency in intelligent manufacturing. This review aggregates recent research on knowledge acquisition and representation within the manufacturing process, addressing existing challenges and mapping potential future developments. It includes an analysis of 123 papers that focus on harnessing advanced intelligent analytics to extract operationally relevant knowledge from the extensive datasets typical in manufacturing environments. The narrative then examines the methodologies for constructing models of knowledge in manufacturing processes and explores their applications in manufacturing principles, design, management, and decision-making. This paper highlights the limitations of current technologies and projects emerging research avenues in the acquisition and representation of process knowledge within intelligent manufacturing systems, with the objective of informing future technological breakthroughs.

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“…For intelligent assembly process planning, grouping optimization is a method has been employed previously. Qian et al [48] constructed an automatic assembly sy by dividing the assembly planning into within-group planning and between-group ning. In this case, there are 34 prefabricated wall components with varying thickn Considering that components' building or structural functions vary according to thickness, components may be delivered to the site in batches according to their thick So this paper employed an overall optimization method and a grouping optimiz method, dividing 34 wall components into three subgroups according to their thick We assume that the sequence between the subgroups is as follows: the 270 mm subg is first, followed by the 200 mm subgroup, and lastly, the 100 mm subgroup.…”

Section: Case Studymentioning

confidence: 99%

“…The second set of parameters is the genetic For intelligent assembly process planning, grouping optimization is a method that has been employed previously. Qian et al [48] constructed an automatic assembly system by dividing the assembly planning into within-group planning and between-group planning. In this case, there are 34 prefabricated wall components with varying thicknesses.…”

Section: Realization Of Optimizationmentioning

confidence: 99%

Graph Database and Matrix-Based Intelligent Generation of the Assembly Sequence of Prefabricated Building Components

Yang,

Jiang,

Dong

et al. 2023

Applied Sciences

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The assembly of prefabricated components is a critical process in prefabricated building construction, influencing both progress and accuracy. However, the assembly sequence planning and optimization (ASPO) of prefabricated components have yet to receive sufficient attention from researchers, and current research has displayed limited automation and poor generalization capabilities. Therefore, this paper proposes a framework for intelligently generating assembly sequences for prefabricated components based on graph databases and matrices. The framework utilizes an adjacency matrix and interference matrix-based modeling method to comprehensively describe the connections and constraint relationships between components, enabling better evaluation of assembly difficulty during optimization. The graph database serves as the central hub for data exchange, facilitating component information storage, automatic querying, and summarization. The obtained assembly sequence and progress plan are fed back into the graph database. To accomplish assembly sequence optimization, a genetic algorithm based on the double-elite strategy is employed. Furthermore, the effectiveness of the proposed framework is validated through an actual engineering case. The results demonstrate that the framework can effectively find an optimal assembly sequence to mitigate the assembly challenge of a prefabricated building.

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An assembly timing planning method based on knowledge and mixed integer linear programming

Cited by 9 publications

References 38 publications

Investigating the effect of intelligent assistance systems on motivational work characteristics in assembly

Investigating the effect of intelligent assistance systems on motivational work characteristics in assembly

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

Graph Database and Matrix-Based Intelligent Generation of the Assembly Sequence of Prefabricated Building Components

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