Entropic evaluation of assemblability in concurrent approach to assembly planning

Fujimoto, Hideo; Ahmed, Abusabah I. A.

doi:10.1109/isatp.2001.929040

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…Notable example use of entropy include Zeltzer et al [15] who proposed an entropic complexity leveling method based on the variation of task cycle time. Similarly, Fujimoto and Ahmed [16] proposed an entropic complexity measure for different stages of process planning, while ElMaraghy et al [17] demonstrate how the entropy function can be used in the quantification of complexity in machining process. While these efforts are worth lauding, the aforementioned studies of complexity measure pay little attention to the operator's choice complexity, its relation to variety, and how it impacts performance in a mixed model assembly line.…”

Section: Background a Manufacturing Complexitymentioning

confidence: 99%

“…Assuming that a company has an obligation to comply with the demand, the scheduling problem of the assembly of weatherstripping that aims to minimize the complexity of the system, can be formulated using (11) - (16). In this example, the demand for car is fixed while the proportion of the car (in the demand) containing a given weatherstripping type is either i) random, provided that each type is represented or ii) uniform; i.e., all types of weatherstripping are demanded on equal (or almost equal) proportion.…”

Section: Illustrative Example a Product Sequencing 1) Weatherstrimentioning

confidence: 99%

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Sequence Based Optimization of Manufacturing Complexity in a Mixed Model Assembly Line

et al. 2019

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Increasing production variability while maintaining operation efficiency remains a critical issue in many manufacturing industries. While the adoption of mixed-model assembly lines enables the production of high product variety, it also makes the system more complex as variety increases. This paper proposes an information entropy-based methodology that quantifies and then minimizes the complexity through product sequencing. The theory feasibility is demonstrated in a series of simulations to showcase the impact of sequencing in controlling the system predictability and complexity. Hence, the framework not only serves as a tool to quantitatively assess the impact of complexity on total system performance but also provides means and insights into how complexity can be mitigated without affecting the overall manufacturing workload.

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Section: Background a Manufacturing Complexitymentioning

confidence: 99%

Section: Illustrative Example a Product Sequencing 1) Weatherstrimentioning

confidence: 99%

Sequence Based Optimization of Manufacturing Complexity in a Mixed Model Assembly Line

et al. 2019

View full text Add to dashboard Cite

show abstract

“…For example, Nakazawa and Suh (1984) and Suh (1990) measured the process complexity in terms of the amount of efforts needed to complete a product, where the effort was quantified based on the product requirement on geometric precision and surface quality. From an assembly operation point of view, Fujimoto and Ahmed (2001) used the concept of entropy by measuring the amount of the uncertainty in key assembly operations, such as gripping, positioning and inserting parts. From a product point of view, Fujimoto et al (2003) defined a measure of complexity based on the product structure and also showed that the impact of product variety could be reduced if the complexity of the product structure was reduced.…”

Section: Introductionmentioning

confidence: 99%

Complexity metrics for mixed model manufacturing systems based on information entropy

Abad

Jin

2011

IJIDS

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Mixed model manufacturing systems are increasingly used to meet global competition by providing a broad variety of products to customers. The increase of product variety adds more complexity to production processes, thus, leading to a negative effect on the performance of production processes. Therefore, it is of great interest to effectively measure such complexity and to quantify its effect on manufacturing system performance. In this paper, a set of complexity metrics are proposed for measuring the complexity of different elements in a manufacturing system. These metrics were defined by constructing a linkage with the communication system's framework. Different from those existing complexity measures defined in the literature, this paper considers production quality into the measure of the process capability on handling the complexity induced by the input demand variety. Examples are given in the paper to discuss different properties of the defined metrics and their potential applications.

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“…According to Calinescu et al [7], all the information required for assessing the static entropy model is available from the production order and process plans for individual parts. Fujimoto and Ahmed [8] defined a complexity index for assembling. The index takes the form of entropy in evaluating the assemblability of a product.…”

Section: Introductionmentioning

confidence: 99%

Complexity of cellular manufacturing systems based on entropy models

Zhang

2010

J Mech Sci Technol

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Even structurally simple manufacturing systems can be operationally complex. This operational complexity can be colloquially defined as the uncertainty associated with managing the dynamic variations, in time or quantity, across information and material flows at the manufacturing systems interface. This paper proposes a means of measuring the information demands placed on cellular manufacturing systems, as a result of this uncertainty. A utility function for complexity is proposed according to the relationships between the complexity and utility in a manufacturing system and the underlying trend that the system becomes more and more complex in an everchanging environment is analyzed. This paper mathematically models the static entropy and the dynamic entropy of cellular manufacturing systems from an information-theoretic perspective. A unique feature of this measure is that it captures, in relative terms, the expected amount of information required to describe the state of the system. The measure provides flexibility in the scope and detail of analysis. Finally, an example is used to demonstrate the validity of the proposed methodology.

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Entropic evaluation of assemblability in concurrent approach to assembly planning

Cited by 7 publications

References 5 publications

Sequence Based Optimization of Manufacturing Complexity in a Mixed Model Assembly Line

Sequence Based Optimization of Manufacturing Complexity in a Mixed Model Assembly Line

Complexity metrics for mixed model manufacturing systems based on information entropy

Complexity of cellular manufacturing systems based on entropy models

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