Research Papers

Modeling of Manufacturing Complexity in Mixed-Model Assembly Lines

[+] Author and Article Information
Xiaowei Zhu, S. Jack Hu, Yoram Koren

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109

Samuel P. Marin

Manufacturing Systems Research Lab, General Motors R&D Center, Warren, MI 48090

J. Manuf. Sci. Eng 130(5), 051013 (Aug 29, 2008) (10 pages) doi:10.1115/1.2953076 History: Received October 30, 2007; Revised February 25, 2008; Published August 29, 2008

Mixed-model assembly lines have been recognized as a major enabler to handle product variety. However, the assembly process becomes very complex when the number of product variants is high, which, in turn, may impact the system performance in terms of quality and productivity. This paper considers the variety induced manufacturing complexity in manual mixed-model assembly lines where operators have to make choices for various assembly activities. A complexity measure called “operator choice complexity” (OCC) is proposed to quantify human performance in making choices. The OCC takes an analytical form as an information-theoretic entropy measure of the average randomness in a choice process. Meanwhile, empirical evidences are provided to support the proposed complexity measure. Based on the OCC, models are developed to evaluate the complexity at each station and for the entire assembly line. Consequently, complexity can be minimized by making system design and operation decisions, such as error-proof strategies and assembly sequence planning.

Copyright © 2008 by American Society of Mechanical Engineers
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Figure 1

An illustration of a PFA and its MMAL

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Figure 2

Mean choice RT as (a) a nonlinear function of the number of stimulus-response alternatives (17) and (b) a linear function of stimulus information, or log2 of the number of alternatives (18), reprinted from Ref. 22

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Figure 3

Choice RT for three different ways of manipulating the stimulus information H, reprinted from Ref. 21, using data from Ref. 19

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Figure 4

Choices in sequential assembly activities at one station

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Figure 5

Complexity propagation scheme

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Figure 6

Complexity propagation of the example in Fig. 1

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Figure 7

Propagation of complexity at the system level in a multistage assembly system

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Figure 8

Incoming and outgoing complexity charts

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Figure 9

Possible configurations for mixed-model assembly systems. Mi’s are machines in the system (27).

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Figure 10

Differences in transfer complexity values for different assembly sequences: (a) Task i precedes j, which results in Cij; (b) task j precedes i, which results in Cji, while Cij and Cji are not equal




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