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Research Papers

Manufacturing System Design Considering Stochastic Product Evolution and Task Recurrence

[+] Author and Article Information
Jeonghan Ko

Industrial and Management Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588jko2@unl.edu

S. Jack Hu

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109jackhu@umich.edu

J. Manuf. Sci. Eng 131(5), 051012 (Sep 24, 2009) (12 pages) doi:10.1115/1.4000095 History: Received December 13, 2007; Revised June 04, 2009; Published September 24, 2009

The conventional approaches to manufacturing system design and line balancing have often focused on a single generation of products, thus leading to new design or rebalancing when new products are introduced or different models are produced in the same line. As the life cycles of product models become shorter and shorter, this new product then new system-design approach is becoming increasingly ineffective due to too frequent production interruption. Therefore, effective solutions to system-design problems should consider the evolution of products over multiple generations and models, and this paper presents such new methods. Mixed-integer programming models are developed for (1) designing manufacturing system configurations that are cost effective for product evolution involving uncertainty and (2) maximizing the recurrences of manufacturing tasks on the same machines throughout product evolution. A decomposition-based solution procedure is also developed to reduce computational complexity. These new methods can provide a stable system-design solution enabling quick product launches with less line change-over for new or different products.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Types of manufacturing system configurations. Squares represent machines, whereas arrows and lines represent material flows between machines. (a) Simple serial-line configuration, (b) serial line with parallel-machine configuration, (c) U-shaped configuration, and (d) complex asymmetric configuration. Adapted from Ref. 21.

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Figure 2

Product families and product evolution scenarios. Nodes (circles with double lines) represent product families. An arrow represents a product change from one product family to another product family.

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Figure 3

Decomposition procedure for the optimization problem

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Figure 4

Consumer electronics for the numerical study

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Figure 5

Product evolution. A path with thick arrows represents Scenario 4.

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Figure 6

Modified combined precedence graph of the toaster assembly tasks

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Figure 7

Feasible configurations. (a) Case 1, (b) Case 2, and (c) Case 3

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Figure 8

A configuration calculated without task-recurrence index

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