Integrating Reconfiguration Cost Into the Design of Multi-Period Scalable Reconfigurable Manufacturing Systems

[+] Author and Article Information
Patrick Spicer

Department of Mechanical Engineering,  The University of Michigan, 2250 GG Brown Building, 2350 Hayward Street, Ann Arbor, MI 48109-2125pspicer@umich.edu

Hector J. Carlo

Department of Mechanical Engineering,  The University of Michigan, 2250 GG Brown Building, 2350 Hayward Street, Ann Arbor, MI 48109-2125hcarlo@umich.edu

J. Manuf. Sci. Eng 129(1), 202-210 (Aug 15, 2006) (9 pages) doi:10.1115/1.2383196 History: Received September 15, 2005; Revised August 15, 2006

A reconfigurable manufacturing system (RMS) that is designed specifically to adapt to changes in production capacity, through system reconfiguration, is called a scalable-RMS. The set of system configurations that a scalable-RMS assumes as it changes over time is called its configuration path. This paper investigates how to determine the optimal configuration path of a scalable-RMS that minimizes investment and reconfiguration costs over a finite horizon with known demand. First, a practical cost model is presented to compute the reconfiguration cost between two scalable-RMS configurations. This model comprehends labor costs, lost capacity costs, and investment/salvage costs due to system reconfiguration and ramp up. Second, the paper presents an optimal solution model for the multiperiod scalable-RMS using dynamic programming (DP). Third, a combined integer programming/dynamic programming (IP-DP) heuristic is presented that allows the user to control the number of system configurations considered by the DP in order to reduce the solution time while still providing a reasonable solution. Numerical problems involving a two-stage and a three-stage scalable-RMS are solved using the DP and IP-DP methodologies. Experimental results suggest that the DP approach, although it is optimal, is not computationally efficient for large problem sizes. However, the combined IP-DP approach offers reasonable results with much less computational effort.

Copyright © 2007 by American Society of Mechanical Engineers
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Figure 1

(a) Multispindle scalable-RMT, (b) multiCNC scalable-RMT

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Figure 2

Example system architecture for a scalable-RMS in a single demand period

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Figure 3

An example of a process stage reconfiguration from period k to k+1

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Figure 4

Reconfiguration cost calculation

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Figure 5

Phases of system operation (scaling up)

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Figure 6

Example system configuration during reconfiguration

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Figure 7

An example of a dynamic programming network (arc costs not shown)

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Figure 8

Partitions (stage configurations) with M=15, B=7, and n=4



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