Simultaneous Modular Product Scheduling and Manufacturing Cell Reconfiguration Using a Genetic Algorithm

[+] Author and Article Information
Hegui Ye

 Marcotte Mining Machinery Services Inc., Sudbury, ON P3A 2A9, Canada

Ming Liang1

Department of Mechanical Engineering, University of Ottawa, 770 King Edward Avenue, Ottawa, ON K1N 6N5, Canadalinag@eng.uottawa.ca


Author to whom correspondence should be addressed.

J. Manuf. Sci. Eng 128(4), 984-995 (Jun 22, 2006) (12 pages) doi:10.1115/1.2336261 History: Received February 16, 2006; Revised June 22, 2006

Modular product design can facilitate the diversification of product variety at a low cost. Reconfigurable manufacturing, if planned properly, is able to deliver high productivity and quick responsiveness to market changes. Together, the two could provide an unprecedented competitive edge to a manufacturing company. The production of a family of modular products in a reconfigurable manufacturing system often requires reorganizing the manufacturing system in such a way that each configuration corresponds to one product variant in the same family. The successful implementation of this strategy lies in proper scheduling of the modular product operations and optimal selection of a configuration for producing each product variant. These two issues are closely related and have a strong impact on each other. Nevertheless, they have often been treated separately, rendering inefficient, infeasible, and conflicting decisions. As such, an integrated model is developed to address the two problems simultaneously. The objective is to minimize the sum of the manufacturing cost components that are affected by the two planning decisions. These include reconfiguration cost, machine idle cost, material handling cost, and work-in-process cost incurred in producing a batch of product variants. Due to the combinatorial nature of the problem, a genetic algorithm (GA) is proposed to provide quick and near-optimal solutions. A case study is conducted using a steering column to illustrate the application of the integrated approach. Our computational experience shows that the proposed GA substantially outperforms a popular optimization software package, LINGO, in terms of both solution quality and computing efficiency.

Copyright © 2006 by American Society of Mechanical Engineers
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Figure 1

Cell configurations for three module instances in cell 2

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Figure 2

Job sequences in three manufacturing cells

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Figure 3

The chromosome coding scheme

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Figure 4

Crossover operation

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Figure 5

Mutation operation

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Figure 6

An exploded view of steering column and its bill of material

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Figure 7

Results of job sequences and selected configurations of the example problem

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Figure 8

Convergence processes of the test problems



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