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

Oleh:

Hegui, Ye ; Ming, Liang

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Journal of Manufacturing Science and Engineering vol. 128 no. 4 (Nov. 2006), page 984-995.
Topik: genetic algorithms; modular product; scheduling; manufacturing; cell reconfiguration; genetic algorithm

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: JJ93.9 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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