Research Papers

Simultaneous Determination of Disassembly Sequence and Disassembly-to-Order Decisions Using Simulation Optimization

[+] Author and Article Information
Mehmet Ali Ilgin

Department of Industrial Engineering,
Celal Bayar University,
Muradiye Campus,
Manisa 45140, Turkey
e-mail: mehmetali.ilgin@cbu.edu.tr

Gökçeçiçek Tuna Taşoğlu

Department of Industrial Engineering,
Celal Bayar University,
Muradiye Campus,
Manisa 45140, Turkey
e-mail: gokcecicek.tuna@gmail.com

1Corresponding author.

Manuscript received November 26, 2015; final manuscript received May 4, 2016; published online June 22, 2016. Assoc. Editor: Karl R. Haapala.

J. Manuf. Sci. Eng 138(10), 101012 (Jun 22, 2016) (8 pages) Paper No: MANU-15-1609; doi: 10.1115/1.4033603 History: Received November 26, 2015; Revised May 04, 2016

Strict environmental regulations and increasing public awareness toward environmental issues force many companies to establish dedicated facilities for product recovery. All product recovery options require some level of disassembly. That is why, the cost-effective management of product recovery operations highly depends on the effective planning of disassembly operations. There are two crucial issues common to most disassembly systems. The first issue is disassembly sequencing which involves the determination of an optimal or near optimal disassembly sequence. The second issue is disassembly-to-order (DTO) problem which involves the determination of the number of end of life (EOL) products to process to fulfill the demand for specified numbers of components and materials. Although disassembly sequencing decisions directly affects the various costs associated with a disassembly-to-order problem, these two issues are treated separately in the literature. In this study, a genetic algorithm (GA) based simulation optimization approach was proposed for the simultaneous determination of disassembly sequence and disassembly-to-order decisions. The applicability of the proposed approach was illustrated by providing a numerical example and the best values of GA parameters were identified by carrying out a Taguchi experimental design.

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Fig. 1

Steps of the proposed methodology

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Fig. 2

Precedence relationships for the product

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Fig. 3

Flow chart of the activities carried at the station disassembling component D

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Fig. 4

Flow chart of the demand process of component D (Type 1)

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Fig. 5

Structure of a chromosome

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Fig. 6

Main effects plot for S/N ratios

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Fig. 7

Convergence graph of GA

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Fig. 8

The converged GA solution




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