Research Papers

A Nongraphical Method to Determine the Optimum Disassembly Plan in Remanufacturing

[+] Author and Article Information
Niloufar Ghoreishi

Adjunct Professor of Engineering,
WUSTL/UMSL Joint Engineering Program,
Washington University in St. Louis,
1 Brooking Dr.,
St. Louis, MO 63130
e-mail: ng1@seas.wustl.edu

Mark J. Jakiela

Hunter Professor of Mechanical Design,
Mechanical Engineering and
Materials Science Department,
Washington University in St. Louis,
1 Brooking Dr.,
St. Louis, MO 63130
e-mail: mjj@seas.wustl.edu

Research Assistant Professor
of Biomedical Engineering,
Biomedical Engineering Department,
Washington University in St. Louis,
1 Brooking Dr.,
St. Louis, MO 63130
e-mail: nekouzadeh@wustl.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received January 30, 2012; final manuscript received October 20, 2012; published online December 10, 2012. Assoc. Editor: Bernard Yannou.

J. Mech. Des 135(2), 021002 (Dec 10, 2012) (13 pages) Paper No: MD-12-1079; doi: 10.1115/1.4023001 History: Received January 30, 2012; Revised October 20, 2012

Optimizing a disassembly process involves maximizing the number of disassembled valuable parts (cores) and minimizing the number of disassembly operations. Usually, some disassembly operations are in common among two or more cores, or sometimes removing a core requires prior removal of other cores (known as precedence relations); these correlations complicate the allocation of the disassembly cost to the cores. To overcome this complexity, the current optimization methods (decision trees) determine the optimum sequence of disassembly operations rather than the optimum set of cores to be disassembled. These methods become difficult to implement when the number of cores increases. In this paper, we developed a mechanized nongraphical approach to determine the optimum set of cores to be disassembled and their required disassembly operations based on the functionality statuses of the cores. This approach introduces a new characterization of the disassembly process and its precedence relations, and can be implemented conveniently using computer codes even when the product consists of many cores. The application of the method is explained with an example. Using this example, it was shown that the optimum disassembly can increase the net profit significantly compared with the complete disassembly.

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Grahic Jump Location
Fig. 1

Schematic representation of a disassembly line and its major segments

Grahic Jump Location
Fig. 2

The connection graph of a product consisting of five cores, a noncore part, and a base

Grahic Jump Location
Fig. 3

The assembly schematic of the computer studied in the practical example. Black circles represent the screw holes for connecting cores to the case, or the sockets for connecting the CPU to the motherboard and the fan to the CPU. Black lines on the motherboard represent the connection slots for the RAM, graphics card and sound card. The power supply is placed on the right side of the case with screws inserted from the left side. The power supply screws are hidden under the motherboard. The wire connections are neglected.

Grahic Jump Location
Fig. 4

The connection graph of the computer cores. Cores are presented as boxes. The lines connecting different cores represent physical connections and are labeled with their associated disassembly units. The case is a noncore part.

Grahic Jump Location
Fig. 5

Optimum disassembly plan and profit of disassembly as functions of rpC2 (repair cost of core 2). If repair cost exceeds $14.3 the optimum disassembly plan switches from disassembling cores 1, 2, 3, 4, and 6 to disassembling all the cores but core 2 (panel A). The profit decreases proportional to rpC2 up to $14.3 and stays constant afterward, as core 2 is no longer disassembled and repaired (panel B).

Grahic Jump Location
Fig. 6

Optimum disassembly plan and maximum profit of disassembly for different combinations of core 4 and core 9 repair costs. There are three optimum plans depending on the values of rpC4 and rpC9 (panel A). The profit of disassembly (panel B) reduces by increasing these repair costs in the green and orange regions (as marked in panel A) and stays constant in the blue region (as marked in panel A).

Grahic Jump Location
Fig. 7

Net profits of optimum and complete disassemblies (panel A) and their differences (panel B) in terms of the average recoverability, ar. Net profit of optimum disassembly (solid curve in panel A) decreases almost linearly by reducing the average recoverability at a lower rate compared with the complete disassembly (dashed line in panel A). The difference between the net profits of optimum and complete disassembly increases almost linearly (solid curve in panel B), while the relative difference increases exponentially (dashed curve in panel B).



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