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Research Papers

Component-Oriented Reassembly in Remanufacturing Systems: Managing Uncertainty and Satisfying Customer Needs

[+] Author and Article Information
Yue Wang

Environmental and Ecological Engineering (EEE),
Purdue University,
500 Central Drive,
West Lafayette, IN 47907
e-mail: yuewang@purdue.edu

Gamini P. Mendis

Environmental and Ecological Engineering (EEE),
Purdue University,
500 Central Drive,
West Lafayette, IN 47907
e-mail: gmendis@purdue.edu

Shitong Peng

Institute of Sustainable Design and Manufacturing,
Dalian University of Technology,
Dalian 116024, China
e-mail: shi-tong.peng@ttu.edu

John W. Sutherland

Fellow ASME
Environmental and Ecological Engineering (EEE),
Purdue University,
500 Central Drive,
West Lafayette, IN 47907
e-mail: jwsuther@purdue.edu

1Corresponding author.

Manuscript received April 16, 2018; final manuscript received November 26, 2018; published online December 24, 2018. Assoc. Editor: William Bernstein.

J. Manuf. Sci. Eng 141(2), 021005 (Dec 24, 2018) (12 pages) Paper No: MANU-18-1242; doi: 10.1115/1.4042150 History: Received April 16, 2018; Revised November 26, 2018

Remanufacturing has recently received significant interest due to its environmental and economic benefits. Traditionally, the reassembly processes in remanufacturing systems are managed using a product-oriented model. When a product is returned and disassembled, the used components may be processed incorrectly, and the quality of the remanufactured products may not meet customer needs. To solve these problems, a component-oriented reassembly model is proposed. In this model, returned components are inspected and assigned scores according to their quality/function and categorized in a reassembly inventory. Based on the reassembly inventory, components are paired under the control of a reassembly strategy, and these pairs are then assembled into reassembly chains. Each chain represents a product. To evaluate the performance of different reassembly strategies under uncertain conditions, we describe the reassembly problem using an agent-environment system. The platform is modeled as a Markov decision process (MDP), and a reassembly score iteration algorithm (RSIA) is developed to identify the optimal reassembly strategy. The effectiveness of the method is demonstrated via a case study using the reassembly process of diesel engines. The results of the case study show that the component-oriented reassembly model can improve the performance of the reassembly system by 40%. A sensitivity analysis is carried out to evaluate the relationship between the parameters and the performance of the reassembly system. The component-oriented model can reassemble products to meet a larger variety of customer needs, while simultaneously producing better remanufactured products.

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Figures

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Fig. 2

An example of the relationship among TS, MS, and CS. (unbiased measurement distribution).

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Fig. 3

A reassembly inventory and multiple reassembly chains

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Fig. 4

An example of pairing processes

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Fig. 5

Agent-environment system for a reassembly system, adapted from Sutton and Barto [29]

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Fig. 6

Probability table for a three-level categorization

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Fig. 7

Backup diagram for iterating through the reassembly value function. There are four components and four CSs. The diagram shows one step of the search process for the highlighted cell (the component in the second row, third column).

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Fig. 8

Reassembly inventory for the seven pieces of engine components

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Fig. 9

The relationship between W and N for p =0.2, d(h,k) = 0.01

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Fig. 10

Histogram of total score for 600 simulations: (a) exhaustive reassembly strategy and (b) component-oriented model

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Fig. 11

Results of sensitivity analysis

Tables

Errata

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