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Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study

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Abstract

End-of-Life (EOL) product recovery is proved to be an attractive way to achieve sustainable manufacturing while extending the producer’s responsibility to closed-loop product service. However, it is still a challenge to provide flexible and smart recovery plans for industrial equipment at different periods of product service. In this paper, we investigate the smart recovery decision-making problem. We propose a system framework for the implementation of smart EOL management based on product condition monitoring. Different product-level EOL business strategies and component-level recovery options are suggested in this recovery decision support system. Then, multi-objective optimization models are formulated to identify the age-dependent recovery roadmap that best matches the product condition and meets the business goals. In order to achieve environmentally friendly recovery, both recovery profits and energy performances are optimized in our models. We conduct a case study of belt lifter used in the automobile assembly line. The Non-dominated Sorting Genetic Algorithm II is used to solve the proposed model. Numerical experiments validate our models and provide practical insights into flexible recovery business.

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Abbreviations

AHP:

Analytic Hierarchy Process

BCD:

Buy-back and Component Dismantling

BOM:

Bill of Material

BOR:

Buy-back and Overall Remanufacturing

EOL:

End-of-Life

ERM:

Early Retirement Mode

IoT:

Internet of Things

OEM:

Original Equipment Manufacturer

PLM:

Product Life-cycle Management

MACBETH:

Measuring Attractiveness by a Categorical Based Evaluation Technique

MOIP:

Multi-Objective Integer Programming

MCDM:

Multi-Criteria Decision-Making

NRM:

Normal Retirement Mode

NSGA:

Non-dominated Sorting Genetic Algorithm

POS:

Pareto Optimal Set

PROMETHEE:

Preference Ranking Organization METHod for Enrichment Evaluation

RCS:

Refurnishing for Clunkers Service

RUL:

Remaining Useful Life

TOPSIS:

Technique for Order Preference by Similarity to Ideal Solution

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Acknowledgement

This work was supported by the Fundamental Research Funds for the Central Universities in China [No. NS2017028].

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Authors and Affiliations

  1. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Kai Meng

  2. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China

    Xiaoming Qian & Peihuang Lou

  3. Jiangsu Key Laboratory of Precision and Micro-Manufacturing Technology, Nanjing, Jiangsu, China

    Xiaoming Qian & Peihuang Lou

  4. Miracle Automation Engineering Co., LTD, Wuxi, Jiangsu, China

    Jiong Zhang

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  1. Kai Meng
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Corresponding author

Correspondence to Xiaoming Qian.

Appendix

Appendix

Recovery roadmap of the lifter used in the automobile assembly line

  1. The symbol ● denotes remanufacturing, ■ for reusing, and ◇ for recycling

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Meng, K., Qian, X., Lou, P. et al. Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study. J Intell Manuf 31, 183–197 (2020). https://doi.org/10.1007/s10845-018-1439-2

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