Quality-driven recovery decisions for used components in reverse logistics

Kai Meng, Peihuang Lou, Richard Peng, Victor Prybutok

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Reverse logistics has emerged as a promising strategy for enhancing environmental sustainability through remanufacturing, reusing, or recycling used components. It is crucial to pursue quality-driven decision-making for component recovery because quality is a dominant factor for component salvage value and its recoverability. To maximise the profit from component recovery, a quality-driven decision model was proposed in this study. Remaining useful life (RUL) was utilised as a measure of quality in the proposed model, where conditional RUL distribution was predicted by utilising both the failure data and condition monitoring data based on a proportional hazard model. Under RUL uncertainty, an interval decision-making approach was developed to suggest recovery strategies for the decision-makers to identify a satisfactory solution according to their risk preferences. Compared to the existing approaches for quality-driven recovery decision-making based on RUL prediction, this work provides a more accurate and powerful approach to managing and mitigating decision risk. Numerical experiments demonstrated the effectiveness and superiority of the proposed model.

Original languageEnglish (US)
Pages (from-to)4712-4728
Number of pages17
JournalInternational Journal of Production Research
Volume55
Issue number16
DOIs
StatePublished - Aug 18 2017

Fingerprint

Logistics
Recovery
Decision making
Salvaging
Condition monitoring
Recycling
Sustainable development
Hazards
Profitability
Reverse logistics
Experiments

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

Meng, Kai ; Lou, Peihuang ; Peng, Richard ; Prybutok, Victor. / Quality-driven recovery decisions for used components in reverse logistics. In: International Journal of Production Research. 2017 ; Vol. 55, No. 16. pp. 4712-4728.
@article{1ef1acf5869042bfbd8d8e4a54c83695,
title = "Quality-driven recovery decisions for used components in reverse logistics",
abstract = "Reverse logistics has emerged as a promising strategy for enhancing environmental sustainability through remanufacturing, reusing, or recycling used components. It is crucial to pursue quality-driven decision-making for component recovery because quality is a dominant factor for component salvage value and its recoverability. To maximise the profit from component recovery, a quality-driven decision model was proposed in this study. Remaining useful life (RUL) was utilised as a measure of quality in the proposed model, where conditional RUL distribution was predicted by utilising both the failure data and condition monitoring data based on a proportional hazard model. Under RUL uncertainty, an interval decision-making approach was developed to suggest recovery strategies for the decision-makers to identify a satisfactory solution according to their risk preferences. Compared to the existing approaches for quality-driven recovery decision-making based on RUL prediction, this work provides a more accurate and powerful approach to managing and mitigating decision risk. Numerical experiments demonstrated the effectiveness and superiority of the proposed model.",
author = "Kai Meng and Peihuang Lou and Richard Peng and Victor Prybutok",
year = "2017",
month = "8",
day = "18",
doi = "10.1080/00207543.2017.1287971",
language = "English (US)",
volume = "55",
pages = "4712--4728",
journal = "International Journal of Production Research",
issn = "0020-7543",
publisher = "Taylor and Francis Ltd.",
number = "16",

}

Quality-driven recovery decisions for used components in reverse logistics. / Meng, Kai; Lou, Peihuang; Peng, Richard; Prybutok, Victor.

In: International Journal of Production Research, Vol. 55, No. 16, 18.08.2017, p. 4712-4728.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Quality-driven recovery decisions for used components in reverse logistics

AU - Meng, Kai

AU - Lou, Peihuang

AU - Peng, Richard

AU - Prybutok, Victor

PY - 2017/8/18

Y1 - 2017/8/18

N2 - Reverse logistics has emerged as a promising strategy for enhancing environmental sustainability through remanufacturing, reusing, or recycling used components. It is crucial to pursue quality-driven decision-making for component recovery because quality is a dominant factor for component salvage value and its recoverability. To maximise the profit from component recovery, a quality-driven decision model was proposed in this study. Remaining useful life (RUL) was utilised as a measure of quality in the proposed model, where conditional RUL distribution was predicted by utilising both the failure data and condition monitoring data based on a proportional hazard model. Under RUL uncertainty, an interval decision-making approach was developed to suggest recovery strategies for the decision-makers to identify a satisfactory solution according to their risk preferences. Compared to the existing approaches for quality-driven recovery decision-making based on RUL prediction, this work provides a more accurate and powerful approach to managing and mitigating decision risk. Numerical experiments demonstrated the effectiveness and superiority of the proposed model.

AB - Reverse logistics has emerged as a promising strategy for enhancing environmental sustainability through remanufacturing, reusing, or recycling used components. It is crucial to pursue quality-driven decision-making for component recovery because quality is a dominant factor for component salvage value and its recoverability. To maximise the profit from component recovery, a quality-driven decision model was proposed in this study. Remaining useful life (RUL) was utilised as a measure of quality in the proposed model, where conditional RUL distribution was predicted by utilising both the failure data and condition monitoring data based on a proportional hazard model. Under RUL uncertainty, an interval decision-making approach was developed to suggest recovery strategies for the decision-makers to identify a satisfactory solution according to their risk preferences. Compared to the existing approaches for quality-driven recovery decision-making based on RUL prediction, this work provides a more accurate and powerful approach to managing and mitigating decision risk. Numerical experiments demonstrated the effectiveness and superiority of the proposed model.

UR - http://www.scopus.com/inward/record.url?scp=85011798937&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85011798937&partnerID=8YFLogxK

U2 - 10.1080/00207543.2017.1287971

DO - 10.1080/00207543.2017.1287971

M3 - Article

VL - 55

SP - 4712

EP - 4728

JO - International Journal of Production Research

JF - International Journal of Production Research

SN - 0020-7543

IS - 16

ER -