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A new uncertain remanufacturing scheduling model with rework risk using hybrid optimization algorithm.

Wenyu Zhang1, Jun Wang1, Xiangqi Liu2,3

  • 1School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, 310018, China.

Environmental Science and Pollution Research International
|March 22, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for remanufacturing scheduling that accounts for uncertainties and rework risks using interval grey numbers. A hybrid optimization algorithm effectively solves this model, outperforming others in simulations.

Keywords:
Differential evolution algorithmInterval grey numberParticle swarm optimization algorithmRemanufacturing schedulingRework risk

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Area of Science:

  • Manufacturing Engineering
  • Operations Research
  • Sustainability Science

Background:

  • Remanufacturing is a key strategy for sustainable production, but scheduling is complex due to product uncertainties and rework risks.
  • Traditional methods struggle to accurately model remanufacturing uncertainties with limited historical data.
  • Addressing these challenges is crucial for realizing the full benefits of remanufacturing.

Purpose of the Study:

  • To propose a novel uncertain remanufacturing scheduling model incorporating rework risk.
  • To develop an effective hybrid optimization algorithm for solving the proposed model.
  • To demonstrate the practical applicability and effectiveness of the new approach.

Main Methods:

  • Utilizing interval grey numbers to clearly describe uncertainties and integrate rework risk.
  • Developing a hybrid optimization algorithm combining differential evolution and particle swarm optimization.
  • Conducting simulation experiments on various scales to validate the algorithm's performance.

Main Results:

  • The proposed model successfully incorporates uncertainty and rework risk in remanufacturing scheduling.
  • The hybrid optimization algorithm demonstrated superior performance in simulation experiments.
  • The algorithm achieved better optimal solutions than baseline algorithms on 17 out of 18 instances.

Conclusions:

  • The developed interval grey number-based model provides a practical and effective method for uncertain remanufacturing scheduling.
  • The hybrid optimization algorithm offers enhanced performance for solving complex scheduling problems with uncertainties.
  • This research contributes to improving the efficiency and sustainability of remanufacturing processes.