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Related Experiment Video

Updated: Oct 5, 2025

An Optimized Protocol for Rearing Fopius arisanus, a Parasitoid of Tephritid Fruit Flies
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Capacitated multi-objective disassembly scheduling with fuzzy processing time via a fruit fly optimization algorithm.

Gang Yuan1,2, Yinsheng Yang3, Guangdong Tian4

  • 1College of Biological and Agricultural Engineering, Jilin University, Changchun, 130022, China.

Environmental Science and Pollution Research International
|January 31, 2022
PubMed
Summary

This study introduces a fuzzy disassembly scheduling model to optimize cycle time and environmental costs in remanufacturing. A novel fruit fly optimization algorithm (FOA) efficiently solves scheduling problems under uncertain processing times.

Keywords:
DisassemblyFruit fly algorithmFuzzy processing timeRemanufacturingScheduling

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Area of Science:

  • Operations Research
  • Industrial Engineering
  • Environmental Management

Background:

  • Disassembly scheduling is crucial for remanufacturing but often faces uncertainty in processing times, especially for obsolete products.
  • Existing models struggle with the inherent variability in disassembly operations, impacting efficiency and cost.

Purpose of the Study:

  • To develop a capacitated fuzzy disassembly scheduling model addressing uncertain processing times.
  • To minimize both cycle time and environmental costs in remanufacturing processes.
  • To propose an efficient metaheuristic algorithm for solving fuzzy disassembly scheduling problems.

Main Methods:

  • A mixed-integer mathematical programming model was formulated to minimize cycle time and environmental cost.
  • A metaheuristic approach, the fruit fly optimization algorithm (FOA), was developed for fuzzy disassembly scheduling.
  • The proposed algorithm was tested on various case studies and compared with other multi-objective optimization algorithms.

Main Results:

  • The fruit fly optimization algorithm (FOA) demonstrated superior computational efficiency compared to other algorithms.
  • The proposed model and algorithm effectively handle uncertainty in disassembly scheduling.
  • Experimental results confirmed the algorithm's applicability across different problem sizes and types.

Conclusions:

  • The developed fuzzy disassembly scheduling model and FOA provide a practical and efficient solution for uncertain environments.
  • This approach enhances remanufacturing processes by optimizing time and environmental factors.
  • The study contributes a novel method for tackling complex scheduling challenges in production and remanufacturing systems.