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Updated: Aug 1, 2025

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An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem.

Leilei Meng1, Weiyao Cheng1, Biao Zhang1

  • 1School of Computer Science, Liaocheng University, Liaocheng 252000, China.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an improved genetic algorithm (IGA) to solve the flexible job shop scheduling problem with a limited number of automatic guided vehicles (AGV). The IGA enhances scheduling efficiency and updates benchmark solutions.

Keywords:
automatic guided vehicleflexible job shop scheduling problemgenetic algorithm

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Area of Science:

  • Operations Research
  • Manufacturing Systems Engineering
  • Artificial Intelligence

Background:

  • Real-world manufacturing faces constraints with limited automatic guided vehicles (AGVs).
  • The flexible job shop scheduling problem with limited AGVs (FJSP-AGV) is critical for production efficiency.
  • Minimizing makespan is a key objective in job shop scheduling.

Purpose of the Study:

  • To address the FJSP-AGV by proposing an improved genetic algorithm (IGA).
  • To minimize the makespan in flexible job shop environments with limited AGVs.
  • To enhance the performance of genetic algorithms for complex scheduling problems.

Main Methods:

  • Developed an Improved Genetic Algorithm (IGA) specifically for the FJSP-AGV.
  • Incorporated a population diversity check method within the IGA.
  • Benchmarked the IGA against state-of-the-art algorithms using five sets of instances.

Main Results:

  • The IGA demonstrated superior performance compared to existing state-of-the-art algorithms.
  • The proposed IGA achieved better results in terms of makespan minimization.
  • The IGA successfully updated the best-known solutions for 34 benchmark instances across four datasets.

Conclusions:

  • The IGA is an effective and efficient approach for solving the FJSP-AGV.
  • The population diversity check is a key enhancement for genetic algorithms in scheduling.
  • This research contributes significant improvements to the field of manufacturing scheduling.