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Operation of the Collaborative Composite Manufacturing CCM System
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Dual-self-learning co-evolutionary algorithm for energy-efficient flexible job shop scheduling problem with

Meizhou Zhang1,2, Min Zhou1,2, Liping Zhang3,4

  • 1Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, China.

Scientific Reports
|September 5, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces energy-efficient scheduling for processing-transportation robots in manufacturing. A new algorithm minimizes energy consumption and production time in flexible job shops.

Keywords:
Co-evolutionary algorithmEnergy-efficient flexible job shop schedulingMulti-objective optimizationProcessing-transportation composite robotsSelf-learning

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Area of Science:

  • Manufacturing Systems Engineering
  • Operations Research
  • Artificial Intelligence

Background:

  • Processing-transportation composite robots are increasingly used in manufacturing, leading to higher energy demands.
  • Efficient scheduling is crucial for managing energy consumption in these complex systems.
  • Flexible job shop environments present unique challenges for integrated robot scheduling.

Purpose of the Study:

  • To investigate energy-efficient scheduling for robot-machine integration in flexible job shops.
  • To minimize both total energy consumption and makespan.
  • To address the growing energy consumption associated with advanced manufacturing robotics.

Main Methods:

  • Development of a novel mixed-integer linear programming (MILP) model.
  • Proposal of a dual-self-learning co-evolutionary algorithm for optimization.
  • Utilizing a three-dimensional solution representation and a greedy decoding strategy.
  • Employing a hybrid initialization method with adaptive random selection and chaos mapping.
  • Implementing a dual-self-learning mechanism for evolutionary operator selection and population interaction.

Main Results:

  • The proposed MILP model effectively formulates the energy-efficient scheduling problem.
  • The dual-self-learning co-evolutionary algorithm demonstrates superior performance in minimizing energy consumption and makespan.
  • Experimental analyses confirm the effectiveness of the proposed algorithm and its components.
  • The greedy decoding strategy successfully reduces idle time and energy usage.

Conclusions:

  • The developed approach provides an effective solution for energy-efficient scheduling in robot-machine integrated flexible job shops.
  • The novel algorithm and its components significantly improve optimization outcomes.
  • This research contributes to sustainable manufacturing practices through reduced energy consumption.