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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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A novel multi-objective dynamic flexible job shop scheduling algorithm using reinforced learning based black widow

Kashif Akram1, Muhammad Usman Bhutta1,2, Shahid Ikramullah Butt1

  • 1School of Mechanical & Manufacturing Engineering (SMME), Campus H-12, National University of Sciences & Technology (NUST), Islamabad, Pakistan.

Plos One
|April 20, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Black Widow Spider Algorithm with Reinforcement Learning (BWSA-RL) for dynamic job shop scheduling. It effectively balances energy efficiency and operational goals in fast-paced manufacturing.

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Manufacturing Systems Engineering

Background:

  • Manufacturing environments face challenges with dynamic job arrivals and the need for efficient scheduling.
  • The flexible job shop scheduling problem (FJSP) is critical for optimizing production cycles.
  • Existing methods struggle with multi-objective dynamic scenarios and disruptive events.

Purpose of the Study:

  • To propose a novel reinforcement learning-based Black Widow Spider Algorithm (BWSA-RL) for the multi-objective dynamic flexible job shop scheduling problem (MODFJSP).
  • To enhance the algorithm's adaptability and solution diversity in real-time manufacturing settings.
  • To improve balancing of energy efficiency with operational objectives like makespan and due-date conformance.

Main Methods:

  • Developed a hybrid reinforcement learning framework for dynamic adjustment of BWSA-RL parameters.
  • Implemented a novel conversion operator for switching between SARSA and Q-learning based on Q-table sparsity.
  • Introduced a hybrid crowding distance (HCD) metric for Pareto front diversity and a rescheduling-heuristic for new job arrivals.
  • Validated the mathematical model using Mixed Integer Linear Programming (MILP).

Main Results:

  • BWSA-RL outperformed four state-of-the-art algorithms in 83.3% of benchmark instances.
  • The conversion operator and HCD metric demonstrated effectiveness in balancing exploration/exploitation and maintaining solution diversity.
  • The proposed approach successfully accommodated new job arrivals through rescheduling-heuristics.
  • BWSA-RL showed robust performance in balancing energy efficiency, makespan, due-date conformance, and schedule stability.

Conclusions:

  • BWSA-RL is a robust and effective approach for addressing the MODFJSP in dynamic manufacturing environments.
  • The hybrid reinforcement learning framework and novel components significantly improve scheduling performance and adaptability.
  • The study provides a valuable tool for optimizing complex manufacturing operations under uncertainty.