Scholarlink

<p>Green scheduling systems are attracting increasing attention due to their environmental impacts. This paper addresses the Green Flexible Job-Shop Scheduling Problem with variable processing speeds, aiming to minimize both the makespan and total energy consumption. To solve this problem, we propose a self-learning Variable Neighborhood Search with Q-learning (SL_VNS) algorithm, which integrates three key mechanisms: (1) four initialization strategies designed to generate high-quality initial solutions; (2) A parameter adaptation strategy based on reinforcement learning to select optimal parameters dynamically; and (3) an elite archive to save historical solutions for improved performance. Computational experiments demonstrate that the proposed algorithm is more effective and robust compared to other algorithms.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A self-learning Variable Neighborhood Search algorithm based on reinforcement learning for Green flexible job-shop scheduling problem

  • 期刊论文

  • 发表日期：

    2026年4月3日

查看全文
  • Maha Ben Hamida,
  • Ameni Azzouz,
  • Lamjed Ben Said

摘要

Green scheduling systems are attracting increasing attention due to their environmental impacts. This paper addresses the Green Flexible Job-Shop Scheduling Problem with variable processing speeds, aiming to minimize both the makespan and total energy consumption. To solve this problem, we propose a self-learning Variable Neighborhood Search with Q-learning (SL_VNS) algorithm, which integrates three key mechanisms: (1) four initialization strategies designed to generate high-quality initial solutions; (2) A parameter adaptation strategy based on reinforcement learning to select optimal parameters dynamically; and (3) an elite archive to save historical solutions for improved performance. Computational experiments demonstrate that the proposed algorithm is more effective and robust compared to other algorithms.