Research on scheduling optimization of mechanical processing workshop based on intelligent manufacturing
基于智能制造的机械加工车间调度优化研究

摘要

随着工业4.0和智能制造的快速发展，机械加工车间调度优化成为提升生产效率、降低能耗的关键环节。本文针对传统车间调度方法在动态环境下的适应性不足问题，提出一种融合多目标优化与强化学习的智能调度框架。首先，基于对国内外智能制造调度文献的系统分析（涵盖JIT、柔性作业车间调度问题FJSP、数字孪生技术等），构建了以最小化最大完工时间（Makespan）、设备负载均衡度和能耗为目标的混合整数规划模型（MILP）。其次，针对模型NP-hard特性，设计改进的NSGA-III多目标遗传算法，引入自适应交叉算子和基于机器学习的帕累托前沿筛选机制以提升收敛效率。

Abstract

With the rapid development of Industry 4.0 and intelligent manufacturing, the optimization of mechanical processing workshop scheduling has become a key link to enhance production efficiency and reduce energy consumption. This paper addresses the insufficiency of traditional workshop scheduling methods in dynamic environments and proposes an intelligent scheduling framework that integrates multi-objective optimization and reinforcement learning. Firstly, based on a systematic analysis of domestic and international literature on intelligent manufacturing scheduling (including Just-In-Time (JIT), Flexible Job Shop Scheduling Problem (FJSP), digital twin technology, etc.), a mixed-integer linear programming (MILP) model is constructed with the objectives of minimizing the maximum completion time (Makespan), balancing equipment load, and reducing energy consumption. Secondly, to address the NP-hard nature of the model, an improved NSGA-III multi-objective genetic algorithm is designed, introducing an adaptive crossover operator and a Pareto front screening mechanism based on machine learning to enhance convergence efficiency.