多资源协同的智能车间设备配置优化

doi:10.3969/j.issn.1007-7375.220161

工业工程 ›› 2024, Vol. 27 ›› Issue (3): 87-97,105.doi: 10.3969/j.issn.1007-7375.220161

• 智能制造系统与车间调度优化 • 上一篇下一篇

多资源协同的智能车间设备配置优化

张惠煜, 梁展鹏, 王松龄, 陈庆新, 毛宁

广东工业大学广东省计算机集成制造重点实验室, 广东广州 510006

收稿日期:2022-08-22 发布日期:2024-07-12
作者简介:张惠煜 (1989—)，男，广东省人，讲师，博士，主要研究方向为随机生产/服务系统建模与优化
基金资助:
国家自然科学基金资助项目 (61973089); 广东省基础与应用基础研究基金资助项目 (2022A1515011175, 2022A1515010991); 广州市基础研究计划资助项目 (2023A04J0406)

Equipment Configuration Optimization for an Intelligent Workshop with Multi-resource Collaboration

ZHANG Huiyu, LIANG Zhanpeng, WANG Songling, CHEN Qingxin, MAO Ning

Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou 510006, China

Received:2022-08-22 Published:2024-07-12

摘要/Abstract

摘要： 针对多资源协同智能车间中设备数量配置问题，以最小化设备购置成本为目标，建立具有系统产出率和生产周期双重约束的优化模型。由于该优化问题是一个随机非线性的整数规划问题，且约束条件无法用决策变量的封闭形式表达，因此，提出一种基于仿真建模的智能优化算法求解该问题。针对多资源协同的生产车间，基于离散事件仿真平台构建系统的性能估算模型，并提出嵌入仿真模型的灰狼优化算法求解设备数量配置的优化方案。通过仿真算例实验以及优化算例对比，验证该方法对比其他算法在优化结果的优越性和稳定性方面具有明显优势。分析实际应用案例确定了优化的配置方案，结果验证了所提方法的有效性，具有实际应用价值。

关键词: 多资源约束, 智能车间, 灰狼优化 (GWO), 仿真优化, 设备配置

Abstract: To address the problem of equipment quantity configuration in an intelligent workshop with multi-resource collaboration, an optimization model is established with dual constraints on system output rate and production cycle and the objective of minimizing equipment purchase cost. Since the optimization problem is a stochastic nonlinear integer programming one, and the constraints cannot be expressed in closed form of decision variables, a simulation-based intelligent optimization algorithm is proposed to solve it. For such production workshops with multi-resource collaboration, a system performance evaluation model is developed based on a discrete event simulation platform. A gray wolf optimization algorithm embedded in the simulation model is proposed to obtain the optimization scheme of equipment quantity configuration. Through simulation experiments and optimization case comparisons, the superiority and stability of the proposed method over other algorithms in optimization results are verified. The optimal configuration scheme is determined by analyzing actual application cases, and the results verify the effectiveness of the proposed method, demonstrating its practical value.

Key words: multi-resource-constrained, intelligent workshop, grey wolf optimizer (GWO), simulation optimization, equipment configuration

中图分类号:

F406.6
TP391

张惠煜, 梁展鹏, 王松龄, 陈庆新, 毛宁. 多资源协同的智能车间设备配置优化[J]. 工业工程, 2024, 27(3): 87-97,105.

ZHANG Huiyu, LIANG Zhanpeng, WANG Songling, CHEN Qingxin, MAO Ning. Equipment Configuration Optimization for an Intelligent Workshop with Multi-resource Collaboration[J]. Industrial Engineering Journal, 2024, 27(3): 87-97,105.

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多资源协同的智能车间设备配置优化

Equipment Configuration Optimization for an Intelligent Workshop with Multi-resource Collaboration

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