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  • 中国标准连:ISSN1005-2895
  • 续出版物号: CN 33-1180/TH
  • 主管单位:轻工业杭州机电设计研究院有限公司
  • 主办单位:轻工业杭州机电设计研究院有限公司、中国轻工机械协会、中国轻工业机械总公司
  • 社  长:刘安江
  • 主  编:黄丽珍
  • 地  址:杭州市余杭区高教路970号西溪联合科技广场4-711
  • 电子邮件:qgjxzz@126.com
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易传龙, 张淞皓, 张守京*.基于改进RSO算法的柔性车间物料配送路径优化[J].轻工机械,2025,43(4):71-77
基于改进RSO算法的柔性车间物料配送路径优化
Material Distribution Path Optimization of Flexible Workshop Based on Improved RSO Algorithm
  
DOI:10.3969/j.issn.1005 2895.2025.04.010
中文关键词:  物流  物料配送  双向配送策略  鼠群优化算法  路径优化
英文关键词:logistics  material distribution  bidirectional strategy  RSO(Rat Swarm Optimizer)  path optimization
基金项目:陕西省“秦创原科学家+工程师”队伍建设项目(2024QCY KXJ 030)。
作者单位
易传龙, 张淞皓, 张守京* (西安工程大学 机电工程学院 陕西 西安710048) 
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中文摘要:
      针对柔性车间物料配送及余料、废料回收的成本高、效率低等问题,课题组对自动引导小车(Automated Guided Vehicle,AGV)路径进行了优化设计。首先,课题组提出了物料和余废料双向配送策略,结合物料配送时间窗限制,建立以最小成本为优化目标的模型;提出了改进的鼠群优化(Rat Swarm Optimizer,RSO)算法,并在RSO算法中引入混沌映射进行种群初始化,在迭代的过程中又引入了高斯游走策略和全局最优路径进行局部扰动及混沌映射,提高算法整体搜索能力,避免陷入局部最优。试验验证结果表明:采用改进RSO算法总成本节省了105.2元,工位优先率提高了12.7%,验证了该模型与改进方法的有效性和可行性。
英文摘要:
      Aiming at the issues of high costs and low efficiency in material distribution and waste recycling in flexible workshops, the research group optimized the path of Automated Guided Vehicle (AGV). Firstly, the research group proposed a bidirectional distribution strategy of material distribution and waste recycling. Combined with a time window constraint for material distribution, a model aimed at minimizing costs as the optimization objective was established. Additionally, an improved Raindrop Splashing Optimization (RSO) algorithm was proposed, and a chaotic map was introduced to initialize the population in the RSO algorithm. Then, during the iterative process, Gaussian random walk strategy and global optimal path were introduced for local perturbation and chaotic mapping to enhance the overall search capability of the algorithm and avoid falling into local optima. The experimental results show that the total cost of the improved RSO algorithm is saved by 105.2 yuan, and the workstation priority rate increased by 12.7%, verifying the effectiveness and feasibility of this model and improvement method.
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