资源约束的模块化服装生产工序编排优化模型与算法
Optimization model and algorithm of modular garment production process scheduling with resource constraints
颜伟雄 1胡觉亮 2韩曙光2
作者信息
- 1. 浙江理工大学服装学院,浙江 杭州 310018
- 2. 浙江理工大学理学院,浙江 杭州 310018
- 折叠
摘要
为适应"多品种、小批量、短周期"服装生产现状,考虑服装生产线工作站带有资源设备数量约束的作业平衡问题(RCALB-VRW),以资源设备总数和平滑系数(SI)的极小化建立双目标优化数学模型.针对RCALB-VRW的特点,提出基于合并工作站策略的装箱遗传算法.首先设计工序分配列表与资源设备列表的双层实数编码方式;其次基于传统资源约束的生产线平衡问题的资源配置算法,对工作站与资源设备进行装箱操作,优化工序编排方案,在混合服装生产线的设备资源投入数量最小化的前提下,实现各工作站平稳作业;最后以两款相近衬衫为算例进行测试,并与另外3种资源约束模型比较,结果表明装箱遗传算法能够更高效地求解有资源设备数量约束的服装生产工序编排.所提方法可为服装智能制造与精益生产的推进提供理论指导.
Abstract
To adapt to the current situation of"multi-variety,small-batch and short-cycle"garment production,the Resource Constrained Assembly Line Balancing problem considering Variable Resource each Workstation(RCALB-VRW)was studied,and a double objective optimization model was established to minimize the total number of re-sources and Smoothness Index(SI).Due to the characteristics of RCALB-VRW,Next Fit—Genetic Algorithm(GA-NF)was based on the combining workstation strategy.A double-layer real number coding method of process allocation list and resource equipment list was designed.Based on the traditional RCALB resource allocation algo-rithm,the workstation and resource equipment were boxed,and the process arrangement scheme was optimized to realize the smooth operation of each workstation in the hybrid garment production line under the minimization of the input quantity of resource equipment.Two similar shirts were taken as examples to calculate and test.Compared with the other three resource constraint models,the results showed that the GA-NF algorithm could more efficiently solve the modular garment production process scheduling with resource equipment quantity constraints.This study could provide theoretical guidance for the promotion of garment intelligent manufacturing and lean production.
关键词
资源约束/工序编排/混合服装生产线/模块化生产/装箱遗传算法Key words
resource constrained/arrangement of the process/mixed garment production line/modular production/next fit-genetic algorithm引用本文复制引用
基金项目
国家自然科学基金资助项目(12071436)
出版年
2024
NETL
NSTL
万方数据