| 摘 要: 为了解决服装厂制订订单生产计划效率低下的问题,首先考虑了员工熟练度这一影响因素,构建基于熟练度的人员调度策略,建立以实际总工序时间为调度目标的数学模型;其次提出了一种基于遗传算法的人员调度优化算法对问题进行求解;最后采用实际工厂数据进行验证,并与粒子群算法、贪心算法进行对比分析。实验结果表明,19人的生产小组生产单件服装的平均时间为2 909.5 s,与原先由人工制订的生产计划所需的时间4 814.4 s相比,生产效率提升了约39.6%。因此,可以得出基于遗传算法制订生产调度计划的效率高于基于人工经验生产调度计划,能够帮助企业大幅节省生产成本。 |
| 关键词: 服装生产线;人员调度;生产调度;遗传算法 |
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中图分类号: TP18
文献标识码: A
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| 基金项目: 东北大学流程工业综合自动化国家重点实验室联合基金(2022-KF-21-01);浙江理工大学基本科研业务费专项资金资助项目(2021Q026) |
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| Research on Personnel Scheduling Model and Strategy Based on Proficiency in Garment Production Line |
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ZHANG Wenhao1, WANG Wushuang2, WANG Chengqun2,3, LUO Shuyun1,3
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(1.School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China;
2.School of Inf ormation Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China;
3.State Key Laboratory of Integrated Automation in Process Industry, Northeastern University, Shenyang 110004, China)
zhangwenhao_zstu@163.com; wangwushuang_zstu@163.com; cqwang@zstu.edu.cn; shuyunluo@zstu.edu.cn
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| Abstract: In order to solve the problem of low efficiency in formulating order production plans for garment factories, considering the influencing factor of employee proficiency, this paper firstly proposes to build a personnel scheduling strategy based on proficiency and establish a mathematical model with actual total process time as the scheduling goal. Secondly, a method based on the personnel scheduling optimization algorithm of the genetic algorithm is proposed to solve the problem. Finally, actual factory data is used for validation and comparative analysis with particle swarm optimization and greedy algorithms. The experimental results show that the average time for a production team of 19 people to produce a single piece of garment is 2 909.5 seconds, which increases about 39.6% in production efficiency compared to the time required for the original manual production plan of 4 814. 4 seconds. Therefore, it can be concluded that the efficiency of formulating production scheduling plans based on genetic algorithm is higher than that based on manual experience, which significantly saves production cost for enterprises. |
| Keywords: garment production line; personnel scheduling; production scheduling; genetic algorithm |
