人工作业系统生产计划与优化
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摘要
本文从我国中小型制造企业经营内外部环境及发展现状、订单式中小型制造系统的现实运作需要和MTO/MOS组织与优化控制建模理论研究现状等方面入手,沿着“归纳订单式中小型制造企业生产系统建模仿真要素——建立符合现实生产规律的生产结构仿真模型——获取大样本量实验数据——再归纳影响生产输出的主要因素——再建模并验证——根据建模与仿真提出管理策略”的思路,从随机不确定条件下生产系统运作特征和生产结构建模仿真、完工期预测和交货期决策、计划投产决策和订单排产三个方面对人工作业系统的生产计划和优化进行了研究。研究主要内容有：
(1)随机不确定条件下生产系统运作特征和生产结构建模仿真研究中,总结当前研究现状和调研珠三角中小型制造企业的管理模式,在跟踪典型生产线运行过程,收集相关订单和生产数据的基础上,研究人工作业系统的生产组织管理和流程,总结随机不确定条件下这类系统的运作特征和模式,进而采用排队论和随机理论等结合赋时着色Petri网建模工具,仿真人工作业系统生产结构和订单生产流程,针对人员所掌握的技能数量不同、技能熟悉程度不同等情况评估仿真系统人员的利用率和完工期变动情况,经评估仿真数据与实际生产数据可以得出,利用生产实际与建立仿真模型相结合的方法,既可以设置多种输入条件,快速灵活地获取样本数据,又可以避免现实采样数据的人为干扰,利用仿真模型为后续研究提供模型和数据支持是有效的方法之一。
(2)完工期预测和交货期决策研究中,首先对影响完工期的因素进行了定量和定性分析,重点针对作业模式因素的影响,以产品批加工周期为目标函数,在传统公式中创新性地引入学习率要素,建立了三种模式加工周期公式,并进行学习率、工序时间差和产品批大小的敏感性分析,根据完工期影响因素分析,最终选择产品订单数量、产品种类、工艺难度、作业模式、废品率、原材料供应状况、生产线当前负荷率、设备故障率、设备负荷、产品是否需要制作模具十个因素作为完工期预测的主要影响因素；最后,创新性地提出利用BP神经网络模型建立完工期预测模型,通过大量完工期数据训练并进行实例验证,利用Matlab分别对影响完工期的最主要因素,如生产线负荷、订单批量,进行了敏感性分析,最后综合考虑正常生产下的订单价格、交货期,以及生产企业或客户可接受的订单价格和客户要求的交货期等8种条件下,利用该完工期模型提出了订单式中小型企业确定订单交货期的策略和方法,能较有效地指导我国中小型制造企业进行实际生产实践。
(3)计划投产和订单排产决策研究中,首先在比较是否考虑工人学习率的两种情况下,以生产成本最小化作为决策目标提出计划投产量模型,从交货期由生产企业确定和由客户确定或客户与生产企业共同确定两种情况,分析和建立订单排产的利润模型,采用灰色关联度分析引入混合订单排序模型,综合分析订单完成时间、库存成本、逾期惩罚成本,优化生产流程的差异产品订单组合,最大程度降低差异订单批量生产成本。对订单式中小型制造企业的生产运作具有极大的实践指导意义。
最后,对全文所做的工作进行了总结,并对未来的研究方向进行了展望。本课题的研究得到国家自然科学基金资助项目：订单式生产人工作业系统(MTO/MOS)组织与优化研究(项目编号：70971026)的资助。
Based on the external environment and development status, the realistic operational needs, and the research of modeling theory of organization and optimization to small and medium-sized make-to-order (MTO) manufacturing enterprise, this article mainly studies the operation characteristics and optimization of the production system under random uncertain conditions, the completion time estimation and delivery time decision, production planning and orders scheduling, along the idea:"to summarize the modeling and simulation elements of small and medium-sized make-to-order (MTO) manufacturing enterprise production system-to establish simulation model with regular realistic production structure-to get large amount of experimental data-to summarize the main factors affecting the production output-to model and validate-to propose management strategy based modeling and simulating". The specific studies are as belows:
(1) Research on the operation characteristics and modeling and simulating for production structure under random uncertain conditions. Current research status, the running mode and running characteristicsis of manual operation system (MOS) are summarized. Based the management model of a small and medium-sized manufacturing enterprises in the Pearl River Delta, this article establishes a Timed-colored-Petri net for manual operation system under random uncertain conditions. Then, the essey simulates production data and evaluates the production results in different situations.
(2) Research on the order completion time estimation and delivery time decision. The essey analyzes the factors which are affected the completion time by quantity and quality. Taking the lead time as the objective function, we compare the three operation modes, calculate the lead time of different operating modes based on certain worker's learning rate, and analyze the sensitivity of relationship between different operating modes and the factors such as the workers'learning rate, the production batch and the process time difference, etc. Then, BP neural network is established to completion time estimation within ten factors. Sensitivity of some factors is analysized in Matlab, such as the load on the production line and the number of orders. This model can help small and medium-sized manufacturing enterprises to make a effectively delivery time decision under eight situations.
(3) Research on production planning and orders scheduling. First, considering of the effects of the learning effect, the essey establish a model of decision-making of planning quantity put into production, which takes the minimum expense expected value. Second, the essey establishes the order scheduling profit model between two situation-manufactures who determine the delivery time and customer or both customer and manufactures that determine the delivery time. Finally, grey relational analysis is introduced to optimize the mixed order scheduling moder considering the order fulfillment time, inventory costs and delayed delivery cost.
Eventually, this essay displays the outlook of the future research. And this study is sponsored by The National Nature Science Foundation(70971026).
(1)随机不确定条件下生产系统运作特征和生产结构建模仿真研究中,总结当前研究现状和调研珠三角中小型制造企业的管理模式,在跟踪典型生产线运行过程,收集相关订单和生产数据的基础上,研究人工作业系统的生产组织管理和流程,总结随机不确定条件下这类系统的运作特征和模式,进而采用排队论和随机理论等结合赋时着色Petri网建模工具,仿真人工作业系统生产结构和订单生产流程,针对人员所掌握的技能数量不同、技能熟悉程度不同等情况评估仿真系统人员的利用率和完工期变动情况,经评估仿真数据与实际生产数据可以得出,利用生产实际与建立仿真模型相结合的方法,既可以设置多种输入条件,快速灵活地获取样本数据,又可以避免现实采样数据的人为干扰,利用仿真模型为后续研究提供模型和数据支持是有效的方法之一。
(2)完工期预测和交货期决策研究中,首先对影响完工期的因素进行了定量和定性分析,重点针对作业模式因素的影响,以产品批加工周期为目标函数,在传统公式中创新性地引入学习率要素,建立了三种模式加工周期公式,并进行学习率、工序时间差和产品批大小的敏感性分析,根据完工期影响因素分析,最终选择产品订单数量、产品种类、工艺难度、作业模式、废品率、原材料供应状况、生产线当前负荷率、设备故障率、设备负荷、产品是否需要制作模具十个因素作为完工期预测的主要影响因素；最后,创新性地提出利用BP神经网络模型建立完工期预测模型,通过大量完工期数据训练并进行实例验证,利用Matlab分别对影响完工期的最主要因素,如生产线负荷、订单批量,进行了敏感性分析,最后综合考虑正常生产下的订单价格、交货期,以及生产企业或客户可接受的订单价格和客户要求的交货期等8种条件下,利用该完工期模型提出了订单式中小型企业确定订单交货期的策略和方法,能较有效地指导我国中小型制造企业进行实际生产实践。
(3)计划投产和订单排产决策研究中,首先在比较是否考虑工人学习率的两种情况下,以生产成本最小化作为决策目标提出计划投产量模型,从交货期由生产企业确定和由客户确定或客户与生产企业共同确定两种情况,分析和建立订单排产的利润模型,采用灰色关联度分析引入混合订单排序模型,综合分析订单完成时间、库存成本、逾期惩罚成本,优化生产流程的差异产品订单组合,最大程度降低差异订单批量生产成本。对订单式中小型制造企业的生产运作具有极大的实践指导意义。
最后,对全文所做的工作进行了总结,并对未来的研究方向进行了展望。本课题的研究得到国家自然科学基金资助项目：订单式生产人工作业系统(MTO/MOS)组织与优化研究(项目编号：70971026)的资助。
Based on the external environment and development status, the realistic operational needs, and the research of modeling theory of organization and optimization to small and medium-sized make-to-order (MTO) manufacturing enterprise, this article mainly studies the operation characteristics and optimization of the production system under random uncertain conditions, the completion time estimation and delivery time decision, production planning and orders scheduling, along the idea:"to summarize the modeling and simulation elements of small and medium-sized make-to-order (MTO) manufacturing enterprise production system-to establish simulation model with regular realistic production structure-to get large amount of experimental data-to summarize the main factors affecting the production output-to model and validate-to propose management strategy based modeling and simulating". The specific studies are as belows:
(1) Research on the operation characteristics and modeling and simulating for production structure under random uncertain conditions. Current research status, the running mode and running characteristicsis of manual operation system (MOS) are summarized. Based the management model of a small and medium-sized manufacturing enterprises in the Pearl River Delta, this article establishes a Timed-colored-Petri net for manual operation system under random uncertain conditions. Then, the essey simulates production data and evaluates the production results in different situations.
(2) Research on the order completion time estimation and delivery time decision. The essey analyzes the factors which are affected the completion time by quantity and quality. Taking the lead time as the objective function, we compare the three operation modes, calculate the lead time of different operating modes based on certain worker's learning rate, and analyze the sensitivity of relationship between different operating modes and the factors such as the workers'learning rate, the production batch and the process time difference, etc. Then, BP neural network is established to completion time estimation within ten factors. Sensitivity of some factors is analysized in Matlab, such as the load on the production line and the number of orders. This model can help small and medium-sized manufacturing enterprises to make a effectively delivery time decision under eight situations.
(3) Research on production planning and orders scheduling. First, considering of the effects of the learning effect, the essey establish a model of decision-making of planning quantity put into production, which takes the minimum expense expected value. Second, the essey establishes the order scheduling profit model between two situation-manufactures who determine the delivery time and customer or both customer and manufactures that determine the delivery time. Finally, grey relational analysis is introduced to optimize the mixed order scheduling moder considering the order fulfillment time, inventory costs and delayed delivery cost.
Eventually, this essay displays the outlook of the future research. And this study is sponsored by The National Nature Science Foundation(70971026).
引文
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