考虑工人疲劳的工作排程研究

为提高企业整体生产力,降低工人累积性疲劳对生产过程的影响,在综合考虑工人疲劳与作业负荷的基础上,以单人多任务排程为研究对象,工人的最大可接受工作时间为休息与否的判定依据,构建了单人工作-休息排程的数学模型。从不同的工作休息组合中选出最佳的排程组合,以优化最小化任务的最大完工时间和因工人工作时间超过最大可接受工作时间而产生的额外能量消耗双目标函数。应用线性加权和法将多目标化为单目标用遗传算法求解,通过算例分析得出模型的满意解,从而得到任务的排序以及工人的休息时间和休息次数的排程组合方案。模型存在多个满意解,为管理者提供多种备选方案,管理者根据实际情况选择实施最优化的组合方案。     

基于精益物流的单件离散型生产作业排程优化

通过实证研究,分析了单件离散型生产作业排程特征和设备对排程的重要性,提出了从设备使用和顾客需求角度研究作业排程优化的必要性.引入设备综合技术性能指数作为质量保证的考量指标,以精益物流为指导思想,综合质量保证指数、生产浪费指数和准时交货指数三项指标,以生产能力、工艺时序、随机不确定性因素为约束条件,建立了组合优化模型.为了有效地求解模型,设计了集成算法,并结合实例分别应用“SM&PR”(仿真&优先规则)和“SM&PR&GA”(仿真&优先规则&遗传算法)进行了仿真试验,检验了模型和算法的有效性.结果显示“SM&PR&GA”是最有效的方法,与传统方法相比,具有动态随机性、全局收敛性和智能性等特征,提高了作业排程的效率和科学性.     

考虑医患满意度和手术成本的日手术排程方法

针对手术排程问题涉及众多利益相关者的特点,本文考虑相关医疗资源约束,在手术排程问题中寻求最优的开启手术室数量以降低手术成本,寻求最优的手术顺序以降低病患的等待时间和医生的加班时间,从而提高病患、医生和医院三方满意度,并依此建立了求解多目标手术排程问题的优化模型;根据问题特点,基于改进的非支配排序算子的非支配排序遗传算法(INSGA-III)求解问题,并提出种群染色体唯一策略等以进一步提升算法的搜索性能,利用GD和HVR指标检验算法的性能。最后,通过对某三甲医院的日手术排程过程进行仿真实验,验证了所提出的手术排程方法的可行性和有效性。     

基于Drools规则引擎的柔性化钣金产线优化排程

针对柔性化钣金产线加工订单呈现离散化特征所导致的人工排程不能满足需求的问题,在订单按时交货的基础上以最小化综合成本及最低综合设备损耗为目标,基于Drools规则约束引擎,研究适用于柔性化钣金产线的快速寻优排程方法.以钣金生产为例,分析排程规划问题的求解过程,通过分析多目标求解的问题提出结合Drools规则引擎的求解方法...     

平准化生产模型研究及应用分析

按照平准化生产原理,探求了汽车平准化生产的多品种均衡性和排程复杂性。在此基础上,构建了基于时段特征的混流切换模型,并进一步研究了混流切换模型下的多品种混流排程方法,解析了生产中获得排程满意解的基本过程,通过实例验证了混流切换模型和生产比倒数法的有效性,降低了实际生产排程复杂性。     

面向择期患者的多医院手术室联合排程研究

多医院手术室的联合排程能够有效缓解医疗服务压力,促进医疗资源配置优化,提高医疗运作效率。现有的手术排程研究多集中于一家医院内部手术资源的优化,极少考虑多医院参与的多手术室联合排程。以生产调度理论为基础,把多家医院看作多个工厂,把手术和术后麻醉恢复看作两阶段加工过程,以最小化最大手术完成时间为目标函数,建立了分布式两阶段混合流水车间调度模型,提出了基于自适应学习的遗传算法,在传统遗传算法中引入包含自我学习、社会学习和随机学习的三段式学习操作,并通过多组测试问题对算法性能进行了评价和对比分析,验证了算法的有效性。     

一类制造与包装流程的仿真优化

生产-包装过程存在多个相互影响的因素,决策过程中需综合考虑以达到最优配置。通过对资源、对象、进程和它们的属性与行为进行计算机仿真,建立了生产-包装过程计算机仿真模型。为获得最优的设备组合和生产排程,可通过成本或利用率等指标优化求解;重复运行模型可找到收敛的最优方案。     

基于MTO生产策略的供应链联盟集成决策模型研究

根据供应链运作的特点提出了供应链绩效综合评价的四个标准，以此为基础建立了MTO生产策略下供应链联盟集成决策指派求解模型。在供应链联盟总体综合绩效最大化和各个层次上企业意愿支付的基础上解决了联盟内部多支供应链生产合作的排程问题。供应链联盟的核心决策者运用该模型可以根据供应链运作环境的改变来动态地制订联盟合作战略。研究表明，该战略符合联盟博弈的Nash均衡，可以在保证联盟稳定性的同时使得联盟的整体绩效得到改善。     

多科室择期手术日排程与医生指派研究

针对多科室的持续时间不确定的手术,通过分析天津某三甲医院的数据发现不同类型的手术持续时间均服从对数正态分布。考虑到不同科室病人的医学特征带来的术间和医生的约束,建立了手术排程和医生指派问题的数学模型,确定每个术间的手术序列并为病人指派符合要求的医生。采用改进的遗传算法求解该模型,利用MATLAB进行多组算例的计算。结果证明了模型和方法的有效性,并为开放术间数和排班医生数的确定提供理论依据。     

采购与多层生产计划问题的协同优化

针对生产与采购这两项企业的重要活动,建立了一种采购计划和多层生产计划的集成优化模型,将采购计划、批量计划以及作业排序这三层计划问题进行综合决策。为求解该问题,设计了一种遗传算法和网络优化方法相结合的协同优化策略,遗传算法中每一个个体同时包含了批量计划和作业排序,并调用网络优化模型得出订购策略。数值仿真实验显示了提出的方法求解该类集成优化问题的良好性能。通过与独立优化方法进行比较,显示了协同优化策略的优越性。     

共存到共生：夹江手工造纸村民居保护与更新研究

目的 针对当今传统手工艺村落原有的良性共生关系被破坏，呈现村落与现代需求、生态环境等多因素间互相对立共存的现象，从手工艺及村落共生性发展的视角，以夹江手工造纸村为例，将研究对象聚焦于受造纸业影响的民居环境，通过对村落自身文化特色和现有共存现象的分析。方法 提出对手工艺与民居环境整体性保护的策略与方法，试图推动手工造纸与民居环境从当下的异质共存向和谐共生的良序关系转化。以共生理论为基础，结合中西方的文化思想观点对共生理论进行溯源、归纳总结与探讨，并形成自我的“共生”解读视角。强调异质共存是共生的前提，结合黑川纪章的共生思想提出动态多元、通时合变的共生设计观，探索共生理论介入的可能性。结果 以石堰村为夹江县手工造纸村的典型样本展开具体个案分析，明确其文化核心价值，深挖“五缘社会”关系的共存现象，并进行系统性整理，结合造纸业的现代化发展，总结出在技艺更迭下石堰村民居环境的演变动因。结论 运用共生理论厘清民居环境内外共生因素之间的作用机制，构建传统产居合一民居保护与更新过程中的共生性体系，确定生态环境与生产环境的共生、传统产居方式与现代人居的共存，由此衍生新的村落文化现象。     

基于改进遗传算法的导向辊生产调度方法研究

目的 研究导向辊生产车间中的调度优化问题,有利于缩短工件的完工时间,提高产线生产效率。方法 以某导向辊生产车间为研究对象,以最小化最大完工时间为目标建立数学模型。针对该导向辊生产车间的实际工况,提出一种改进的遗传算法进行求解。通过对10种不同尺寸的导向辊进行生产调度,分别采用改进的遗传算法和传统遗传算法进行试验分析。结果 改进的遗传算法相比传统遗传算法寻优能力更高,工件的完工时间从139 min缩短为113 min,缩短了18.7%左右,生成了完工时间为113 min的生产调度甘特图。结论 与传统遗传算法相比,改进的遗传算法在导向辊生产调度优化中具有更高的全局优化能力和寻优精度。     

基于遗传算法的瓦楞辊齿形参数优化

目的研究降低钙塑瓦楞复合纸板生产成本的问题。方法通过对瓦楞齿形参数的研究,建立以压楞系数为目标函数的数学模型,利用MATLAB平台中的遗传算法工具箱对数学模型进行优化计算,并将优化后的结果与现有瓦楞辊参数进行对比。结果优化后,瓦楞辊的齿顶圆弧半径、齿根圆弧半径和楞高减小,楞数增加,压楞系数减小,原纸耗材降低,生产成本降低。结论通过利用MATLAB平台中的遗传算法工具箱对压楞系数齿形参数模型进行优化,齿顶圆弧半径减小0.161 mm,齿根圆弧半径减小0.111 mm,楞数增加1.268,楞高减小0.955 mm,压楞系数减小0.1801。压楞系数的减小表明原纸的耗材的减小,可以明显降低生产成本。     

遗传算法的双目标柔性作业车间调度研究

研究了FMS环境下先进制造车间路径柔性的优化调度问题.同时考虑现代生产准时制的要求,建立了柔性作业车间调度问题的双目标数学优化模型,并给出了求解模型的遗传算法的具体实现过程;针对模型的特殊性,提出了染色体两层编码结构,将AOV网络图应用到解码和适应度函数的计算中,通过一个调度实例进行验证,给出了相应的选择、交叉、变异操作设计方案.     

Integrated optimisation of scheduling and ordering based on semi-finished goods delayed differentiation

The implementation of dynamic ordering policies is becoming increasingly important for the competitiveness of modern manufacturing systems. However, existing models on dynamic ordering pay little attention to production scheduling, which greatly affects the fulfilment of dynamic ordering, especially in complex manufacturing systems. Therefore, it is imperative to establish a new model which integrates both dynamic ordering and production scheduling. Accordingly, a quantitative measurement method for integration is needed. To this end, this paper proposes a semi-finished goods delayed differentiation (SFGDD) model by taking into account integration of the scheduling inventory control and dynamic ordering simultaneously. The objective of this model is to study the relationship between the shop floor inventory and the ordering control based on the semi-finished goods dynamic dispatching mechanism. In addition, the days of inventory (DOI) and a backorder penalty exponential function are developed to quantitatively measure such a relationship. To obtain the optimal results, this paper employs a heuristic genetic algorithm (HGA) with a heuristic encoding scheme to synchronise the generation and selection of inventory variables coherently. A case study on a semiconductor assembly and test manufacturing (ATM) is presented, and a significant revenue enhancement and inventory reduction are achieved accordingly.     

一种新型短切碳纤维增强纸基摩擦材料研究

以短切碳纤维为增强纤维,采用湿法工艺研制成功一种新型无石棉纸基摩擦材料,采用TG-DTA热分析方法、定速摩擦试验和惯量摩擦研究了摩擦材料耐热性能、摩擦性能和磨损性能.试验结果表明,新研制的炭纤维增强纸基摩擦材料的起始分解温度和失重速率等耐热性能指标优良,动摩擦系数高达0.136～0.149,且摩擦系数稳定,磨损率低,是一种理想的新型无石棉纸基摩擦材料.     

The Moisture and Rate-Dependent Mechanical Properties of Paper: A Review

Paper is a complex structure of composite biological fibers.The behavior of paper is time-dependent with respect to load, moisturecontent, or temperature, whether these control parameters are fixed orvaried in combination. A key question is whether the time-dependentproperties are a consequence of the fiber micro-structure, theinterfiber bond, the fiber distribution in a sheet, or a combination ofthese. Hypotheses for the physical mechanisms responsible forstress-strain relations observed under constant, monotonic, and cyclicloading and, especially, for the role of moisture bonding in thetime-dependent behavior are compared. The moisture accelerated creepphenomenon, due to varying ambient relative humidity, is an importanttime-dependent behavior which creates practical problems such as thewall collapse of stacked cartons in warehouses with non-constanthumidity and control of the paper-making machine. For each type ofloading and ambient control variation, after a discussion of possiblephysical mechanisms inducing the observed response, the mathematicalmodels proposed in the literature are reviewed as tools for the designof both the paper-making process and applications.     

A genetic algorithm approach for production scheduling with mould maintenance consideration

The traditional approach for maintenance scheduling concerns single-resource (machine) maintenance during production which may not be sufficient to improve production system reliability as a whole. Besides, in the literature many researchers schedule maintenance activities periodically with fixed maintenance duration. However, in a real manufacturing system maintenance activities can be executed earlier and the maintenance duration will become shorter since less time and effort are required. A practical example is that in a plastic production system, the proportion of machine-related downtime is even lower than mould-related downtime. The planned production operations are usually interrupted seriously because of the mismatch among the maintenance periods between injection machine and mould. In this connection, this paper proposes to jointly schedule production and maintenance tasks of multi-resources in order to improve production system reliability by reducing the mismatch among various processes. To integrate machine and mould maintenance tasks in production, this paper attempts to model the production scheduling with mould scheduling (PS-MS) problem with time-dependent deteriorating maintenance schemes. The objective of this paper is to propose a genetic algorithm approach to schedule maintenance tasks jointly with production jobs for the PS-MS problem, so as to minimise the makespan of production jobs.     

Research on Forecasting Flowering Phase of Pear Tree Based on Neural Network

Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes. The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience, which have problems such as inaccurate recognition time, time-consuming and energy sapping. Therefore, this paper proposes a neural network-based method for predicting the flowering phase of pear tree. Firstly, based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019, three principal components (the temperature factor, weather factor, and humidity factor) with high correlation coefficient with the flowering phase of pear tree are obtained by using the principal component analysis method. Then, the three components are used as input factors for the BP neural network. A BP neural network prediction model is constructed based on genetic algorithm optimization. The crossover operator and mutation operator in the adaptive genetic algorithm are improved. Finally, the meteorological sample data from 2013 to 2019 are used to test and verify the algorithm in this paper. The results demonstrate that, the model can solve the local optimization problem of the BP neural network model. The prediction results of the flowering phase of pear tree are evaluated in terms of relevance and prediction accuracy. Both are superior to the traditional effective accumulated temperature and the prediction results of the stepwise regression method. This method can provide more reliable forecast information for the blooming period, which can provide decision-making reference for improving the development of tourism industry.