A data-driven based decomposition–integration method for remanufacturing cost prediction of end-of-life products

Remanufacturing cost prediction is conducive to visually judging the remanufacturability of end-of-life (EOL) products from economic perspective. However, due to the randomness, non-linearity of remanufacturing cost and the lack of sufficient data samples. The general method for predicting the remanufacturing cost of EOL products is very low precision. To this end, a data-driven based decomposition–integration method is proposed to predict remanufacturing cost of EOL products. The approach is based on historical remanufacturing cost data to build a model for prediction. First of all, the remanufacturing cost of individual EOL product is arranged as a time series in reprocessing order. The Improved Local Mean Decomposition (ILMD) is employed to decompose remanufacturing cost time series data into several components with smooth, periodic fluctuation and use this as input. BP neural network based on Particle Swarm Optimization (PSO-BP) algorithm is utilized to predict the cost of each component. Finally, the predicted components are added to obtain the final prediction result. To illustrate and verify the feasibility of the proposed method, the remanufacturing cost of DH220 excavator is applied as the sample data, and empirical results show that the proposed model is statistically superior to other benchmark models owing to its high prediction accuracy and less computation time. And proposed method can be utilized as an effective tool to analyze and predict remanufacturing cost of EOL products.     

A hybrid method of blockchain and case-based reasoning for remanufacturing process planning

Remanufacturing plays a vital role in promoting the development of circular economy for its great advantages in energy saving, material saving and emission reduction. Remanufacturing process planning (RPP), which affects the performance of remanufacturing greatly, becomes increasingly important to the remanufacturing enterprises. In general, RPP is knowledge dependent. Some remanufacturing enterprises, especially small and middle-sized remanufacturing enterprises (SMREs) may have inadequate remanufacturing knowledge, which makes it difficult to implement a proper RPP. Therefore, how to share and make full use of the knowledge in different remanufacturing enterprises for RPP has become a bottleneck. To this end, a hybrid method integrating blockchain (BC) and case-based reasoning (CBR) for RPP, which can take full advantage of the remanufacturing knowledge by cross enterprises knowledge sharing, is presented in this paper. In this proposed method, a BC network was utilized to record the remanufacturing knowledge and its associated transactions to guarantee the security and reliability of knowledge sharing, and CBR was employed to retrieve and reuse the most suitable solution by analyzing the similarity between previous remanufacturing cases and new case with the nearest neighbor algorithm. Finally, a used lathe guideway was set as a case study to verify the feasibility and superiority of the proposed approach. The hybrid method has been applied in a prototype system written in HTML and JavaScript. The results indicated that the proposed approach can effectively help SMREs to obtain optimum solutions for RPP with comprehensive economic, environmental and social benefits.

     

考虑模糊作业时间的再制造加工车间多目标调度方法

针对再制造加工过程中作业时间的不确定性以及现行车间调度问题中多目标并行的特点,以三角模糊数描述再制造加工车间作业时间的不确定性,建立以完工时间、加工成本、设备负载平衡和加工能耗为目标的再制造加工车间调度模型,并提出一种基于多种群协同进化的混合人工鱼群算法对模型进行求解.该算法采用多种群协同进化的思想提高单种群混合人工鱼群算法的搜索能力,并考虑对多目标再制造加工车间调度问题的适用性,最后以个体分散程度为指标更新Pareto解集中的最优解.仿真实验验证了所提出方法的可行性.     

基于改进AHP-熵博弈赋权的输变电工程评价

针对现有单一赋权法在输变电工程评价过程中存在的片面性问题,为了提高评价结果的科学性与准确性,提出了一种基于博弈论组合赋权的模糊综合评价方法;在综合考虑输变电工程项目特点的基础上,依据项目全寿命周期理论建立综合评价指标体系;通过博弈论赋权的方法,组合改进AHP(层次分析法)求取的主观权重与熵值法求取的客观权重,得出一种均衡主客观结果的最优权重;结合模糊数学理论,建立模糊综合评价模型,并以该模型对湖南长沙地区某220 kV输变电工程进行评价,验证了该方法的科学性与可行性.     

一种基于中介真值程度度量的模糊综合评价方法

鉴于现有的综合评价方法大多为刚性的评价,不适用于模糊因素评价,提出了一种基于中介真值程度度量的模糊综合评价方法,对效益型指标、成本型指标和区间型指标3种情况下的真值程度度量过程和参数取值方法进行了设计;并基于真值程度度量结果构造了中介模糊评判矩阵来进行综合评价。 为处理模糊信息创建了中介数学系统。相比传统模糊评价方法,基于中介真值程度度量的模糊综合评价方法更具有客观性,因此能更有效地处理模糊现象。与其他综合评价方法的对比实验表明,所提出的方法是有效的,并具有一定的优越性。     

基于AHP的电力企业综合考评模型

为规范省级电力公司专业领域考评工作,提升考评管理水平和效率,提出了一种基于AHP的模糊综合考评模型. 根据考评对象、考评标准和考评流程设计一套专业领域考核指标体系,使用层次分析法确定各指标项的权重,应用模糊综合评价法建立动态指标考评模型,开发实现了一个面向电力企业信通专业的智能综合考评系统,可为省级电力公司各部门各专业提供全方位的自动化考评管理支持,为提升企业精细化管理水平提供有效依据.     

区域信息化水平评价方法研究

提出贯穿区域信息化全生命周期的信息化水平评价模型及体系,运用数据仓库模型分析特征数据,给出区域信息化水平多指标分层综合评价算法,建立模糊评价矩阵,设定九分位法权重系数,逐层依次计算得到信息化水平综合评价结果,并通过实例演示评价过程,证明了该方法的优越性和普遍适用性。     

Disassembly sequence planning using discrete Bees algorithm for human-robot collaboration in remanufacturing

Remanufacturing helps to improve the resource utilization rate and reduce the manufacturing cost. Disassembly is a key step of remanufacturing and is always finished by either manual labor or robots. Manual disassembly has low efficiency and high labor cost while robotic disassembly is not flexible enough to handle complex disassembly tasks. Therefore, human-robot collaboration for disassembly (HRCD) is proposed to flexibly and efficiently finish the disassembly process in remanufacturing. Before the execution of the disassembly process, disassembly sequence planning (DSP), which is to find the optimal disassembly sequence, helps to improve the disassembly efficiency. In this paper, DSP for human-robot collaboration (HRC) is solved by the modified discrete Bees algorithm based on Pareto (MDBA-Pareto). Firstly, the disassembly model is built to generate feasible disassembly sequences. Then, the disassembly tasks are classified according to the disassembly difficulty. Afterward, the solutions of DSP for HRC are generated and evaluated. To minimize the disassembly time, disassembly cost and disassembly difficulty, MDBA-Pareto is proposed to search the optimal solutions. Based on a simplified computer case, case studies are conducted to verify the proposed method. The results show the proposed method can solve DSP for HRC in remanufacturing and outperforms the other three optimization algorithms in solution quality.     

基于模糊综合评判法的高校网络安全评估

分析了目前影响高校网络安全的因素,建立了基于硬件、软件及外部环境的高校网络安全的评估指标体系,给出了基于多级模糊综合评价模型及其评价方法,并结合实例给出了使用评价模型进行网络安全评价的步骤和方法.评价结果表明,提出的方法具有可行性和指导性.     

基于犹豫模糊集的网络舆情突发事件应急群决策方法

考虑决策者在时间紧急及信息不完备的情况下给出评估值时可能会出现犹豫不决的情况,提出基于犹豫模糊集的网络舆情突发事件应急群决策法.首先,通过犹豫模糊信息熵及交叉熵建立各评价指标的权重确定模型;其次,利用犹豫模糊加权平均算子及得分函数计算各突发事件评价指标的犹豫模糊评估值得分;然后结合各评价指标的权重值及评估值得分,计算出各网络舆情突发事件综合危害性得分,进而辅助应急部门确定各网络舆情突发事件的处置顺序;最后通过案例分析证明了方法的有效性.     

TOPSIS定权的FCE法在物流供应商选择中的应用

根据汽车逆向物流供应链结构特点,构建了第三方汽车逆向物流供应商评价指标体系,创新性地提出了采用定量的TOPSIS法来确定二级指标的权重,采用定性的层次分析法(AHP)来确定一级指标的权重,构建了基于TOPSIS法与AHP法相结合定权的模糊综合评判法模型,给出了该模型的计算步骤。通过算例验证了该模型的可行性和该评价指标体系的实用性。     

Prediction of service life of large centrifugal compressor remanufactured impeller based on clustering rough set and fuzzy Bandelet neural network

In order to predict the service life of large centrifugal compressor impeller correctly, the rough set and fuzzy Bandelet neural network are combined to construct the novel prediction model which can give full play to theirs advantages. The attribute reduction algorithm based rough set and clustering method is firstly designed to optimize the inputting variables of fuzzy Bandelet neural network. And then the prediction model based on fuzzy Bandelet neural network is proposed, the Bandelet function is used as the excitation function of hidden layer and is combined with fuzzy theory to improve the prediction effectiveness of the prediction model. The training algorithm of fuzzy Bandelet neural network is designed based on improved genetic algorithm, the improved genetic algorithm introduces the adaptive differential evolution method into the traditional genetic algorithm, which can effectively optimize the parameters of fuzzy Bandelet neural network. Finally, the original 30 input variables of fuzzy Bandelet neural network are reduced to 9 input nodes based on rough set using 500 remanufacturing impellers as research objects. The service life of remanufacturing impeller is predicted based on three prediction models, and simulation results show that the fuzzy Bandelet neural network optimized by improved genetic algorithm has highest prediction precision and efficiency, which can correctly predict the service life of remanufacturing impeller.     

一种灰色模糊油田区块水驱指标评价模型

针对油田开发中的不确定因素,结合油藏开发因素中的定性化指标,提出了一种基于油田开发水驱指标的模糊综合评价模型.通过灰色关联法定量筛选影响油田开发水驱因素的九项指标,运用层次分析法确定了各指标的权重,结合模糊数学中的判断矩阵实现了油田各区块的模糊综合评价.此方法不仅可以对所有区块进行总体评价,还能对各个具体区块进行精细的评价,为油田开发与管理提供了有效的指导.最后,通过实际例子验证了模型的有效性以及可行性.     

基于改进的BOCR和FANP模型的ERP系统选择方法*

ERP系统能够明显影响企业未来的竞争力和企业绩效。ERP系统的选择是一个典型的多因素决策问题。基于BOCR理论建立了ERP系统选择的评价指标体系,考虑了准则/指标之间的相互影响和反馈关系。由于评价过程中信息的不精确和模糊性,用三角模糊数表示专家或决策者的偏好意见,根据模糊优先规划方法计算三角模糊判断矩阵的局部权重。依据准则/指标之间的网络结构关系建立了未加权超矩阵,计算了收敛后的极限超矩阵,以得出各指标的综合权重。最后以案例说明如何应用提出的方法。     

Neuro-fuzzy approach to forecast returns of scrapped products to recycling and remanufacturing

Forecasting of scrapped products to recycling poses severe problems to recycling and remanufacturing companies due to uncertainties in timing and quantities of returns. In this paper an extended forecasting method to provide prognoses for return values (amount and time) of scrapped products to recycling is presented. The suggested model is based on relevant influencing factors and product life cycle data and has been applied to a case study (photocopiers) for evaluation and usability. The approach employs a simulation study, the design of a fuzzy inference system for the forecasting of the return in a determined planning period and the design of a neuro-fuzzy system for the forecasting of return values with respect to time.     

炮弹制造质量综合评估方法研究

针对炮弹制造质量水平评判过程中存在的抽样试射成本高、难以评价批次炮弹质量、未有效利 用制造过程质量数据等问题,开展炮弹制造质量综合评估方法研究。首先研究炮弹制造质量综合评估框架,规 范炮弹制造质量综合评估过程;将失效模式分析方法确定的炮弹关键特性作为评估指标,构建覆盖炮弹采购、 加工、装配和检验等制造全过程的制造质量评估指标体系,并应用层次分析法(AHP)确定各指标的权重;采用 生产过程能力指数构建制造质量评估计算模型,基于加权和法与加权几何平均法构建制造质量评估聚合模型, 形成炮弹制造质量两层评估模型,充分利用制造过程中的关键特性质量检测数据实现炮弹制造过程质量的定量 评估。最后以某型炮射导弹为例,验证了炮弹制造质量综合评估方法的可操作性、可行性和有效性。     

基于模糊-两阶段超效率SBM的电网应急能力动态综合评价

首先,对电网应急能力进行基于时间维度和空间维度的拆解,构建基本电网应急能力评价指标体系;其次,考虑到指标评价标准具有动态性的问题,采用三角模糊数对指标进行量化处理,并建立可对多个模糊数集结的优化模型;然后,采用α-截集对三角模糊数进行处理并建立基于单一时间节点下的模糊-两阶段超效率SBM模型实现对电网应急能力的静态评价;最后,以时间维度拆解为基础,建立电网应急能力动态综合评价模型,实现对应急能力的整体评价、排序,使评价结果更加具有科学性和合理性.实例分析结果表明,所提出方法可有效区分应急能力仍需作出改善的区域,并在一定程度上克服评价标准需要动态变化的问题,具有较强的客观性.     

Optimal strategies for manufacturing/remanufacturing system with the consideration of recycled products

This paper analyzes the model of the recycled products which are considered with the minimum quality level in manufacturing/remanufacturing system. In this model, a constant demand is satisfied by manufacturing raw materials and remanufacturing recycled products which are up to the quality level. It is assumed that functions of recycling rate, buyback cost and remanufacturing cost are depend on the minimum quality level. The quality level of recycled products is set to be exponential distribution and then the model is established. The results show that when the buyback cost is low (the quality of the recycled products is low), the average total cost is low, though the remanufacturing cost is high. Namely, the companies are willing to recycle the used products with low quality level. Meanwhile, the optimal strategy of recycling, manufacturing and remanufacturing is investigated here. Through construction of a solution process on Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and numerical examples with sensitive analysis, the validity of the model has been proved.     

自抗扰控制技术在高低温环境模拟系统中的应用

针对现有健康状态评估方法主观性较强,准确度不高等问题,提出一种基于DSmT理论和模糊综合评判的健康状态评估模型。首先,确定评估对象的评价指标体系,对采集的原始数据进行预处理;然后利用模糊综合评判理论确定广义基本信度赋值;接着采用DSmT融合规则对广义基本信度赋值合成,得到健康状态等级。对于多级的系统评估,可将融合后的结果作为新的广义基本信度赋值进行DSmT融合。实例验证表明,该状态评估模型能够准确有效地实现对系统的健康状态评估,而且能够克服高冲突证据的融合问题,具有良好的应用价值。     

一种基于模糊评估分层模型的构件可测试性评价方法

分析了构件可测试性研究的情况,提出了一个基于模糊评估分层模型的构件可测试性评价方法。该方法按照模糊综合评价的原理建立指标集和评价集,并且提出了AHP, SCTFCE和MCTFCE 3个算法,以对构件的可测试性作出一个较准确的评估结果。仿真实验表明,这是一种有效的评价方法。