A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition

This paper proposes a multi-objective hybrid artificial bee colony (MOHABC) algorithm for service composition and optimal selection (SCOS) in cloud manufacturing, in which both the quality of service and the energy consumption are considered from the perspectives of economy and environment that are two pillars of sustainable manufacturing. The MOHABC uses the concept of Pareto dominance to direct the searching of a bee swarm, and maintains non-dominated solution found in an external archive. In order to achieve good distribution of solutions along the Pareto front, cuckoo search with Levy flight is introduced in the employed bee search to maintain diversity of population. Furthermore, to ensure the balance of exploitation and exploration capabilities for MOHABC, the comprehensive learning strategy is designed in the onlooker search so that every bee learns from the external archive elite, itself and other onlookers. Experiments are carried out to verify the effect of the improvement strategies and parameters’ impacts on the proposed algorithm and comparative study of the MOHABC with typical multi-objective algorithms for SCOS problems are addressed. The results show that the proposed approach obtains very promising solutions that significantly surpass the other considered algorithms.     

Cloud manufacturing service composition and optimal selection with sustainability considerations: a multi-objective integer bi-level multi-follower programming approach

The process of service composition and optimal selection (SCOS) is an important issue in cloud manufacturing (CMfg). However, the current studies on CMfg and SCOS have generally focused on optimising the allocation of resources against quality of service (QoS), in terms of e.g. cost, quality, and time. They have seldom taken the perspective of sustainability into discussion, although sustainability is indispensable in the CMfg environment. Addressing this gap, we aim to (1) propose a comprehensive method to assess the sustainability of cloud manufacturing (SoM) in terms of the economic, environmental, and social aspects; (2) establish a multi-objective integer bi-level multi-follower programming (MOIBMFP) model to simultaneously maximise SoM and QoS from the perspectives of both platform operator and multiple service demanders; and (3) design a hybrid particle swarm optimisation algorithm to solve the proposed MOIBMFP model. The experimental results show that the proposed algorithm is more feasible and effective than the typical multi-objective particle swarm optimisation algorithm when solving the proposed model. In other words, the proposed model and algorithm suggest better alternatives to meet the needs of the platform operator and service demanders in the CMfg environment.     

云制造中产品个性化定制研究综述

消费者对个性化产品购买需求的增加,对制造企业的定制生产能力提出了越来越高的要求。而现有制造企业存在的信息化水平不高、制造资源不足等问题,严重影响了制造企业向个性化生产转型的进程。针对上述情况,结合云制造概念及技术提出了云制造中产品个性化定制生产模式。为了更加深入地研究云制造中产品个性化定制的运行模式及阐述云制造平台在定制生产过程中的主要作用,从体系架构、服务流程、关键技术等方面对相关研究进行了回顾和介绍,然后列举了个性化定制生产模式在汽车、软件和服装行业的应用,最后对研究趋势进行了展望。分析表明,云制造平台汇聚全球的制造资源为个性化定制服务,能够有效解决单一制造企业实施个性化定制过程中面临的一系列困难,做到以较低的成本快速高效地满足消费者日益多样化、个性化的需求。     

Collaboration of large equipment complete service under cloud manufacturing mode

This paper focuses on the need of the large equipment manufacturing industry to adapt collaborative operation to transform the industry to cloud manufacturing services and to solve the new problem of federal resources coordination in complete service operation. We systematically study federal resources cooperation under cloud manufacturing mode to complete a large complex project. The primary research contents are divided into four points. First, a system structure of cloud manufacturing service mode is presented. Second, a synergy logic framework from the global system perspective is designed based on generalised partial global planning. Third, a multi-level system coordination mechanism is established by integrating various methods, including the bidding game mechanism for enterprise external resources, the planning control mechanisms for enterprise internal resource and the global collaborative optimisation mechanism for enterprise global federal resources. Finally, a cloud manufacturing service platform for a typical enterprise is developed by combining theory with practice. The results can realise collaborative management in resource selection and configuration, service process planning control and service information feedback in cloud manufacturing service, as well as achieve overall synergy effect for the system.     

TOPSIS在制造企业ASP平台选型中的应用

根据制造企业选择ASP服务提供商的业务流程,采用TOPSIS方法构造评价函数,建立了ASP平台软件系统评价指标体系;采用Delphi法确定评价指标的权重系数,求解评价函数最优解作为评价目标的最优解,最后举例进行了验证.结果表明,采用TOPSIS和Delphi相结合的方法对制造企业ASP平台软件系统进行选型,可以有效地减少ASP平台选型的主观盲目性,提高平台选型的科学性.     

基于信息熵多属性决策的物流供应商选择评价

生产商选择合适的物流供应商有利于降低营运成本,提高竞争优势.高效的第三方物流活动在增强企业核心竞争力、降低企业成本等方面起着重要作用.针对传统的熵值和熵权计算公式的不足,提出改进的熵权计算公式.同时把专家的主观权重和样本自身产生的熵权相结合,通过一定的方式混合加权作为不确定属性的权重.在此基础上结合多属性决策模型建立多属性决策评价方法.该方法将主、客观分析相结合,具有一定的优势：既减少了决策问题的主观性,又使得整个评价过程能够量化,有效解决了第三方物流供应商选择评价中的不确定多属性决策问题.最后通过实例分析证明该方法的可行性和有效性.     

Technical attribute prioritisation in QFD based on cloud model and grey relational analysis

Promptly development of new products can be achieved through quality function deployment (QFD) process, which is critical to companies’ survival. Since the multi-criteria decision-making problem involved in QFD, a novel method integrating cloud model and grey relational analysis is put forward in this paper. Taking into account the subjectivity and ambiguity in linguistic evaluations, some scholars utilise fuzzy theory, rough theory, interval-valued fuzzy-rough sets and MCDM methods to improve traditional QFD. However, much priori information requirements, inability to handle subjectivity and randomness, and lack of mechanism to overcome small sample size problem are some inevitable drawbacks in these methods. To solve these deficiencies, a hybrid methodology is proposed in this paper, integrating the fortes of cloud model in processing ambiguity and randomness, and the merits of grey relational analysis in overcoming small sample size error as well as revealing the inner correlations. The comparative analysis of different approaches as well as the sensitivity analysis of criteria weights is implemented to prove the stability of the novel method. The results obtained in this paper shows that the proposed method can be a practical tool for improving the efficiency and accuracy of traditional QFD in reality management.     

基于混合型模糊指标的云制造资源选择决策方法研究

针对云资源优选决策过程中存在定性指标模糊性,以及定量指标以区间性度量为代表的决策难点问题,提出了一种不确定性环境下基于OWA算子的混合型多指标云制造资源优势度决策方法。该方法首先运用标度法将不可量化的模糊性定性指标转化为区间数定量指标;其次利用基于相对优势度的区间数排序,结合OWA算子对云服务的优势度进行集结,得到云制造资源的综合优势度,给出云制造资源集合的优先排序,从而为用户选择出最需要的制造服务资源。通过在数控机床设备优选过程中的应用,验证了该方法的可行性和实用性。     

A novel model for optimisation of logistics and manufacturing operation service composition in Cloud manufacturing system focusing on cloud-entropy

In recent years, economic globalisation and manufacturing resource globalisation as two key factors have driven enterprises to transform their business processes to survive in competitive environments. This transformation is challenging as manufacturing enterprises should maintain their support for their customers with high-quality products, lower cost, product customisation capabilities, and quick delivery. Moreover, globalisation has resulted in geographically distributed suppliers across the globe. This challenge will turn into a major research topic when paradigms like Cloud manufacturing are introduced. Cloud manufacturing is a new paradigm which provides ubiquitous, convenient, on-demand network access to a shared pool of configurable manufacturing resources. In this paper, to achieve the ideal goal of Cloud manufacturing, the concept of supplier network logistics planning and manufacturing service composition is focused. Every production task and logistics operation would be defined as a service considering operation process chart flows. The paper has proposed a mathematical model which selects an optimal set of manufacturing and logistics service composition in order to lower operation and logistics costs in operational perspective while fulfilling a novel idea for configured cloud entropy of logistics and operation suppliers. Finally, the paper has presented a numerical example and concluded the remarks, and outlined future research.     

Design of delayed reconfigurable manufacturing system based on part family grouping and machine selection

To improve the convertibility of reconfigurable manufacturing system (RMS), the concept of delayed reconfigurable manufacturing system (D-RMS) was proposed. RMS and D-RMS are both constructed around part family. However, D-RMS may suffer from ultra-long system problem with unacceptable idle machines using generic RMS part families. Besides, considering the complex basic system structure of D-RMS, machine selection of D-RMS should be addressed, including dedicated machine, flexible machine, and reconfigurable machine. Therefore, a system design method for D-RMS based on part family grouping and machine selection is proposed. Firstly, a part family grouping method is proposed for D-RMS that groups the parts with more former common operations into the same part family. The concept of longest relative position common operation subsequence (LPCS) is proposed. The similarity coefficient among the parts is calculated based on LPCS. The reciprocal value of the operation position of LPCS is adopted as the characteristic value. The average linkage clustering (ALC) algorithm is used to cluster the parts. Secondly, a machine selection method is proposed to complete the system design of D-RMS, including machine selection rules and the dividing point decision model. Finally, a case study is given to implement and verify the proposed system design method for D-RMS. The results show that the proposed system design method is effective, which can group parts with more former common operations into the same part family and select appropriate machine types.     

Alternative production strategies based on the comparison of additive and traditional manufacturing technologies

Additive manufacturing technology has been evolving for several years. New material options, better processing speeds and greater autonomy are some of the characteristics of this technology that are still under research. However, in its current state, many commercially available 3D printers are competing with traditional manufacturing techniques in the fabrication of end-use products. In this paper, different additive manufacturing technologies are compared with injection moulding in a real-world case study. The comparison is conducted in terms of lead time and total production cost. From the case under study, it becomes obvious that none of the additive manufacturing technologies examined is yet able to practically replace injection moulding for medium- and high production volumes. However, when considering low-volume production, both rapid tooling and additive manufacturing may offer an alternative that could result into shorter lead times and decreased total production costs. In addition, the introduction of Additive Manufacturing in a producer’s production portfolio can increase flexibility, reduce warehousing costs and assist the company towards the adoption of a mass customisation business strategy.     

智慧停车场在线计量云平台分析与设计研究

为提高停车场电子计时收费装置的检定效率和解决其在技术监督管理现状中存在的问题,提出运用现代云计算理念和大数据技术,建立以在线监测和校准系统为核心应用的停车场智能管理系统公共监管和服务云平台。在可行性研究的基础上,提出该系统的设计架构;在需求分析的基础上,提出智慧停车场客户端单元、在线检测单元、信息发布单元和行政管理单元等功能模块的概要设计方案;详细描述云平台的核心应用——在线计量检测的实现原理与过程。该系统用互联网技术来实现停车场计时收费系统的在线计量检定,经实践证明其改变传统停车场电子计时收费系统的计量检测模式,根据云端虚拟服务器的网络并发响应能力,检测效率理论上可以提高数千倍;实现停车信息、安全、检测和监督等综合服务集成到平台上,最大限度为管理部门和公众提供多个层面的信息服务。     

Perturbation analysis for continuous and discrete flow models: a study of the delivery time impact on the optimal buffer level

In this paper, a manufacturing system composed of a single-product machine, a buffer and a stochastic demand is considered. Two models are presented: continuous and discrete flow models including constant delivery times, machine failures and random demands. The objective is to determine the value of the optimal buffer level, for a hedging point policy which minimises the total average cost function. The cost function is the sum of inventory, transportation and lost sales costs. Infinitesimal perturbation analysis is used for optimisation of the failure-prone manufacturing system. The trajectories of buffer level are studied for the continuous and discrete cases and the infinitesimal perturbation analysis estimators are evaluated. These estimators are shown to be unbiased and then they are implemented in an optimisation algorithm which determines the optimal buffer level in the presence of constant delivery time. Numerical results are presented for continuous and discrete flow models and then compared in order to evaluate the application of the infinitesimal perturbation analysis on the discrete flow model.     

基于物元分析与云模型的地下工程围岩稳定性评价

针对以往地下工程岩体质量评判过程中存在的模糊性与随机性问题,根据岩石质量指标、岩石单轴饱和抗压强度、岩体完整性系数、结构面强度系数和地下渗水量这5项指标建立地下工程围岩稳定性评价指标体系.基于物元分析法,通过计算专家评判矩阵的效度物元及合成权重物元来确定评价指标的重要性.根据确定的评价标准,得到各评价因子隶属于各级别的综合云模型,并针对广州抽水蓄能电站二期地下工程岩体实测数据,先通过正向云发生器计算各评价因子所对应的各个评价级别的隶属度,再与其权重结合得到合成隶属度,然后以最大隶属度为依据判别出岩体稳定性所属级别.将该方法所得评价结果与约简概念格-模糊优选评判法、粗糙集-逼近理想解的排序法(RS-TOPSIS)进行对比,三者判别等级一致,表明该方法准确可靠,且免除了对大量样本的学习过程,能够克服评价指标定性语言和定量数值相互转换所产生的模糊随机性,可以用于地下工程岩体稳定性的评判.     

Risk analysis of human error in interaction design by using a hybrid approach based on FMEA,SHERPA, and fuzzy TOPSIS

User experience is the focus of interaction design, and designing for errors is crucial for improving user experience. One method of designing for errors is to identify human errors and then initiate corrective actions on high-risk errors to reduce their adverse effects. In this study, we proposed a hybrid approach for risk analysis of human error concerning user experience of interactive systems. In this approach, systematic human error reduction and prediction approach (SHERPA) is first adopted to identify human error concerning user experience. Subsequently, failure mode and effect analysis (FMEA) is used to analyze the risk factors of the error, including occurrence, severity, and detection. Fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is then used to calculate the risk priority number to rank the errors. Finally, corrective actions for high-risk errors are recommended. An in-vehicle information system was used to demonstrate the proposed approach. The results indicated that the proposed approach can effectively analyze the risk of human error concerning user experience and be used as a universal reliability approach for improving user experience in interaction design.     

基于Web服务的PDC钻头井底流场分析平台研究

为解决常规PDC(polycrystalline diamond compact)钻头井底流场水力性能数值模拟分析时存在的门槛高、耗时长、重复工作量多等问题,建立了基于Web服务的PDC钻头井底流场分析平台。平台框架由上至下分为软件接口层、分析服务层和结果输出层。在服务端通过知识封装技术实现钻头井底流场分析过程的自动化、参数化和集成化;利用Web封装技术对参数化流场分析模板进行封装和管理,从而实现客户端基于Web服务的PDC钻头井底流场的分析研究。实例验证表明,采用该平台分析PDC钻头水力性能可有效降低CFD软件的使用门槛,提高钻头井底流场的分析效率,节约人力与物力,能够为石油装备企业提供规范化的技术支持。     

Geometric analysis of porous structures based on Hough Transform and Genetic Algorithms for additive manufacturing

Observed in nature, porous structures are used to create lightweight objects that have high structural integrity. Recently, porous freeform structures could be produced by additive manufacturing. However, the state-of-the-art computer-aided design tools do not yet enable engineers to incorporate porous structures in mechanical design. In this paper, a new approach is described, representing porous structures with circles or freeform curves. Using the Hough Transform method, a coarse geometric representation is computed. Using Genetic Algorithms on Bezier Curves, a fine geometric representation is found. The feasibility of these methods is demonstrated on synthetic images of composite materials and micro-CT images of bone. This representation can act as a base for additive manufacturing for complex scanned micro-structures.     

基于数字孪生与深度学习技术的制造加工设备智能化方法研究

针对传统制造加工设备在生产加工过程中存在设备与数据信息联系不紧密,设备使用维护多依赖于人工经验等问题,提出了一种新的设备智能化方法。首先,在信息层建立能反映制造加工设备真实状态的数字孪生体;其次,基于历史加工大数据,通过数字孪生体对加工过程的行为进行建模及深度学习和训练,并利用训练好的人工神经网络根据采集到的实时数据来预测制造加工设备下一时刻的状态,使制造加工设备实现物理层与信息层数据的深度融合,拥有自我感知、自我预测的能力,最终实现智能化;最后,以浆料微流挤出成型设备挤出结构系统的智能化实施过程为例,验证了所提出方法的可行性。实例结果表明该设备智能化方法可有效地对挤出结构系统的运行状态进行监测及预测,为后续提高挤出成型精度提供了有效的数据信息。研究表明数字孪生和深度学习技术能够提升制造加工设备的智能化程度,可为未来智能制造的发展提供理论支撑。     

基于FIM和SIM机制的移动通信业务融合问题解决方法研究

在业务特征模块化解决思路的基础上,对WIN（无线智能网）中提出的FIM（特征交互管理）和SIM（业务交互管理）功能实体进行了扩展,提出了交换机和SCP（业务控制点）中的业务特征模块调用机制,给出了用户同时申请FNS（亲情号码）、3PC（第三方计费）和CLIR（主叫号码屏蔽）三个业务情况下使用业务特征模块调用机制的模拟呼叫信令图。信令图表明,通过FIM和SIM机制的扩展,可以将多个功能上不冲突的业务组合在一起,解决业务融合问题。     

Cost and quality of service analysis of production systems based on the cumulative downtime

The paper presents a method for the analysis and design of industrial production systems based on a joint assessment of the cost and the quality of service. The operation of a production system is seen as the accomplishment of a sequence of missions, each one corresponding to the cost-effective production and delivery of a specified quantity of products within a specified time frame. The paper shows that the probability of successfully accomplishing a mission is a non-linear function of the cumulative production downtime and that this time cannot be obtained using conventional Markov based techniques. The paper also introduces an analytical model and a procedure that allows the density function of the downtime to be obtained and shows how, using these tools, the production costs and the quality of service may be assessed and related to the internal design of the shop floor. The method seems to be particularly valuable in the analysis of production systems integrated in just-in-time supply chains, in which the reliability of the deliveries is an outstanding requirement.