Reconfiguration optimization for manufacturing network in terms of reliability analysis

Complex networks of material flow in manufacturing and logistics: Modeling, analysis, and prediction using stochastic block models

Purpose– The topology of manufacturing systems is specified during the design phase and can afterwards only be adjusted at high expense. The purpose of this paper is to exploit the availability of large-scale data sets in manufacturing by applying measures from complex network theory and from classical performance evaluation to investigate the relation between structure and performance.Design/methodology/approach– The paper develops a manufacturing system network model that is composed of measures from complex network theory. The analysis is based on six company data sets containing up to half a million operation records. The paper uses the network model as a straightforward approach to assess the manufacturing systems and to evaluate the impact of topological measures on fundamental performance figures, e.g., work in process or lateness.Findings– The paper able to show that the manufacturing systems network model is a low-effort approach to quickly assess a manufacturing system. Additionally, the paper demonstrates that manufacturing networks display distinct, non-random network characteristics on a network-wide scale and that the relations between topological and performance key figures are non-linear.Research limitations/implications– The sample consists of six data sets from Germany-based manufacturing companies. As the model is universal, it can easily be applied to further data sets from any industry.Practical implications– The model can be utilized to quickly analyze large data sets without employing classical methods (e.g. simulation studies) which require time-intensive modeling and execution.Originality/value– This paper explores for the first time the application of network figures in manufacturing systems in relation to performance figures by using real data from manufacturing companies.

ABSTRACTIn the process of reliability analysis of computer network, there is the high probability of network failure in the application process by using the current analysis method. In this paper, a reliability analysis method of computer network based on intelligent cloud computing method is proposed. The proposed method is integrated with the theoretical idea of analytic hierarchy process. From the research angle of network reliability, the reliability model of computer network is set up by using the failure criterion of computer network, and the intelligent cloud computing method is used to calculate the reliability state parameters of the network. According to the obtained state parameters of network reliability analysis, the reliability analysis matrix of computer network is established, and then to accurately complete the reliability analysis of computer network. The simulation results show that the reliability analysis method of computer network based on intelligent cloud computing is highly accurate and robust.

Mission reliability model is important for evaluation of the production capability of a manufacturing system. The manufacturing system mission reliability refers to the ability that the manufacturing system completes the production mission under specified conditions and within the specified time. The production mission includes two factors: quality of products and the productivity. Calculation of traditional evaluation parameters like Cpk and Ppk excludes the abnormal interruption of production and inspection errors. For some manufacturing systems with low degree of automation in the field of domestic weapons, both production disruptions and misjudgments in inspection processes have an impact on the mission reliability of the manufacturing system. A method of mission reliability modeling of discrete manufacturing system is proposed in this paper. The manufacturing system is composed of several processes. Abnormal interruption of production, inspection errors and substandard quality parameters of the products are involved in the modeling of the process mission reliability. The sequential and concurrent relationship between the processes is also taken into account in the modeling process.

The reliability of energy internet is severely affected due to the instability of wind farms connection. Also, the traditional reliability modeling and evaluation methods do not take enough relevant factors into consideration, and the calculation method is relatively simple. Therefore, based on the VSC-MTDC system in multi-terminal flexible DC grid containing wind power, a reliability evaluation method for smart grid with wind power access and a reliability modeling method for wind farm that can simultaneously consider the uncertainty and correlation of wind speed and turbine failure are proposed. Then, a simulation model is calculated to optimize the power flow distribution and minimize the loss. Finally, by analyzing the results before and after the OPF, the effectiveness of the optimization method and correctness of the reliability model are verified, which provides basis for the further study on the reliability of smart grid.

Accurate and dynamic reliability modeling for the running manufacturing system is the prerequisite to implement preventive maintenance. However, existing studies could not output the reliability value in real time because their abandonment of the quality inspection data originated in the operation process of manufacturing system. Therefore, this paper presents an approach to model the manufacturing system reliability dynamically based on their operation data of process quality and output data of product reliability. Firstly, on the basis of importance explanation of the quality variations in manufacturing process as the linkage for the manufacturing system reliability and product inherent reliability, the RQR chain which could represent the relationships between them is put forward, and the product qualified probability is proposed to quantify the impacts of quality variation in manufacturing process on the reliability of manufacturing system further. Secondly, the impact of qualified probability on the product inherent reliability is expounded, and the modeling approach of manufacturing system reliability based on the qualified probability is presented. Thirdly, the preventive maintenance optimization strategy for manufacturing system driven by the loss of manufacturing quality variation is proposed. Finally, the validity of the proposed approach is verified by the reliability analysis and optimization example of engine cover manufacturing system.

As necessities in people's lives, the stability and quality safety of the production and circulation of agricultural products are widely of concern to the whole society. With the development of economic globalization, networking and informatization, the structure and dynamic behaviour of the agricultural product supply chain are becoming increasingly complex. A concept increasingly central to supply chain theory is sustainability. As the frontier of network science, complex network theory provides a new method to study complex real networks. This paper adopts the complex network theory to explore the formation and evolution mechanism of the agricultural product supply chain network and the risk propagation mechanism on the supply chain network through modelling and simulation. On this basis, it proposes the control strategy of risk propagation. The research in this paper can enrich the results of complex network theory in the field of supply chain based on ecological theory. The network model construction can reveal the formation and evolution mechanism of the agricultural product supply chain network. The analysis of risk dynamics can provide a theoretical basis and practical guidance for controlling risk propagation of agricultural product supply chain networks.

Manufacturing systems are complex networks of material flow. Complex network theory has been used as a descriptive and empirical research tool for various network types. However, due to the distinct origin of the various investigated networks (e.g., social networks, biological networks, the Internet), it is not clear if there is a meaningful application of network measures in manufacturing systems. This chapter investigates network modeling and network measures in manufacturing systems, and categorizes them according to their type of research.

ABSTRACTA framework for reliability and maintenance analysis of job shop manufacturing systems is proposed in this paper. An efficient preventive maintenance (PM) policy in terms of failure effects analysis (FEA) is proposed. Subsequently, reliability evaluation and component importance measure based on FEA are performed under the PM policy. A job shop manufacturing system is applied to validate the reliability evaluation and dynamic maintenance policy. Obtained results are compared with existed methods and the effectiveness is validated. Some vague understandings for issues such as network modelling, vulnerabilities identification, the evaluation criteria of repairable systems, as well as PM policy during manufacturing system reliability analysis are elaborated. This framework can help for reliability optimisation and rational maintenance resources allocation of job shop manufacturing systems.

Structural characteristics and spatiotemporal changes of a reticular river network based on complex network theory

We explore the relationship between a yield stress fluid flow in a Hele-Shaw cell with irregular walls, and a non-Darcy 2D porous medium flow with limiting pressure gradient. The continuum (Hele-Shaw) flow is a much-studied simplification of a cementing displacement flow model, solved via a variational formulation that leads to a convex streamfunction minimization problem. Our interest is with the analogy between the discretization used for the numerical solution and network flows that model the associated porous media flow, i.e. via the pore-throat approach. A staggered mesh, with pressure nodes at the cell corners and streamfunction nodes at the cell center is used for the continuum problem, which naturally separates into a network representation comprising primal and dual graphs, linking streamfunction and pressure nodes, respectively. We show explicitly how the continuum model defines a network model and vice versa. We develop the variational form of the network flow, including an appropriate (discrete) streamfunction minimization and a discrete version of the principle of virtual work. Two network models are explored, based on different interpretations of the minimization problem. The network flow results are compared with analogous computed continuum flow results in 3 specific geometries. We find that our network model I, which is the most natural interpretation of the continuum model as a network flow using our discretization, generally under-predicts flow rates. This is problematic from the perspective of considering the network flow as an approximation to the porous media or Hele-Shaw flow. Network model II rectifies this situation, via a pressure interpolation method. In our examples we find that the network II flow converges to the continuum flow as the mesh and network are refined. This is not the usual comparison made, as in many pore-throat models the network is fixed according to the underlying pore-space geometry. Despite the differences, both network models have their own advantages and disadvantages. Network model I offers a more natural way of modelling the flow and is easier to apply, for example to complex meshes, e.g. unstructured triangular. Network model II gives the more accurate physical representation of the Hele-Shaw flow. Lastly, we have developed approximate algebraic relationships for the total flow rate as a function of the total applied pressure gradient, both close to the critical onset pressure and for large pressure gradients. These correlations align well with previous findings for Bingham fluid flows in porous media. • Hele-Shaw cell – porous media analogy derived for Herschel-Bulkley fluids. • Discrete pore-throat network models derived. • Discrete principle of virtual work. • Convergence as network is refined. • Improved network model that gives convergence.

Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model

The paper presents an image-based network model of retinal vasculature taking account of the 3D vascular distribution of the retina. Mouse retinas were prepared using flat-mount technique and vascular images were obtained using confocal microscopy. The vascular morphometric information obtained from confocal images was used for the model development. The network model developed directly represents the vascular geometry of all the large vessels of the arteriolar and venular trees and models the capillaries using uniformly distributed meshes. The vasculatures in different layers of the retina, namely the superficial, intermediate and deep layer, were modelled separately in the network and were linked through connecting vessels. The branching data of the vasculatures was recorded using the method of connectivity matrix of network (the graph theory). Such an approach is able to take into account the detailed vasculature of individual retinas concerned. Using such network model, circulation analyses to predict the spatial distribution of the pressure, flow, hematocrit, apparent viscosity and wall shear stress in the entire retinal vasculature can be carried out.KeywordsMurine retinanetwork topologymorphological datanetwork modelspatial pressureflowratevelocitywall shear stress

This chapter introduces the concept of reliability modeling in processes using machinery. Reliability modeling methods are a proven way of predicting the availability and reliability of process systems made up of machinery and flow elements. The right design configuration for particular system depends on the cost of downtime, equipment, and maintenance. The chapter explores Raptor, a reliability modeling tool, to illustrate the benefits and tradeoffs of different machinery sparing configurations. Swapping frequencies for common, API centrifugal pumps, ranging from 50 to 500 horsepower, typically range from 30 days to up to 90 days. Every processing facility is different, but most facilities tend to base their swapping intervals based on the skill levels of their operators and their past experiences. Machinery users must weigh the cost of capital and maintenance with the value of process downtime. Redundancy only makes sense if the cost of additional capital is fully justified on its own merits.

Process reliability modeling is of great significance to guarantee the reliability of the manufacturing process. This paper will describe how to establish the relationship between reliability index of process system and input parameters of each working procedure by studying the key characteristics of manufacturing process, which pays more attention to the process instead of product. Firstly, the key process parameters in process will be determined, whose levels can be designated appropriately according to the requirements of process reliability index. Secondly, reliability modeling method will be employed to catch the significant factors affected the reliability of process, and the relationship between output parameters of product, input value of process characteristics and reliability index were analyzed based on key characteristics. Finally, this methodology is applied into manufacturing process reliability modeling of a certain type of product, and interrelated input parameters are selected to improve reliability index of the product.