Presence Of Bottleneck Research Articles

On 1-D PDE-Based Cardiovascular Flow Bottleneck Modeling and Analysis: A Vehicular Traffic Flow-Inspired Approach

We illustrate the potential of PDE-based traffic flow control in cardiovascular flow analysis, monitoring, and control, presenting a PDE-based control-oriented formulation, for 1-D blood flow dynamics in the presence of stenosis. This is achieved adopting an approach for modeling and analysis that relies on the potential correspondence of 1-D blood flow dynamics in the presence of stenosis, with 1-D traffic flow dynamics in the presence of bottleneck. We reveal such correspondence in relation to the respective (for the two flow types), speed dynamics and a (consistent with them) fundamental diagram-based reduction; bottleneck dynamic effects description and resulting boundary conditions; and free-flow/congested regimes characterization.

On 1-D PDE-Based Cardiovascular Flow Bottleneck Modeling and Analysis: A Vehicular Traffic Flow-Inspired Approach

We illustrate the potential of PDE-based traffic flow control in cardiovascular flow analysis, monitoring, and control, presenting a PDE-based control-oriented formulation, for 1-D blood flow dynamics in the presence of stenosis. This is achieved adopting an approach for modeling and analysis that relies on the potential correspondence of 1-D blood flow dynamics in the presence of stenosis, with 1-D traffic flow dynamics in the presence of bottleneck. We reveal such correspondence in relation to the respective (for the two flow types), speed dynamics and a (consistent with them) fundamental diagram-based reduction; bottleneck dynamic effects description and resulting boundary conditions; and free-flow/congested regimes characterization.

Quantum estimation through a bottleneck

We study the estimation of a single parameter characterizing families of unitary transformations acting on two systems. We consider the situation with the presence of bottleneck, i.e. only one of the systems can be measured to gather information. The estimation capabilities are related to unitaries' generators. In particular, we establish continuity of quantum Fisher information with respect to generators. Furthermore, we find conditions on the generators to achieve the same maximum quantum Fisher information we would have in the absence of bottleneck. We also discuss the usefulness of initial entanglement across the two systems as well as across multiple estimation instances.

Scalability-aware mechanism based on workload prediction in ultra-peer networks

Ultra-peers networks are emergent architectures in large-scale distributed computing environments. Controlling the workload is essential since the system scales up rapidly and accommodates a dynamic change in the number of users, resources, etc. Thus, developing an advanced model based on scalability aspects is a necessity, since it is an important and critical issue when designing such systems. In this paper, we present a scalability-aware approach for ultra-peers networks where each ultra-peer behaves like an ecosystem, in which, we prevent the presence of bottleneck in these structured information systems, and help each ultra-peer to find and stay in a steady state. We make use of neural networks in conjunction with the queueing theory to understand the behaviour of each ultra-peer, by first estimating its future workload, then to take decision on whether the next period will cause a bottleneck situation or not. After that we propose solutions to allow each ultra-peer to scale with the growth of the network size. The effectiveness of the design on scalability is evaluated using synthetic as well as realistic workloads for a number of different scenarios. Results show that the ultra-peer has successfully supervised its state to scale with the growth of the system size.

A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty

Cell-load variation is considered as a major shortcoming in cellular manufacturing systems. It can cause long queues in front of machines and impose extra costs to the cellular layouts. In this paper the impact of inflation on cell-load variation in cellular manufacturing systems is examined. For this purpose, a new method is proposed for scheduling dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim is finding the trade-off values between in-house manufacturing and using outsource services while system costs are not deterministic and may be varied from period to period by inflation. To solve the model, a hybrid genetic and simulated annealing algorithms is developed because of the high potential of outcomes to be trapped in the local optima. The results are then compared in LINGO® 12.0 software. In continue a Taguchi method (an L9 orthogonal optimization) is used to estimate parameters of the proposed method in order to solve experiments derived from literature. Our findings show that the condition of dynamic costs affects the routing of materials in process and may induce machine-load variation that yield to cell-load diversity. An increase in changing costs causes the loading level of each cell to vary, which in turn results in the development of “complex dummy sub-cells.” To measure the level of cell-load variation a new mathematical index is developed. Then, a new method is proposed for minimizing cell-load variation in the mentioned condition by using control lines. The results indicate that the proposed method can significantly reduce the level of cell-load variation in CMS.

A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost

In this paper, a new method is proposed for short-term period scheduling of dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to find best production strategy of in-house manufacturing and outsourcing in small and medium scale cellular manufacturing companies. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to be used in real industries. To solve the proposed problem, a number of metaheuristics are developed including Branch and Bound; Simulated Annealing algorithms; Fuzzy Art Control; Ant Colony Optimization and a hybrid Multi-layer Perceptron and Simulated Annealing algorithms. Our findings indicate that the uncertain condition of system costs affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. The results showed that the proposed method can significantly reduce cell load variation while finding the best trading off values between in-house manufacturing and outsourcing.

A new method for decreasing cell-load variation in dynamic cellular manufacturing systems

Cell load variation is considered a significant shortcoming in scheduling of cellular manufacturing systems. In this article, a new method is proposed for scheduling dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to control cell load variation during the process of determining the best trading off values between in-house manufacturing and outsourcing. A genetic algorithm (GA) is developed because of the high potential of trapping in the local optima, and results are compared with the results of LINGO ® 12.0 software. The Taguchi method (an L_9 orthogonal optimization) is used to estimate parameters of GA in order to solve experiments derived from literature. An in-depth analysis is conducted on the results in consideration of various factors, and control charts are used on machine-load variation. Our findings indicate that the dynamic condition of product demands affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. An increase in product uncertainty level causes the loading level of each cell to vary, which in turn results in the development of “complex dummy sub-cells”. The effect of the complex sub-cells is measured using another mathematical index. The results showed that the proposed GA can provide solutions with limited cell-load variations.

Optimizing a multi-stage production/inventory system by DC programming based approaches

This paper deals with optimizing the cost of set up, transportation and inventory of a multi-stage production system in presence of bottleneck. The considered optimization model is a mixed integer nonlinear program. We propose two methods based on DC (Difference of Convex) programming and DCA (DC Algorithm)--an innovative approach in nonconvex programming framework. The mixed integer nonlinear problem is first reformulated as a DC program and then DCA is developed to solve the resulting problem. In order to globally solve the problem, we combine DCA with a Branch and Bound algorithm (BB-DCA). A convex minorant of the objective function is introduced. DCA is used to compute upper bounds while lower bounds are calculated from a convex relaxation problem. The numerical results compared with those of COUENNE ( http://www.coin-or.org/download/binary/Couenne/ ), a solver for mixed integer nonconvex programming, show the rapidity and the ∈-globality of DCA in almost cases, as well as the efficiency of the combined DCA-Branch and Bound algorithm. We also propose a simple heuristic algorithm which is proved by experimental results to be better than an existing heuristic in the literature for this problem.

Pore Shape Affects Spontaneous Charge Redistribution in Small Pores

A complete understanding of charge redistribution (spontaneous movement of charge once the system has been placed on open-circuit) in porous materials is desirable because charge redistribution will affect the performance of devices incorporating porous electrodes. In this paper, a model pore based on the de Levie transmission line model is used to examine the effects of pore geometry on charge redistribution, including pore size, pore shape, and the presence of bottlenecks. Results highlight that pore geometry does affect the charge redistribution profile and emphasize the importance of the diameter of the pore mouth on charge redistribution. For cone, inverted cone, and cylindrically shaped pores having the same total resistance and capacitance, cone-shaped pores were able to accumulate the most charge. Results also indicate that the presence of a restrictive bottleneck beyond the pore mouth results in a charge redistribution profile with multiple slopes.

Diagnosis of capacity bottlenecks via passive monitoring in 3G networks: An empirical analysis

In this work we address the problem of inferring the presence of a capacity bottleneck from passive measurements in a 3G mobile network. The study is based on one month of packet traces collected in the UMTS core network of mobilkom austria AG & Co KG, the leading mobile telecommunications provider in Austria, EU. During the measurement period a bottleneck link in the UMTS core network was revealed and removed, therefore the traces enable the accurate analysis and comparison of the traffic behavior in the two network conditions: with and without a capacity bottleneck. Two approaches to bottleneck detection are investigated. The first one is based on the signal analysis of the marginal rate distribution of the traffic aggregate along one day cycle. Since TCP-controlled traffic dominates the overall traffic mix, the presence of a bottleneck strains the aggregate rate distribution and compresses it against the capacity limit during the peak hour. The second approach is based on the analysis of several TCP performance parameters, e.g. estimated frequency of retransmissions. Such statistics are unstable due to the presence of few top users, but this effect can be counteracted with simple filtering methods. Both approaches are validated via simulations. Our results show that both approaches can be used to provide early warning about future occurrences of capacity bottlenecks, and can complement other existing monitoring tools in the operation of a production network.