The Experts below are selected from a list of 699270 Experts worldwide ranked by ideXlab platform
Yi-kuei Lin - One of the best experts on this subject based on the ideXlab platform.
-
A stochastic model to study the System Capacity for supply chains in terms of minimal cuts
International Journal of Production Economics, 2010Co-Authors: Yi-kuei LinAbstract:Abstract For a single-commodity stochastic flow network, the System Capacity is the maximum flow from the source to the sink. We construct a p-commodity stochastic flow network with unreliable nodes, in which branches and nodes all have several possible capacities and may fail, to model a supply chain. Different types of commodities, transmitted through the same network simultaneously, consume the capacities of branches and nodes differently. That is, the Capacity weight depends on branches, nodes and types of commodity. We first define the System Capacity as a vector and propose a performance index, the probability that the upper bound of the System Capacity is a given pattern. Such a performance index can be easily computed in terms of upper boundary states meeting the demand exactly. An efficient algorithm based on minimal cuts is thus presented to generate all upper boundary states. The manager can apply this performance index to measure the transportation level of a supply chain.
-
System Capacity for a two-commodity multistate flow network with unreliable nodes and Capacity weight
Computers & Operations Research, 2007Co-Authors: Yi-kuei LinAbstract:The System Capacity of a single-commodity flow network is the maximum flow from the source to the destination. This paper discusses the System Capacity problem for a two-commodity multistate flow network composed of multistate components (edges and nodes). In particular, each component has both Capacity and cost attributes. Both types of commodity, which are transmitted through the same network simultaneously, consume the capacities of edges and nodes differently. That is, the Capacity weight varies with types of commodity, edges and nodes. We first define the System Capacity as a 2-tuple vector and then propose a performance index, the probability that the upper bound of the System Capacity is a given pattern subject to the budget constraint. Such a performance index can be easily computed in terms of upper boundary vectors. An efficient algorithm based on minimal cuts is thus presented to generate all upper boundary vectors. The manager can apply this performance index to measure the quality level of supply-demand Systems such as computer, logistics, power transmission, telecommunication and urban traffic Systems.
-
Using minimal cuts to study the System Capacity for a stochastic-flow network in two-commodity case
Computers & Operations Research, 2003Co-Authors: Yi-kuei LinAbstract:Abstract Traditionally, the flow network is assumed to allow a single type of commodity to transmit through it. The System Capacity is the maximum value of flow from the source to the sink. It is trivial that the System Capacity is fixed for a deterministic flow network. However, for a stochastic-flow network (the Capacity of each arc may have several possible values), the System Capacity is not fixed. Hence, many authors proposed methods to calculate two performance indices, the probability that the System Capacity is greater than d and the System Capacity is less than d, for a level d in terms of minimal paths and minimal cuts, respectively. In the case that two types of commodities are transmitted through the stochastic-flow network, this paper uses the properties of minimal cuts to define the System Capacity as a two-tuple vector and to propose an algorithm in order to evaluate a new performance index, the probability that the upper bound of System Capacity is (d1,d2), for a level (d1,d2). Scope and purpose The max-flow min-cut theorem states that for a deterministic flow network, the maximum value of the flow from s to t equals the minimum Capacity of all s–t minimal cuts. Hence, we can use the minimal cuts to deal with the flow problems in network analysis. This paper studies the stochastic-flow network in which two or more types of commodities are transmitted through it. From the point of view of quality management, it is an important issue to define the System Capacity and then to develop some indices in order to evaluate the System performance. The existing performance index, the probability that the lower bound of the System Capacity is (d1,d2), for a level (d1,d2) can be calculated in terms of minimal paths. The purpose of this paper is to use the properties of minimal cuts in order to define the System Capacity for such a network. Then to develop a new performance index, the probability that the upper bound of the System Capacity is (d1,d2), for a level (d1,d2) and to propose an algorithm to evaluate it. However, the two performance indices cannot convert into each other.
-
Study on the System Capacity for a multicommodity stochastic-flow network with node failure
Reliability Engineering & System Safety, 2002Co-Authors: Yi-kuei LinAbstract:Abstract For a deterministic flow network, the System Capacity in single-commodity case is the maximum value of flow from the source node to the sink node. In a multicommodity stochastic-flow network with node failure (arcs and nodes all have several possible capacities and may fail), different types of commodities are transmitted through the same network simultaneously and compete the capacities. This paper defines the System Capacity as a pattern for a multicommodity stochastic-flow network with node failure. And we propose a performance index, the probability that the upper bound of the System Capacity is a given pattern, to evaluate the performance for a System. An algorithm based on the properties of minimal cuts is proposed to evaluate such a performance index.
Pierre A. Humblet - One of the best experts on this subject based on the ideXlab platform.
-
System Capacity of f tdma cellular Systems
IEEE Transactions on Communications, 1998Co-Authors: Giuseppe Caire, Raymond Knopp, Pierre A. HumbletAbstract:We study the System Capacity of cellular Systems with time-division multiple access, slow time-frequency hopping (F-TDMA), and conventional single-user processing at the receivers. System Capacity is formally defined as the maximum of the product of the number of users per cell times the user spectral efficiency for a given maximum outage probability. We adopt an information-theoretic definition of outage as the event that the mutual information of the block-interference channel resulting from a finite number of signal bursts spanned by the transmission of a user code word falls below the actual code rate, because of fading, shadowing, and interference. Starting from this definition, we develop a general framework which naturally takes into account many different aspects of F-TDMA cellular Systems like channel reuse, channel utilization, waveform design, time-frequency hopping, voice activity exploitation, handoff, and power control strategies. Most importantly, our analysis does not rely on the choice of a particular coding scheme and can be applied to a very large class of Systems in order to find guidelines for Capacity-maximizing System design. A numerical example based on a typical urban mobile environment shows that there is a considerable Capacity gap between actual F-TDMA Systems and the limits predicted by our analysis. However, this gap can be filled by carefully designed (practical) Systems, which make use of conventional single-user processing and simple coded modulation schemes.
-
System Capacity of f tdma cellular Systems
Global Communications Conference, 1997Co-Authors: Giuseppe Caire, Raymond Knopp, Pierre A. HumbletAbstract:We study the System Capacity of cellular Systems with time-division multiple access and slow time-frequency hopping (F-TDMA). The System Capacity is defined as the number of users/cell/spl times/bit/s/Hz, for a given maximum outage probability. The outage is defined as the event that the mutual information of the interference channel falls below the actual code rate. We develop a general framework and a numerical example based on a typical urban mobile environment.
Caili Guo - One of the best experts on this subject based on the ideXlab platform.
-
Exploiting Polarization for System Capacity Maximization in Ultra-Dense Small Cell Networks
IEEE Access, 2017Co-Authors: Shuo Chen, Zhimin Zeng, Caili GuoAbstract:Ultra-dense deployment of small cell networks is widely regarded as a key role to meet the increasing demand for huge Capacity of wireless communication Systems. Meanwhile, network densification will result in severe inter-cell interference and thus impair System performance. Traditional radio resource management schemes pay attention to interference mitigation through resource allocation in time, frequency, space, and power domains, or a mix of them. In this paper, polarization, an important and underutilized property of electromagnetic waves, is exploited as a novel means to enhance System Capacity. We propose a multicell joint polarization, power and subcarrier allocation (MC-JPPSA) scheme to maximize System Capacity through joint optimization of transmitting polarization states, power, and subcarriers with an iterative approach. Though the iterative solution is suboptimal, simulation results demonstrate that MC-JPPSA scheme can strike a balance between performance and complexity compared with the optimal exhaustive search. Furthermore, the proposed scheme outperforms traditional joint power and subcarrier allocation schemes by means of exploiting polarization in ultra-dense small cell networks.
Jon Crowcroft - One of the best experts on this subject based on the ideXlab platform.
-
System Capacity Analysis for Ultra-Dense Multi-Tier Future Cellular Networks
IEEE Access, 2019Co-Authors: Syed Waqas Haider Shah, Adnan Noor Mian, Shahid Mumtaz, Jon CrowcroftAbstract:Ultra-dense multi-tier cellular networks have recently drawn the attention of researchers due to their potential efficiency in dealing with high-data rate demands in upcoming 5G cellular networks. These networks consist of multi-tier base stations including micro base stations with very high-System Capacity and short inter-site distances, overlooked by central macro base stations. In this way, network densification is achieved in the same area as that of traditional mobile networks, which offers much higher System Capacity and bandwidth reuse. This paper utilizes a well-known analytical tool, stochastic geometry for modeling and analyzing interference in ultra-dense multi-tier cellular networks. Primarily, we have studied different factors affecting the System Capacity including the network densification, cell load, and multi-tier interference. The role of the ergodic channel Capacity is also discussed. Moreover, the effects of channel interference, System bandwidth, and the network densification on the spectral and energy efficiencies of the network are observed. Finally, the results show that the network densification and the cell load have a profound impact on System performance as well as spectral and energy efficiencies of the networks.
Linda K. Nozick - One of the best experts on this subject based on the ideXlab platform.
-
Estimating Freight Transportation System Capacity, Flexibility, and Degraded-Condition Performance
Transportation Research Record: Journal of the Transportation Research Board, 2006Co-Authors: Yao Sun, Mark A. Turnquist, Linda K. NozickAbstract:This paper extends previous models of rail freight System Capacity and Capacity flexibility in three important ways. First, uncertainty in the future traffic pattern is included; second, volume-delay functions and a level-of-service constraint are added to represent deterioration of service quality as Capacity limits in individual facilities are approached; and third, a stochastic traffic assignment procedure is added to eliminate the need for path enumeration and to make the model more useful for large networks. These enhancements make the model suitable for use in the assessment of the performance of a freight network under conditions in which individual links or terminals have degraded Capacity. An illustration of the model as applied to the intermodal double-stack container network in the western United States indicates the improved estimates of Capacity and Capacity flexibility that the model provides.
-
Estimating Freight Transportation System Capacity, Flexibility, and Degraded-Condition Performance (With Discussion)
Transportation Research Record, 2006Co-Authors: Yao Sun, Mark A. Turnquist, Linda K. NozickAbstract:This paper extends previous models of rail freight System Capacity and Capacity flexibility in three important ways. First, uncertainty in the future traffic pattern is included; second, volume–delay functions and a level-of-service constraint are added to represent deterioration of service quality as Capacity limits in individual facilities are approached; and third, a stochastic traffic assignment procedure is added to eliminate the need for path enumeration and to make the model more useful for large networks. These enhancements make the model suitable for use in the assessment of the performance of a freight network under conditions in which individual links or terminals have degraded Capacity. An illustration of the model as applied to the intermodal double-stack container network in the western United States indicates the improved estimates of Capacity and Capacity flexibility that the model provides.
