Stochastic Networks (stochastic + network)

Distribution by Scientific Domains


Selected Abstracts


GPSPA: a new adaptive algorithm for maintaining shortest path routing trees in stochastic networks

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 10 2004
Sudip Misra
Abstract This paper presents a new efficient solution to the Dynamic Shortest Path Routing Problem, using the principles of Generalized Pursuit Learning. It proposes an efficient algorithm for maintaining shortest path routing trees in networks that undergo stochastic updates in their structure. It involves finding the shortest path in a stochastic network, where there are continuous probabilistically based updates in link-costs. In vast, rapidly changing telecommunications (wired or wireless) networks, where links go up and down continuously and rapidly, and where there are simultaneous random updates in link costs, the existing algorithms are inefficient. In such cases, shortest paths need to be computed within a very short time (often in the order of microseconds) by scanning and processing the minimal number of nodes and links. The proposed algorithm, referred to as the Generalized Pursuit Shortest Path Algorithm (GPSPA), will be very useful in this regard, because after convergence, it seems to be the best algorithm to-date for this purpose. Indeed, it has the advantage that it can be used to find the shortest path within the ,statistical' average network, which converges irrespective of whether there are new changes in link-costs or not. Existing algorithms are not characterized by such a behaviour inasmuch as they would recalculate the affected shortest paths after each link-cost update. The algorithm has been rigorously evaluated experimentally, and it has been found to be a few orders of magnitude superior to the algorithms available in the literature. Copyright © 2004 John Wiley & Sons, Ltd. [source]


Branching probabilities planning of stochastic network model using genetic algorithm supported by sensitivity analysis

ELECTRICAL ENGINEERING IN JAPAN, Issue 4 2008
Kenzo Kurihara
This paper proposes a new method of planning the project durations that are modeled as stochastic networks such as GERT networks. Since stochastic networks have a variable time and a branching probability for each arrow, the total duration of the network is also modeled as a variable. In order to complete the project by the desired date with a certain confidence, the variable times or branching probabilities of the network should be designed appropriately. We will propose a planning method for branching probabilities to realize the desired project duration using GA and Monte Carlo simulation. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(4): 43,53, 2008; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/eej.20582 [source]


Hydraulic pathways in the crystalline rock of the KTB

GEOPHYSICAL JOURNAL INTERNATIONAL, Issue 1 2000
Günter Zimmermann
Fracture systems and fluid pathways must be analysed in order to understand the dynamical processes in the upper crust. Various deterministic as well as stochastic fracture networks in the depth section of the Franconian Lineament (6900 to 7140 m), which appears as a brittle ductile shear zone and prominent seismic reflector, were modelled to simulate the hydraulic situation at the two boreholes of the Continental Deep Drilling Program (KTB). They led to estimations of the hydraulic permeability in crystalline rock. The geometrical parameters of the fractures, such as fracture locations and orientations, were determined from structural borehole measurements, which create an image of the borehole wall. The selection of potentially open fractures was decided according to the stress field. Only fractures with the dip direction (azimuth) of the fracture plane perpendicular to the maximum horizontal stress field were assumed to be open. The motivation for this assumption is the fact that the maximum horizontal stress is higher than the vertical stress from the formation, indicating that the state of stress is a strike-slip faulting. Therefore, the probability of open fractures due to this particular stress field at the KTB sites is enhanced. Length scales for fracture apertures and extensions were stochastically varied and calibrated by hydraulic experiments. The mean fracture aperture was estimated to be 25 ,m, assuming an exponential distribution, with corresponding permeability in the range of 10,16 m2. Similar results were also obtained for log-normal and normal distributions, with a variation of permeability of the order of a factor of 2. The influence of the fracture length on permeability of the stochastic networks was also studied. Decreasing the fracture length beyond a specific threshold of 10 m led to networks with vanishing connectivity and hence vanishing permeability. Therefore, we assume a mean fracture length exceeding the threshold of 10 m as a necessary assumption for a macroscopic hydraulically active fracture system at the KTB site. The calculated porosity due to the fracture network is of the order of 10,3 per cent, which at first sight contradicts the estimated matrix porosity of 1 to 2 per cent from borehole measurements and core measurements. It can be concluded from these results, however, that if the fluid transport is due to a macroscopic fracture system, only very low porosity is needed for hydraulic flow with permeabilities up to several 10,16 m2, and hence the contribution of matrix porosity to the hydraulic transport is of a subordinate nature. [source]


GPSPA: a new adaptive algorithm for maintaining shortest path routing trees in stochastic networks

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 10 2004
Sudip Misra
Abstract This paper presents a new efficient solution to the Dynamic Shortest Path Routing Problem, using the principles of Generalized Pursuit Learning. It proposes an efficient algorithm for maintaining shortest path routing trees in networks that undergo stochastic updates in their structure. It involves finding the shortest path in a stochastic network, where there are continuous probabilistically based updates in link-costs. In vast, rapidly changing telecommunications (wired or wireless) networks, where links go up and down continuously and rapidly, and where there are simultaneous random updates in link costs, the existing algorithms are inefficient. In such cases, shortest paths need to be computed within a very short time (often in the order of microseconds) by scanning and processing the minimal number of nodes and links. The proposed algorithm, referred to as the Generalized Pursuit Shortest Path Algorithm (GPSPA), will be very useful in this regard, because after convergence, it seems to be the best algorithm to-date for this purpose. Indeed, it has the advantage that it can be used to find the shortest path within the ,statistical' average network, which converges irrespective of whether there are new changes in link-costs or not. Existing algorithms are not characterized by such a behaviour inasmuch as they would recalculate the affected shortest paths after each link-cost update. The algorithm has been rigorously evaluated experimentally, and it has been found to be a few orders of magnitude superior to the algorithms available in the literature. Copyright © 2004 John Wiley & Sons, Ltd. [source]


A decomposition algorithm applied to planning the interdiction of stochastic networks

NAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 4 2005
Harald Held
Abstract We describe the application of a decomposition based solution method to a class of network interdiction problems. The problem of maximizing the probability of sufficient disruption of the flow of information or goods in a network whose characteristics are not certain is shown to be solved effectively by applying a scenario decomposition method developed by Riis and Schultz [Comput Optim Appl 24 (2003), 267,287]. Computational results demonstrate the effectiveness of the algorithm and design decisions that result in speed improvements. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005. [source]


State-space partition techniques for multiterminal flows in stochastic networks

NETWORKS: AN INTERNATIONAL JOURNAL, Issue 2 2006
Matthew S. Daly
Abstract This article develops state-space partition methods for computing performance measures for stochastic networks with demands between multiple pairs of nodes. The chief concern is the evaluation of the probability that there exist separate, noninteracting flows that satisfy all demands. This relates to the multiterminal maximum flow problem discussed in the classic article of Gomory and Hu. The network arcs are assumed to have independent, discrete random capacities. We refer to the probability that all demands can be satisfied as the network reliability (with the understanding that its definition is application dependent). In addition, we also consider the calculation of secondary measures, such as the probability that a particular subset of demands can be met, and the probability that a particular arc lies on a minimum cut. The evaluation of each of these probabilities is shown to be NP-hard. The proposed methods are based on an iterative partition of the system state space, with each iteration tightening the bounds on the measure of interest. This last property allows the design of increasingly efficient Monte Carlo sampling plans that yield substantially more precise estimators than the standard Monte Carlo method that draws samples from the original capacity distribution. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 48(2), 90,111 2006 [source]