Analysis Of Stochastic Networks Research Articles

A probabilistic framework for post-disaster recovery modeling of buildings and electric power networks in developing countries

Post-disaster recovery is a significant challenge, especially in developing countries. Various technical, environmental, socioeconomic, political, and cultural factors substantially influence post-disaster recovery. As a result, methodologies relevant in developed nations may not be directly applicable in Global South contexts. This study introduces a probabilistic framework for modeling the post-disaster recovery of buildings and electric power networks (EPNs) in developing countries. The proposed framework combines an asset-level assessment of individual buildings with a community-level assessment of EPNs to evaluate a building portfolio's post-event functionality state. As part of the framework, a stochastic network analysis is proposed to estimate the recovery time of damaged buildings while accounting for technical, environmental, socioeconomic, political, and cultural factors, quantified using empirical data gathered from past events. A probabilistic modeling approach is proposed to quantify the EPN's initial post-event outage levels. Empirical formulations for estimating the recovery time of an EPN as a function of its initial post-event outage levels are calibrated using post-event data from developing countries. A case study is presented to illustrate the application of the proposed framework to model the post-earthquake recovery of a synthetic low-income residential community. The analysis shows that negative technical, environmental, socioeconomic, political, and cultural factors could amplify the reconstruction time of damaged buildings by a factor of almost three. The proposed framework can support decision-makers in disaster planning and management strategies for vulnerable low-income communities.

Social network analysis to understand the dynamics of global supply chains

PurposeThe aim of this study is to increase the understanding of collaborative relationships and assess according to the project size, the influence of the contributory factors in shaping collaboration network structure in projects developed in global supply chains (GSC).Design/methodology/approachThe paper used a case study methodology applied to eight global projects developed by an Austrian company leader in global market intra-logistics solutions and warehouse automation. The cases were studied by two approaches in network analysis. First, visual and descriptive analysis to describe structural aspects of the network. Second, stochastic network analysis to evaluate the influence of contributory factors in the structure of the collaboration network.FindingsThe results evidence that independently of the project size and project manager influence, project team roles (PTR) who have a reciprocal communication among other PTR tend to have a higher collaboration intensity (CI). Additionally, the results highlight the influence of the project manager in shaping the collaboration network in standard projects (STP) and small projects (SMP). According to the project size, the results show that the PTR that form complete triangles or cluster or who communicate frequently among each other tend to have a high CI, being more evident these tendencies in large-scale projects than STP and SMP.Originality/valueThis research provides a framework to identify the key actors and contributory factors in shaping collaborative relationships in GSC. The findings could be used to support the decision-making process and formulation strategies for effective collaborative relationship management in GSC.

Phylotranscriptomic patterns of network stochasticity and pathway dynamics during embryogenesis.

The hourglass model is a popular evo-devo model depicting that the developmental constraints in the middle of a developmental process are higher, and hence the phenotypes are evolutionarily more conserved, than those that occur in early and late ontogeny stages. Although this model has been supported by studies analyzing developmental gene expression data, the evolutionary explanation and molecular mechanism behind this phenomenon are not fully understood yet. To approach this problem, Raff proposed a hypothesis and claimed that higher interconnectivity among elements in an organism during organogenesis resulted in the larger constraints at the mid-developmental stage. By employing stochastic network analysis and gene-set pathway analysis, we aim to demonstrate such changes of interconnectivity claimed in Raff's hypothesis. We first compared the changes of network randomness among developmental processes in different species by measuring the stochasticity within the biological network in each developmental stage. By tracking the network entropy along each developmental process, we found that the network stochasticity follows an anti-hourglass trajectory, and such a pattern supports Raff's hypothesis in dynamic changes of interconnections among biological modules during development. To understand which biological functions change during the transition of network stochasticity, we sketched out the pathway dynamics along the developmental stages and found that species may activate similar groups of biological processes across different stages. Moreover, higher interspecies correlations are found at the mid-developmental stages. Supplementary data are available at Bioinformatics online.

The diversity bonus in pooling local knowledge about complex problems

Recently, theoreticians have hypothesized that diverse groups, as opposed to groups that are homogeneous, may have relative merits [S. E. Page, The Diversity Bonus (2019)]-all of which lead to more success in solving complex problems. As such, understanding complex, intertwined environmental and social issues may benefit from the integration of diverse types of local expertise. However, efforts to support this hypothesis have been frequently made through laboratory-based or computational experiments, and it is unclear whether these discoveries generalize to real-world complexities. To bridge this divide, we combine an Internet-based knowledge elicitation technique with theoretical principles of collective intelligence to design an experiment with local stakeholders. Using a case of striped bass fisheries in Massachusetts, we pool the local knowledge of resource stakeholders represented by graphical cognitive maps to produce a causal model of complex social-ecological interdependencies associated with fisheries ecosystems. Blinded reviews from a scientific expert panel revealed that the models of diverse groups outranked those from homogeneous groups. Evaluation via stochastic network analysis also indicated that a diverse group more adequately modeled complex feedbacks and interdependencies than homogeneous groups. We then used our data to run Monte Carlo experiments wherein the distributions of stakeholder-driven cognitive maps were randomly reproduced and virtual groups were generated. Random experiments also predicted that knowledge diversity improves group success, which was measured by benchmarking group models against an ecosystem-based fishery management model. We also highlight that diversity must be moderated through a proper aggregation process, leading to more complex yet parsimonious models.

Government as Network Catalyst: Accelerating Self-Organization in a Strategic Industry

AbstractGovernments have long-standing interests in preventing market failures and enhancing innovation in strategic industries. Public policy regarding domestic technology is critical to both national security and economic prosperity. Governments often seek to enhance their global competitiveness by promoting private sector cooperative activity at the inter-organizational level. Research on network governance has illuminated the structure of boundary-spanning collaboration mainly for programs with immediate public or nonprofit objectives. Far less research has examined how governments might accelerate private sector cooperation to prevent market failures or to enhance innovation. The theoretical contribution of this research is to suggest that government programs might catalyze cooperative activity by accelerating the preferential attachment mechanism inherent in social networks. We analyze the long-term effects of a government program on the strategic alliance network of 451 organizations in the high-tech semiconductor industry between 1987 and 1999, using stochastic network analysis methods for longitudinal social networks.

Forecasting the Project Duration Average and Standard Deviation from Deterministic Schedule Information

Most construction managers use deterministic scheduling techniques to plan construction projects and estimate their duration. However, deterministic techniques are known to underestimate the project duration. Alternative methods, such as Stochastic Network Analysis, have rarely been adopted in practical contexts as they are commonly computer-intensive, require extensive historical information, have limited contextual/local validity and/or require skills most practitioners have not been trained for. In this paper, we propose some mathematical expressions to approximate the average and the standard deviation of a project duration from basic deterministic schedule information. The expressions’ performance is successfully tested in a 4100-network dataset with varied activity durations and activity durations variability. Calculations are quite straightforward and can be implemented manually. Furthermore, unlike the Project Evaluation and Review Technique (PERT), they allow drawing inferences about the probability of project duration in the presence of several critical and subcritical paths with minimal additional calculation.

Heterogeneity and netting efficiency under central clearing: A stochastic network analysis

AbstractThis paper examines the effect of heterogeneity in clearing members’ exposure management practices under central clearing. Our network model specifies the dynamics of prenetted interbank exposures to shape interdependent exposure distributions beyond normality. Employing over‐the‐counter derivatives market data from the U.S. Office of the Comptroller of the Currency, our simulation results indicate that heterogeneity in bank‐to‐bank exposure dynamics is systemically desirable, while the entire system benefits more from the central clearing in more homogeneous environments. Policymakers should incentivize individual clearing members to enhance resiliency and stability in counterparty exposure management to maximize netting efficiency under central clearing.

Real-time manufacturing modeling and simulation framework using augmented reality and stochastic network analysis

While the development of augmented reality (AR) technologies has made it possible to assign real-time features to many systems and applications, these trends are rare in manufacturing modeling and simulation. This research study proposes a real-time manufacturing layout modeler and material flow simulator. The manufacturing devices of interest are positioned using AR labels, and the generated layout is converted into a stochastic Petri net model, where the validity of material flow and other criteria are checked. In order to overcome the limitations of the Petri net model and enhance analytical functionalities, stochastic network analyses are embedded into the framework. The layout model with greater uncertainty is analyzed, and manufacturing performance indicators such as cycle time, throughput, and work-in process are estimated. The proposed framework is not simply an integration of AR techniques and manufacturing simulations, but provides an efficient AR labeling architecture for large-scale manufacturing environments, and is suitable for a fast, real-time rendering. In order to verify the effectiveness of the proposed framework, real-time modeling and simulation examples were used as case studies. The results showed that the proposed system contributes to more accurate layout design and simulation analysis by using the embedded AR techniques and queuing network methods.

A critical-path focus for earned duration increases its sensitivity for project-duration monitoring and forecasting in deterministic, fuzzy and stochastic network analysis

A critical-path focus for earned duration increases its sensitivity for project-duration monitoring and forecasting in deterministic, fuzzy and stochastic network analysis

Bordering the area of spatial relevance for schools: a stochastic network approach using the example of Hamburg, Germany

Most approaches to spatial definitions of schooling markets are based on assumptions which may cause incorrect estimates. This paper presents stochastic network analysis as an alternative approach. Based upon individual student data of the metropolis Hamburg, the results are compared to those of traditional approaches. First, this article gives a short introduction to the current setting of Germany’s educational system, the national efforts for school improvement and the relevance of an adequate spatial definition of a market in this context. Subsequently, the applied method and the essential data structure are described. Following the identification of local and regional schooling markets in Hamburg by applying stochastic network analysis, the authors quantify meso- and small-scale competition amongst schools involved in the transition of students from primary to secondary schools in context of social composition.

Optimal Delays, Safe Floats, or Release Dates? Applications of Simulation Optimization in Stochastic Project Scheduling

Abstract In the field of project scheduling, the application of activity floats is often the subject of researches that seek to maximize the net present value (NPV) of project networks. However, NPV-based scheduling in stochastic projects may result in a conflict with the primary objective of scheduling, i.e. minimizing the makespan. The main objective of this study is to improve the financial gain while respecting the makespan (i.e. applying safe floats) in stochastic projects. Next, instead of the traditional use of float which prescribes a fixed delay in the start of activity in any condition, we use release dates as the by-product of above-mentioned method to reduce the variation in time and NPV of the project with less computational effort. The project models are simulated in SAS Simulation Studio. Response Surface Methodology (RSM) is used to design the experiments, interpret the results and predict the solution. The focus is on the temporal analysis of stochastic networks, and no resource constraint is considered.

Metastability, spectrum, and eigencurrents of the Lennard-Jones-38 network.

We develop computational tools for spectral analysis of stochastic networks representing energy landscapes of atomic and molecular clusters. Physical meaning and some properties of eigenvalues, left and right eigenvectors, and eigencurrents are discussed. We propose an approach to compute a collection of eigenpairs and corresponding eigencurrents describing the most important relaxation processes taking place in the system on its way to the equilibrium. It is suitable for large and complex stochastic networks where pairwise transition rates, given by the Arrhenius law, vary by orders of magnitude. The proposed methodology is applied to the network representing the Lennard-Jones-38 cluster created by Wales's group. Its energy landscape has a double funnel structure with a deep and narrow face-centered cubic funnel and a shallower and wider icosahedral funnel. However, the complete spectrum of the generator matrix of the Lennard-Jones-38 network has no appreciable spectral gap separating the eigenvalue corresponding to the escape from the icosahedral funnel. We provide a detailed description of the escape process from the icosahedral funnel using the eigencurrent and demonstrate a superexponential growth of the corresponding eigenvalue. The proposed spectral approach is compared to the methodology of the Transition Path Theory. Finally, we discuss whether the Lennard-Jones-38 cluster is metastable from the points of view of a mathematician and a chemical physicist, and make a connection with experimental works.

Binomial Moment Equations for Stochastic Reaction Systems

A highly efficient formulation of moment equations for stochastic reaction networks is introduced. It is based on a set of binomial moments that capture the combinatorics of the reaction processes. The resulting set of equations can be easily truncated to include moments up to any desired order. The number of equations is dramatically reduced compared to the master equation. This formulation enables the simulation of complex reaction networks, involving a large number of reactive species much beyond the feasibility limit of any existing method. It provides an equation-based paradigm to the analysis of stochastic networks, complementing the commonly used Monte Carlo simulations.

Estimating project and activity duration: a risk management approach using network analysis

Variations in the durations of activities are commonplace in the construction industry. This is due to the fact that the construction industry is influenced greatly by variations in weather, productivity of labour and plant, and quality of materials. Stochastic network analysis has been used by previous researchers to model variations in activities and produce more effective and reliable project duration estimates. A number of techniques have been developed in previous literature to solve the uncertain nature of networks, these are: PERT (program evaluation and review techniques), PNET (probabilistic network evaluation technique), NRB, (narrow reliability bounds methods) and MCS (Monte Carlo simulation). Although these techniques have proved to be useful in modelling variations in activities, dependence of activity duration is not considered. This can have a severe impact on realistically modelling projects. In this context, the objective of the present research is to develop a methodology that can accurately model activity dependence and realistically predict project duration using a risk management approach. A simulation model has been developed to encapsulate the methodology and run experimental work. In order to achieve this, the following tasks are tackled: identify risk factors that cause activity variations using literature reviews and conducting interviews with contractors; model risk factors and their influence on activity variations through conducting case studies and identifying any dependence between them; develop a computer based simulation model that uses a modified Monte Carlo technique to model activity duration and dependence of risk factors; and run experimental work to validate and verify the model.

The use of cutsets in Monte Carlo analysis of stochastic networks

Monte Carlo methods utilizing a new network concept, Uniformly Directed Cutsets (UDCs), are presented for analyzing directed, acyclic networks with probabilistic arc durations. The procedures involve sampling arc values for arcs not on a UDC and utilizing known probability information for arcs on a UDC. This approach results in less sampling effort and less associated variance than a straightforward simulation approach. A proof of this variance reduction is offered. The procedures provide estimates for project completion time distributions, criticality indices, minimum time distributions and path optimality indices. All of these network performance measures are useful to decision makers in project planning. Application areas include PERT-type network planning, equipment replacement analysis, reliability modeling, stochastic dynamic programming problems and maximal flow problems.

Stochastic network analysis with resource constraints, cost parameters, and queueing capabilities using gerts methodologies

The purpose of this paper is to describe the application of GERTS (Graphical Evaluation Review Technique Simulation) to stochastic network configurations with Resource Constraints Cost Parameters and Queueing characteristics. Specifically, the analysis programs GERTS III, GERTS III-R, GERTS III-Q, and GERTS III-QR will be explained with the emphasis on modelling characteristics, applicability and problem structure. General characteristics of the Gerts modelling logic and capabilities of GERTS III will be addressed, and specific attention will be given to the additional specialized structures of the analysis programs GERTS III-R, GERTS III-Q, and GERTS III-QR.

Flowgraph Techniques for Closed Systems

A two-port with both generator and load constitutes a closed system; it can be defined as a system with only interdependent variables or as a system containing no independent variables. The flowgraph describes the relationships between variables in a closed system as determined by the topology of its components. The corresponding closed flowgraph represents a zero-port network from which N-port networks can be generated as subsets by truncation of the closed system. A constraint governing any closed system expressing the properties of its cut-sets and tie-sets is referred to as the topology equation. The topology equation can be computed from the sum of loops in the closed flowgraph, and forms the key for the derivation of properties of closed systems and derived N-port subsets. Oscillators analyzed as zero-ports are discussed in detail to illustrate the rationale for the flowgraph approach to closed systems. To exploit the closed system model conceptually and computationally for N-ports, an approach is suggested and illustrated for N = 1 by an analysis of transfer functions and for N = 2 by a sensitivity analysis of stochastic networks.