Service and capacity allocation in M/G/c/c state-dependent queueing networks

The problem of service and capacity allocation in state-dependent M/G/c/c queueing networks is analyzed and algorithms are developed to compute the optimal allocation c. The model is applied to the modeling of pedestrian circulation systems and basic series, merge, and split topologies are examined. Also of interest are applications to problems of evacuation planning in buildings. Computational experiments assert the algorithm's speed, robustness, and effectiveness. The results obtained indicate that the pattern of the optimal capacity surprisingly repeats over different topologies and it is also heavily dependent upon the arrival rate. Additional computational simulation results are provided to show the accuracy of the approach in all configurations tested.     

Approximate analysis of M/G/c/c state-dependent queueing networks

Congestion is ever present in most practical situations. We describe a methodology for approximate analysis of open state-dependent M/G/c/c queueing networks in which the service rate is subject to congestion, that is, it is a function of the number of customers in the system. Important performance measurements are easily computed with high accuracy, such as the blocking probability, throughput, expected number of customers in the system (known also as work-in-process), and expected waiting time. The methodology forms a basic building block useful in many practical applications and contexts. Computational results demonstrate that the methodology provides accurate results in many topological configurations as well as in the analysis of network evacuation problems in high-rise buildings.     

The evaluation of pedestrians’ behavior using <Emphasis Type="Italic">M/G/C/C</Emphasis> analytical,weighted distance and real distance simulation models

M/G/C/C analytical and simulation models have long been used to evaluate the performance of a large and complex topological network. However, such evaluation is only founded on a network’s total arrival rate and its weighted distance. Thus, this paper discusses some concepts and issues in building an M/G/C/C simulation model of a complex geometric system where all its arrival sources and their exact distances to the end of their networks (i.e., corridors) have been taken into consideration in measuring the impacts of various evacuation rates to its throughput, blocking probability, expected service time and expected number of pedestrians. For this purpose, the Dewan Tuanku Syed Putra hall, Universiti Sains Malaysia, Malaysia has been selected as a case study for various evaluations of complex pedestrian flows. These results were analyzed and compared with the results of our analytical and weighted distance simulation models. We then documented the ranges of arrival rates for each of the model where their results exhibited significant discrepancies and suggest ideal rates to best evacuate occupants from the hall. Our model can be utilized by policy makers to recommend suitable actions especially in emergency cases and be modified to build and measure the performance of other real-life complex systems.     

On the use of Harrell’s C for clinical risk prediction via random survival forests

Random survival forests (RSF) are a powerful method for risk prediction of right-censored outcomes in biomedical research. RSF use the log-rank split criterion to form an ensemble of survival trees. The most common approach to evaluate the prediction accuracy of a RSF model is Harrell’s concordance index for survival data (‘C index’). Conceptually, this strategy implies that the split criterion in RSF is different from the evaluation criterion of interest. This discrepancy can be overcome by using Harrell’s C for both node splitting and evaluation. We compare the difference between the two split criteria analytically and in simulation studies with respect to the preference of more unbalanced splits, termed end-cut preference (ECP). Specifically, we show that the log-rank statistic has a stronger ECP compared to the C index. In simulation studies and with the help of two medical data sets we demonstrate that the accuracy of RSF predictions, as measured by Harrell’s C, can be improved if the log-rank statistic is replaced by the C index for node splitting. This is especially true in situations where the censoring rate or the fraction of informative continuous predictor variables is high. Conversely, log-rank splitting is preferable in noisy scenarios. Both C-based and log-rank splitting are implemented in the R package ranger. We recommend Harrell’s C as split criterion for use in smaller scale clinical studies and the log-rank split criterion for use in large-scale ‘omics’ studies.     

A new humanitarian relief logistic network for multi-objective optimization under stochastic programming

Millions of affected people and thousands of victims are consequences of earthquakes, every year. Therefore, it is necessary to prepare a proper preparedness and response planning. The objectives of this paper are i) minimizing the expected value of the total costs of relief supply chain, ii) minimizing the maximum number of unsatisfied demands for relief staff and iii) minimizing the total probability of unsuccessful evacuation in routes. In this paper, a scenario based stochastic multi-objective location-allocation-routing model is proposed for a real humanitarian relief logistics problem which focused on both pre- and post-disaster situations in presence of uncertainty. To cope with demand uncertainty, a simulation approach is used. The proposed model integrates these two phases simultaneously. Then, both strategic and operational decisions (pre-disaster and post-disaster), fairness in the evacuation, and relief item distribution including commodities and relief workers, victim evacuation including injured people, corpses and homeless people are also considered simultaneously in this paper. The presented model is solved utilizing the Epsilon-constraint method for small- and medium-scale problems and using three metaheuristic algorithms for the large-scale problem (case study). Empirical results illustrate that the model can be used to locate the shelters and relief distribution centers, determine appropriate routes and allocate resources in uncertain and real-life disaster situations.

     

Lessons from the evacuation of the world trade centre, 9/11 2001 for the development of computer-based simulations

This paper reviews the state-of-the-art in evacuation simulations. These interactive computer based tools have been developed to help the owners and designers of large public buildings to assess the risks that occupants might face during emergency egress. The development of the Glasgow Evacuation Simulator is used to illustrate the existing generation of tools. This system uses Monte Carlo techniques to control individual and group movements during an evacuation. The end-user can interactively open and block emergency exits at any point. It is also possible to alter the priorities that individuals associate with particular exit routes. A final benefit is that the tool can derive evacuation simulations directly from existing architects, models; this reduces the cost of simulations and creates a more prominent role for these tools in the iterative development of large-scale public buildings. Empirical studies have been used to validate the GES system as a tool to support evacuation training. The development of these tools has been informed by numerous human factors studies and by recent accident investigations. For example the 2003 fire in the Station nightclub in Rhode Island illustrated the way in which most building occupants retrace their steps to an entrance even when there are alternate fire exits. The second half of the paper uses this introduction to criticise the existing state-of-the-art in evacuation simulations. These criticisms are based on a detailed study of the recent findings from the 9/11 Commission (2004). Ten different lessons are identified. Some relate to the need to better understand the role of building management and security systems in controlling egress from public buildings. Others relate to the human factors involved in coordinating distributed groups of emergency personnel who may be physically exhausted by the demands of an evacuation. Arguably, the most important findings centre on the need to model the ingress and egress of emergency personnel from these structures. The previous focus of nearly all-existing simulation tools has been on the evacuation of building occupants rather than on the safety of first responders. Thanks are due to J. Appleby, P. Cooper, A. Foss, S. Hailey and B. Jenks who were responsible for the design and implementation of the GES application. They also drove the development of the Boyd Orr evacuation scenarios that are used to illustrate the opening sections of this paper.     

Stochastic network evacuation models

Stochastic network evacuation models are crucial for the safe evacuation of occupants from buildings. In this paper, we compare an analytical closed queueing network and a simulation model for analyzing the evacuation of occupants from a hospital. Evacuation times, arc congestion, and optimization of the evacuation routes and staff assigned to the evacuation network are included.     

Implementation of a C1 triangular element based on the P-version of the finite element method

The implementation of a computer code CONE (for C¹ continuity) based on the p-version of the finite element method is described. A hierarchic family of triangular finite elements of degree p ≥ 5 is used. This family enforces C¹-continuity across inter-element boundaries, and the code is applicable to fourth order partial differential equations in two independent variables, in particular to the biharmonic equation. Applications to several benchmark problems in plate bending are presented. Sample results are examined and compared both with theoretical predictions and with the computations of other programs. Significant improvements are shown for the results obtained using CONE.     

A BIM-based simulation framework for fire safety management and investigation of the critical factors affecting human evacuation performance

Fire hazards are a big threat to human life and property safety. The U.S. fire statistics reveal that, in 2017 alone, 1,319,500 fires caused 3400 deaths and 14,670 injuries, which resulted in a loss of $23 billion [1]. Effective evacuation planning in densely occupied buildings should be primarily put in place if both the number of injuries/fatalities and the level of property loss are to be minimized. However, it is not realistic, and is unethical to study human evacuation performance under a burning building. For this reason, computational tools tend to be the best approach for simulating fire growth as well as human response to fire hazards. This study aims to develop a BIM-based simulation framework that implements the Fire Dynamic Simulator (FDS) and agent-based modeling (ABM) for simulating fire growth and evacuation performance for different building layout scenarios. An experimental implementation is conducted to validate the proposed framework, which verified the benefits of (1) using BIM to offer a platform for conducting simulation design and visualizing the simulation results of (a) hazardous fire zones and (b) effective escape routes; (2) simulating fire growth using the FDS tool; (3) developing an agent-based model that accounts for the critical factors affecting evacuation performance; and (4) applying a statistical analysis for investigating the effects of influential parameters from the proposed model. As a result, the simulation outputs can be used to optimize the building design and to investigate the influential factors on human evacuation efficiency. The proposed framework contributes to building fire safety management by enabling to minimize both injuries/fatalities and property loss.     

Deciding k-colorability in expected polynomial time

For every fixed k?3 we describe an algorithm for deciding k-colorability, whose expected running time in polynomial in the probability space G(n,p) of random graphs as long as the edge probability p=p(n) satisfies p(n)?C/n, with C=C(k) being a sufficiently large constant.     

结合最短路径改进的社会力人群疏散仿真模型

社会力模型广泛应用于人群疏散仿真,针对该模型在仿真过程中存在行人停滞不前、无法通过非凸边形障碍物和疏散路径与行人实际选择的路径不相符等问题,提出了一种社会力改进模型。该模型基于场景中的障碍物生成路径节点,利用这些节点生成无向图,同时考虑了节点的安全系数和拥挤系数对节点通行性的影响生成最短疏散路径。通过改进后的社会力模型进行了多种场景的仿真实验,实验结果显示行人在复杂障碍物场景中能有效绕过障碍物,生成合理的疏散路径,表明该模型有效改善社会力模型,使人群疏散仿真更加真实。     

Cliques with maximum/minimum edge neighborhood and neighborhood density

Given a graph G=(V,E) and a clique C of G, the edge neighborhood of C can be defined as the total number of edges running between C and V?C, being denoted by N(C). The density of the edge neighborhood N(C) can be set as the ratio (|N(C)|/(|C|·|V?C|)).This paper addresses maximum/minimum edge neighborhood and neighborhood density cliques in G. Two versions will be undertaken, by fixing, or not, the size of the cliques.From an optimization point of view these problems do not bring much novelty, as they can be seen as particular or special versions of weighted clique problems. However, from a practical point of view, they concentrate on certain kinds of properties of cliques, rather than their size, revealing clique's engagement in the graph. In fact, a maximum edge neighborhood clique should be strongly embraced in the graph, while a minimum edge neighborhood clique should reveal an almost isolated strong component. In particular, special versions of the new problems allow to distinguish among cliques of the same size, namely among possible tied maximum cliques in the graph.We propose node based formulations for these edge based clique related problems. Using these models, we present computational results and suggest applications where the new problems can bring additional insights.     

Shear and membrane locking in curved C0 elements

Locking phenomena in C⁰ curved finite elements are studied for displacement, hybrid-stress and mixed formulations. It is shown that for a curved beam element, shear and membrane locking are interrelated and either shear or membrane underintegration can alleviate it. However, reduced shear integration tends to diminish the membrane-flexural coupling which characterizes curved elements. Locking can also be expected in certain types of mixed formulations, the hybrid-stress formulations avoids locking for beams (but not for shells). Methods for avoiding locking are explored and alternatives evaluated.     

Multi-Criteria optimisation using past,real time and predictive performance benchmarks

Performance based design, construction, commissioning and operation of buildings requires virtual testing and validation of project alternatives. In the case of environmental and energy management of buildings, whole Building Energy Simulation (BES) models can be used to determine indoor environmental conditions, building energy consumption, system performance, and associated CO₂ emissions, etc. BES is currently used at the design and commissioning phases of the Building Life Cycle (BLC) but not during the operational phase. This paper defines a methodology that incorporates predictive BES into building operation while acknowledging present technological limitations with respect to model accuracy and required resources. This predictive model also requires detailed definition and characterisation of inputs including: Historical data from buildings; Real time data such as measurements from meters and wired and wireless sensors underpinned by a Building Management System (BMS) and Future data such as short term weather forecast values and expected occupancy schedules. The paper concludes with a demonstration of the predictive BES model methodology using an existing building at University College Cork, Ireland.     

Robust and fair Multicast Congestion Control (M2C)

Since 1995 and the Receiver-driven Layered Multicast (RLM) protocol, numerous multicast congestion control protocols have been proposed, such as RLC, FLID-SL, FLID-DL and finally the WEBRC protocol. However these protocols suffer from some limitations and have not been extensively experimented on a wide range of network conditions. This article presents the Multicast Congestion Control (M2C) protocol, which is based on a dynamic layer mechanism and a congestion window mechanism for each receiver. M2C is designed to be TCP-friendly and to fairly share the bandwidth when competing with unicast and multicast streams.To reduce simulation approximations and to carry out extensive evaluations over the Internet we implemented M2C and several other multicast congestion control protocols. The experiments reveal interesting network behaviors, such as the join time latency problem, that simulations and local testbeds had not revealed before. We evaluated the impact of this latency on the state of the art protocols. Focusing on WEBRC and M2C, we have then defined and run a validation benchmark to qualify their behavior in terms of fairness and convergence time. These experiments show M2C robustness and good behavior for the mentioned metrics and its improvements compared to WEBRC. Furthermore, we have analyzed the signaling overhead of these protocols.Thus, M2C is an efficient multicast congestion control with an open source implementation which is easily usable by multicast applications, such as file transfer or video streaming.     

建筑物火灾中的人群疏散研究

建筑物火灾是我国频发的安全事故,所以应研究建筑物火灾人群安全疏散问题。由于在建筑物火灾中,人群疏散时出现拥堵,存在不安全因素,造成人员伤亡。针对在现有的研究中未考虑人员行为的影响,提出了智能体(Agent)的人群行为建模技术在建筑物火灾中的人群疏散仿真中的应用方法。仿真结果显示基于Agent的行为模型可以仿真出人员特性及决策过程对人群疏散的影响,弥补现有的人群疏散模型的不足。仿真结果证明,Agent的行为建模技术具有仿真火灾全过程中人员疏散行为的功能,适用于建筑物火灾中的人群优化疏散策略。     

Isoparametric C 0 interior penalty methods for plate bending problems on smooth domains

In this paper we develop isoparametric C ⁰ interior penalty methods for plate bending problems on smooth domains. The orders of convergence of these methods are shown to be optimal in the energy norm. We also consider the convergence of these methods in lower order Sobolev norms and discuss subparametric C ⁰ interior penalty methods. Numerical results that illustrate the performance of these methods are presented.     

M/G/c/c state dependent queuing model for a road traffic system of two sections in tandem

We propose in this article a M/G/c/c state dependent queuing model for road traffic flow. The model is based on finite capacity queuing theory which captures the stationary density-flow relationships. It is also inspired from the deterministic Godunov scheme for the road traffic simulation. We first present a reformulation of the existing linear case of M/G/c/c state dependent model, in order to use flow rather than speed variables. We then extend this model in order to consider upstream traffic demand and downstream traffic supply. After that, we propose the model for two road sections in tandem where both sections influence each other. In order to deal with this mutual dependence, we solve an implicit system given by an algebraic equation. Finally, we derive some performance measures (throughput and expected travel time). A comparison with results predicted by the M/G/c/c state dependent queuing networks shows that the model we propose here captures really the dynamics of the road traffic.     

The Trace Model: A model for simulation of the tracing process during evacuations in complex route environments

In emergency evacuations, not all pedestrians know the destination or the routes to the destination, especially when the route is complex. Many pedestrians follow a leader or leaders during an evacuation. A Trace Model was proposed to simulate such tracing processes, including (1) a Dynamic Douglas–Peucker algorithm to extract global key nodes from dynamically partial routes, (2) a key node complementation rule to address the issue in which the Dynamic Douglas–Peucker algorithm does not work for an extended time when the route is straight and long, and (3) a modification to a follower’s impatience factor, which is associated with the distance from the leader. The tracing process of pupils following their teachers in a primary school during an evacuation was simulated. The virtual process was shown to be reasonable both in the indoor classroom and on the outdoor campus along complex routes. The statistical data obtained in the simulation were also studied. The results show that the Trace Model can extract relatively global key nodes from dynamically partial routes that are very similar to the results obtained by the classical Douglas–Peucker algorithm based on whole routes, and the data redundancy is effectively reduced. The results also show that the Trace Model is adaptive to the motions between followers and leaders, which demonstrates that the Trace Model is applicable for the tracing process in complex routes and is an improvement on the classical Douglas–Peucker algorithm and the social force model.     

Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization

Modeling NO_x emissions from coal fired utility boiler is critical to develop a predictive emissions monitoring system (PEMS) and to implement combustion optimization software package for low NO_x combustion. This paper presents an efficient NO_x emissions model based on support vector regression (SVR), and compares its performance with traditional modeling techniques, i.e., back propagation (BPNN) and generalized regression (GRNN) neural networks. A large number of NO_x emissions data from an actual power plant, was employed to train and validate the SVR model as well as two neural networks models. Moreover, an ant colony optimization (ACO) based technique was proposed to select the generalization parameter C and Gaussian kernel parameter γ. The focus is on the predictive accuracy and time response characteristics of the SVR model. Results show that ACO optimization algorithm can automatically obtain the optimal parameters, C and γ, of the SVR model with very high predictive accuracy. The predicted NO_x emissions from the SVR model, by comparing with the BPNN model, were in good agreement with those measured, and were comparable to those estimated from the GRNN model. Time response of establishing the optimum SVR model was in scale of minutes, which is suitable for on-line and real-time modeling NO_x emissions from coal-fired utility boilers.