Algorithms to Quantify Impact of Congestion on Time-Dependent Real-World Urban Freight Distribution Networks

2010 ◽  
Vol 2168 (1) ◽  
pp. 104-113 ◽  
Author(s):  
Ryan G. Conrad ◽  
Miguel Andres Figliozzi
Keyword(s):  
Real World ◽  
Distribution Networks ◽  
Time Dependent ◽  
Urban Freight

Performance Approximation for Time-Dependent Queues with Generally Distributed Abandonments

2021 ◽  
Author(s):  
Gregor Selinka ◽  
Raik Stolletz ◽  
Thomas I. Maindl
Keyword(s):  
Performance Measures ◽  
Real World ◽  
Queueing System ◽  
Time Dependent ◽  
Interval Length ◽  
Arrival Process ◽  
Real World Data ◽  
World Data ◽  
Extant Literature ◽  
Approximation Quality

Many stochastic systems face a time-dependent demand. Especially in stochastic service systems, for example, in call centers, customers may leave the queue if their waiting time exceeds their personal patience. As discussed in the extant literature, it can be useful to use general distributions to model such customer patience. This paper analyzes the time-dependent performance of a multiserver queue with a nonhomogeneous Poisson arrival process with a time-dependent arrival rate, exponentially distributed processing times, and generally distributed time to abandon. Fast and accurate performance approximations are essential for decision support in such queueing systems, but the extant literature lacks appropriate methods for the setting we consider. To approximate time-dependent performance measures for small- and medium-sized systems, we develop a new stationary backlog-carryover (SBC) approach that allows for the analysis of underloaded and overloaded systems. Abandonments are considered in two steps of the algorithm: (i) in the approximation of the utilization as a reduced arrival stream and (ii) in the approximation of waiting-based performance measures with a stationary model for general abandonments. To improve the approximation quality, we discuss an adjustment to the interval lengths. We present a limit result that indicates convergence of the method for stationary parameters. The numerical study compares the approximation quality of different adjustments to the interval length. The new SBC approach is effective for instances with small numbers of time-dependent servers and gamma-distributed abandonment times with different coefficients of variation and for an empirical distribution of the abandonment times from real-world data obtained from a call center. A discrete-event simulation benchmark confirms that the SBC algorithm approximates the performance of the queueing system with abandonments very well for different parameter configurations. Summary of Contribution: The paper presents a fast and accurate numerical method to approximate the performance measures of a time‐dependent queueing system with generally distributed abandonments. The presented stationary backlog carryover approach with abandonment combines algorithmic ideas with stationary queueing models for generally distributed abandonment times. The reliability of the method is analyzed for transient systems and numerically studied with real‐world data.

scholarly journals Technical note: Problem specific variators in a genetic algorithm for the optimization of drinking water networks

2018 ◽  
Author(s):  
Karel van Laarhoven ◽  
Ina Vertommen ◽  
Peter van Thienen
Keyword(s):  
Drinking Water ◽  
Real World ◽  
Water Distribution ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Technical Note ◽  
Practical Implementation ◽  
Utility Level ◽  
Water Utility ◽  
Drinking Water Distribution

Abstract. Genetic algorithms can be a powerful tool for the automated design of optimal drinking water distribution networks. Fast convergence of such algorithms is a crucial factor for successful practical implementation at the drinking water utility level. In this technical note, we therefore investigate the performance of a suite of genetic variators that was tailored to the optimisation of a least-cost network design. Different combinations of the variators are tested in terms of convergence rate and the robustness of the results during optimisation of the real world drinking water distribution network of Sittard, the Netherlands. The variator configurations that reproducibly reach the furthest convergence after 105 function evaluations are reported. In the future these may aid in dealing with the computational challenges of optimizing real world networks.

scholarly journals Hydropower Generation Through Pump as Turbine: Experimental Study and Potential Application to Small-Scale WDN

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 958 ◽  
Author(s):  
Matteo Postacchini ◽  
Giovanna Darvini ◽  
Fiorenza Finizio ◽  
Leonardo Pelagalli ◽  
Luciano Soldini ◽  
...  
Keyword(s):  
Real World ◽  
Water Demand ◽  
Rotational Speed ◽  
Water Distribution ◽  
Hydrodynamic Pressure ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Small Scale ◽  
Hydropower Generation ◽  
Reverse Mode

Pump-As-Turbine (PAT) technology is a smart solution to produce energy in a sustainable way at small scale, e.g., through its exploitation in classical Water Distribution Networks (WDNs). PAT application may actually represent a suitable solution to obtain both pressure regulation and electrical energy production. This technology enables one to significantly reduce both design and maintenance costs if compared to traditional turbine applications. In this work, the potential hydropower generation was evaluated through laboratory tests focused on the characterization of a pump working in reverse mode, i.e., as a PAT. Both hydrodynamic (pressure and discharge) and mechanical (rotational speed and torque) conditions were varied during the tests, with the aim to identify the most efficient PAT configurations and provide useful hints for possible real-world applications. The experimental findings confirm the good performances of the PAT system, especially when rotational speed and water demand are, respectively, larger than 850 rpm and 8 L/s, thus leading to efficiencies greater than 50%. Such findings were applied to a small municipality, where daily distribution of pressure and discharge were recorded upstream of the local WDN, where a Pressure Reducing Valve (PRV) is installed. Under the hypothesis of PRV replacement with the tested PAT, three different scenarios were studied, based on the mean recorded water demand and each characterized by specific values of PAT rotational speed. The best performances were observed for the largest tested speeds (1050 and 1250 rpm), which lead to pressure drops smaller than those actually due to the PRV, thus guaranteeing the minimum pressure for users, but also to mechanical powers smaller than 100 W. When a larger mean water demand is assumed, much better performances are reached, especially for large speeds (1250 rpm) that lead to mechanical powers larger than 1 kW combined to head drops a bit larger than those observed using the PRV. A suitable design is thus fundamental for the real-world PAT application.

scholarly journals Antagonistic One-To-N Stochastic Duel Game

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1114 ◽  
Author(s):  
Song-Kyoo (Amang) Kim
Keyword(s):  
Real World ◽  
Payoff Function ◽  
Marketing Strategies ◽  
Time Dependent ◽  
Random Time ◽  
Game Model ◽  
Explicit Formulas ◽  
Entry Timing ◽  
The One ◽  
Person Game

This paper is dealing with a multiple person game model under the antagonistic duel type setup. The unique multiple person duel game with the one-shooting-to-kill-all condition is analytically solved and the explicit formulas are obtained to determine the time dependent duel game model by using the first exceed theory. The model could be directly applied into real-world situations and an analogue of the theory in the paper is designed for solving the best shooting time for hitting all other players at once which optimizes the payoff function under random time conditions. It also mathematically explains to build the marketing strategies for the entry timing for both blue and red ocean markets.

scholarly journals Generating spatial precipitation ensembles: impact of temporal correlation structure

2012 ◽  
Vol 16 (9) ◽  
pp. 3419-3434 ◽  
Author(s):  
O. Rakovec ◽  
P. Hazenberg ◽  
P. J. J. F. Torfs ◽  
A. H. Weerts ◽  
R. Uijlenhoet
Keyword(s):  
Sensitivity Analysis ◽  
Real World ◽  
Temporal Correlation ◽  
Rain Gauge ◽  
Time Dependent ◽  
Spatial And Temporal Variability ◽  
Conditional Simulations ◽  
Hilly Region ◽  
Spatially Distributed ◽  
Precipitation Estimates

Abstract. Sound spatially distributed rainfall fields including a proper spatial and temporal error structure are of key interest for hydrologists to force hydrological models and to identify uncertainties in the simulated and forecasted catchment response. The current paper presents a temporally coherent error identification method based on time-dependent multivariate spatial conditional simulations, which are conditioned on preceding simulations. A sensitivity analysis and real-world experiment are carried out within the hilly region of the Belgian Ardennes. Precipitation fields are simulated for pixels of 10 km × 10 km resolution. Uncertainty analyses in the simulated fields focus on (1) the number of previous simulation hours on which the new simulation is conditioned, (2) the advection speed of the rainfall event, (3) the size of the catchment considered, and (4) the rain gauge density within the catchment. The results for a sensitivity analysis show for typical advection speeds >20 km h−1, no uncertainty is added in terms of across ensemble spread when conditioned on more than one or two previous hourly simulations. However, for the real-world experiment, additional uncertainty can still be added when conditioning on a larger number of previous simulations. This is because for actual precipitation fields, the dynamics exhibit a larger spatial and temporal variability. Moreover, by thinning the observation network with 50%, the added uncertainty increases only slightly and the cross-validation shows that the simulations at the unobserved locations are unbiased. Finally, the first-order autocorrelation coefficients show clear temporal coherence in the time series of the areal precipitation using the time-dependent multivariate conditional simulations, which was not the case using the time-independent univariate conditional simulations. The presented work can be easily implemented within a hydrological calibration and data assimilation framework and can be used as an improvement over currently used simplistic approaches to perturb the interpolated point or spatially distributed precipitation estimates.

scholarly journals Generating spatial precipitation ensembles: impact of temporal correlation structure

2012 ◽  
Vol 9 (3) ◽  
pp. 3087-3127 ◽  
Author(s):  
O. Rakovec ◽  
P. Hazenberg ◽  
P. J. J. F. Torfs ◽  
A. H. Weerts ◽  
R. Uijlenhoet
Keyword(s):  
Real World ◽  
Temporal Correlation ◽  
Rain Gauge ◽  
Time Dependent ◽  
Spatial And Temporal Variability ◽  
Conditional Simulations ◽  
Observation Network ◽  
Hilly Region ◽  
Spatially Distributed ◽  
Added Uncertainty

Abstract. Sound spatially distributed rainfall fields including a proper spatial and temporal error structure are of key interest for hydrologists to force hydrological models and to identify uncertainties in the simulated and forecasted catchment response. The current paper presents a temporal coherent error identification method based on time-dependent multivariate spatial conditional simulations, which are made further conditional on preceding simulations. Synthetic and real world experiments are carried out within the hilly region of the Belgian Ardennes. Precipitation fields are simulated for pixels of 10 × 10 km2 resolution. Uncertainty analyses in the simulated fields focus on (1) the number of previous simulation hours on which the new simulation is conditioned, (2) the advection speed of the rainfall event, (3) the size of the catchment considered, and (4) the rain gauge density within the catchment. The results for a synthetic experiment show for typical advection speeds of >20 km h−1, no uncertainty is added in terms of across ensemble spread when conditioned on more than one or two previous simulations. However, for the real world experiment, additional uncertainty can be still added when conditioning on a higher number of previous simulations. This is, because for actual precipitation fields, the dynamics exhibit a larger spatial and temporal variability. Moreover, by thinning the observation network with 50%, the added uncertainty increases only slightly. Finally, the first order autocorrelation coefficients show clear temporal coherence in the time series of the areal precipitation using the time-dependent multivariate conditional simulations, which was not the case using the time-independent univariate conditional simulations.

scholarly journals Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation

10.2196/19907 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e19907 ◽  
Author(s):  
Se Young Jung ◽  
Hyeontae Jo ◽  
Hwijae Son ◽  
Hyung Ju Hwang
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Influenza Pandemic ◽  
The United States ◽  
Sir Model ◽  
Time Dependent ◽  
Systems Science ◽  
Model Parameters ◽  
Viral Spread ◽  
Spread Model

Background The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend. Objective The aim of this study was to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning to respond promptly to the dynamic situation of the outbreak and proactively minimize damage. Methods In this study, we investigated a mathematical model with time-dependent parameters via deep learning based on forward-inverse problems. We used data from the Korea Centers for Disease Control and Prevention (KCDC) and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Because the data consist of confirmed, recovered, and deceased cases, we selected the susceptible-infected-recovered (SIR) model and found approximated solutions as well as model parameters. Specifically, we applied fully connected neural networks to the solutions and parameters and designed suitable loss functions. Results We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional Runge-Kutta fourth order model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from the KCDC, the Korean government, and news media. We also crossvalidated our model using data from the CSSE for Italy, Sweden, and the United States. Conclusions The methodology and new model of this study could be employed for short-term prediction of COVID-19, which could help the government prepare for a new outbreak. In addition, from the perspective of measuring medical resources, our model has powerful strength because it assumes all the parameters as time-dependent, which reflects the exact status of viral spread.

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