Arrival Time Uncertainty Research Articles

Real-Time Multiple-Workflow Scheduling in Cloud Environments

With the development of cloud computing, an increasing number of applications in different fields have been deployed to the cloud. In this process, the real-time scheduling of multiple workflows composed of tasks from these different applications must consider various influencing factors that strongly affect scheduling performance. This paper proposes a real-time multiple-workflow scheduling (RMWS) scheme to schedule workflows dynamically with minimum cost under different deadline constraints. Due to the uncertainty of workflow arrival time and specification, RMWS dynamically allocates tasks and divides the scheduling process into three stages. First, when a new workflow arrives, the latest start time and the latest finish time of each task are calculated according to the deadline, and the subdeadline of each task is obtained by probabilistic upward ranking. Then, each ready task is allocated according to its subdeadline and the increased cost of the virtual machine (VM). Meanwhile, only one waiting task can be assigned to each VM to reduce delay fluctuations. Finally, when the task is completed on the assigned VM, all the parameters of the relevant tasks are updated before allocating them to appropriate VMs. The experimental results based on four real-world workflow traces show that the proposed algorithm is superior to two state-of-the-art algorithms in terms of total rental cost, resource utilization, success rate and deadline deviation under different conditions.

Research on Ship Arrival Law Based on Route Matching and Deep Learning

Maritime transportation has always been the most important mode of transportation in international trade. With the deepening development of economic globalization, the scale of international trade, which is the core content of economic globalization, is also expanding, and the shipping industry is also developing greatly. In this paper, aiming at improving the operation efficiency of container terminals, AIS data is used as the research basis to predict the arrival time of ships and reduce the uncertainty of arrival time of ships, so as to provide support for the construction of smart ports. The transitive closure method based on equivalence relation is used to fuzzy cluster the routes to be matched and the historical routes used for matching, and the optimal route matching is realized by cutting set selection. At the same time, the navigation trajectory features based on AIS data are constructed, and the RNN-LSTM (Recurrent Neural Networks-Long Short-Term Memory) model is proposed by using the characteristics of deep learning and time series.The results show that the RNN-LSTM ship trajectory prediction model based on deep learning can achieve excellent prediction results and provide technical support for intelligent transportation at sea.

Improving the computational efficiency of first arrival time uncertainty estimation using a connectivity-based ranking Monte Carlo method

The first arrival time of a solute plume migrating from its source to an environmentally sensitive target is one of the key quantities of interest when assessing the risks of groundwater contamination. First arrival times are correlated with the hydraulic connectivity properties of spatially heterogeneous porous formations. Hydraulic connectivity leads to the presence of preferential flow paths which in turn control the transport dynamics of the leading edge of the solute plume and therefore first arrival times. In applications, these arrival times are subject to uncertainty given the lack of a detailed site characterization. The Monte Carlo method is commonly adopted to estimate the uncertainty of solute arrival times, however it leads to a computational burden. In this work, we build upon the existing knowledge regarding the correlation between connectivity and first arrival times to propose an innovative connectivity-based ranking Monte Carlo approach to quantify the uncertainty of first arrival times. The proposed method is tailored to predict first arrival times and allows to alleviate the computational costs when compared to the traditional Monte Carlo method. Our method consists of ranking the randomly generated spatially heterogeneous hydraulic conductivity fields according to their connectivity. The connectivity metric adopted is based on the concept of the minimum hydraulic resistance and can be obtained at a very low computational cost through the use of graph theory. We illustrate the methodology by analyzing the convergence rate of the first arrival time means and standard deviations. We compare the convergence rate of the first arrival times statistics obtained through the proposed methodology with those computed through the traditional Monte Carlo method. Overall, our results indicate the that the proposed methodology ensures a faster convergence of the considered quantities, thus reducing the time required for their estimation and the associated computational burden.

Scheduling ships with uncertain arrival times through the Kiel Canal

The Kiel Canal is a two-way waterway that connects the Baltic Sea and the North Sea. The canal consists of an alternating sequence of narrow transit segments and wider siding segments. This calls for solving a ship scheduling problem to decide which ships have to wait in sidings to let opposing traffic pass through such that the total traversing time of all ships is minimized. This paper extends previous studies on scheduling ships through the Kiel Canal by considering that the arrival times of the ships at the entrance to the canal are subject to uncertainty. This is a major challenge in the planning as it gives frequent need of replanning to make the schedules feasible. We propose a mathematical formulation for the problem to mitigate the negative effects of the uncertainty. This formulation incorporates time-corridors, so that the schedule will still be valid as long as the ships arrive within their given time-corridors. To solve real-sized instances of the problem, we adapt a matheuristic that adds violated constraints iteratively to the problem. The matheuristic was tested within a rolling horizon simulation framework to study the effect of arrival time uncertainty. We show by experiment that solutions of the matheuristic for different time-corridor widths can be used to identify a suitable corridor width that trades off the average traversing time of ships and the number of reschedules required in the planning. A simple myopic heuristic, reflecting the current scheduling practice, was used to generate benchmark results, and tests on real data showed that the matheuristic provides solutions with significantly less need of replanning, while at the same time keeping the total traversing times for the ships short. We also provide simulations to gain insight about the effect on the ships’ average traversing time from upgrading the narrow transit segments.

Generating Uncertainty Distributions for Seismic Signal Onset Times

ABSTRACTSignal arrival-time estimation plays a critical role in a variety of downstream seismic analyses, including location estimation and source characterization. Any arrival-time errors propagate through subsequent data-processing results. In this article, we detail a general framework for refining estimated seismic signal arrival times along with full estimation of their associated uncertainty. Using the standard short-term average/long-term average threshold algorithm to identify a search window, we demonstrate how to refine the pick estimate through two different approaches. In both cases, new waveform realizations are generated through bootstrap algorithms to produce full a posteriori estimates of uncertainty of onset arrival time of the seismic signal. The onset arrival uncertainty estimates provide additional data-derived information from the signal and have the potential to influence seismic analysis along several fronts.

Ensemble CME Modeling Constrained by Heliospheric Imager Observations

Predicting the arrival of coronal mass ejections (CMEs) is one key objective of space weather forecasting. In operational space weather forecasting, solar wind numerical models are used for this task and ensemble techniques are being increasingly explored as a means to improve these forecasts. Currently, these forecasts are not constrained by the available in situ and remote sensing observations, such as those from the heliospheric imagers (HIs) on the National Aeronautics and Space Administration's (NASA's) STEREO spacecraft, which record white‐light images of solar wind and CMEs. We report case studies of four CMEs and show how HI observations can be used to improve the skill and reduce the uncertainty of ensemble hindcasts of these events. Using a computationally efficient solar wind model, we produce 200‐member ensemble hindcasts, perturbing the modeled CME parameters within uniform distributions about the best estimates. By comparing the trajectory of the modeled CME flanks with HI observations, we compute a weight for each ensemble member. Weighting the ensemble distribution of CME arrival times improves the skill and reduces the hindcast uncertainty of each event. For these four events, the weighted ensembles show a mean reduction in arrival time error of 20.1 ± 4.1%, and a mean reduction in arrival time uncertainty of 15.0 ± 7.2%, relative to the unweighted ensembles. This technique could be applied in operational space weather forecasting, if real‐time HI observations were available. Therefore, as NASA and the European Space Agency are currently planning the next space weather monitoring missions, our proof‐of‐concept study provides some evidence of the potential value of including HIs on these missions.

Stochastic terminal flight arrival and departure scheduling problem under performance-based navigation environment

Flight arrival and departure scheduling (FAADS) problem, one of the critical tasks in terminal air traffic operation, faces new challenges as the terminal air traffic management has transformed to adapt to the performance-based navigation (PBN) environment; beside of that, the terminal system uncertainties which are usually due to the time-varying interference of the convective weather and flight arrival time will also exacerbate the difficulty to realize an efficient terminal air traffic operation. In order to effectively address the above issues, we propose the formulation and solution approach for a stochastic terminal FAADS problem under PBN environment. The proposed FAADS problem formulation combines the flexible 4-dimensional trajectory requirement under PBN environment and the stochastic quantifications both from the convective weather and flight arrival time uncertainties, which can finally make the FAADS results realize the avoidance of the convective weather and immunity of the flight arrival time variations within tolerable risk probabilities. We provide an efficient solution approach to tackle this problem with complex mixed integer and nonlinear programming basics and illustrate the capabilities of the solution approach by a test case on real terminal system in Shanghai Metroplex. Numerical results demonstrate the effectiveness of proposed problem formulation and solution approach.

Assessment of uncertainty in bid arrival times: A Bayesian mixture model

In this paper, we propose a Bayesian approach to model uncertainty in the bid arrival time by focusing on the time of the first bid in secondary (retail) market online business-to-business auctions. The proposed model is based on a Bayesian finite mixture of beta distributions. Our main objectives is to study potential heterogeneity of different auctions. In doing so, we incorporate some auction-specific features into the model and analyze their effect on the first bid time. We consider multiple competing models both in terms of fit and predictive performance. We also discuss managerial implications of the study and suggest how auctioneers can benefit from both the explanatory and predictive aspects of the model.

A Simulation-Based Planning Methodology for Decreasing Patient Waiting Times in Pure Walk-In Clinics

This article presents a simulation-based planning methodology that aims to improve patient service quality in pure walk-in clinics. Capacity planning is one of the major challenges in walk-in clinics because of the uncertainty in both patient demand and arrival times. This work presents a discrete-event simulation model for walk-in clinics that takes into consideration patient behavior in terms of arrival times for capacity planning at the clinic level. The goal of the model is to provide a tool that will allow clinics to develop protocols that will reduce patient waiting times by scheduling doctor and medical assistants considering demand uncertainties. A case study is presented to illustrate the benefits of the methodology. The results of the computational study show that by allocating the right number of resources at particular times of the day, walk-in clinics can achieve operational steady state while providing services to patients with minimum waiting times. The tool can be adapted and used to support any walk-in clinic.

Arrival-time picking uncertainty: Theoretical estimations and their application to microseismic data

Traveltime-based methods depend on the accurate determination of the arrival times of seismic waves. They further benefit from information on the uncertainty with which the arrival times are determined. Among other applications, arrival-time uncertainties are used to weight data in inversion algorithms and to define the resolution of reconstructed velocity models. The most physically meaningful approaches for the estimation of arrival-time uncertainties are based on probabilistic formulations. The two approaches for the assessment of the lower bound of arrival-time uncertainties, the Cramér–Rao Bound (CRB) and the Ziv–Zakai Bound (ZZB), have been reviewed. The CRB determines the minimum-achievable estimation error under the assumption of a high signal-to-noise ratio (S/N) but underestimates said error for small S/N. The ZZB provides a better result for noisy data because it utilizes a priori information. The CRB and ZZB require knowledge of the spectral variance of the signal, which often is hard to determine in seismic experiments. Furthermore, both bounds assume additive white Gaussian noise (AWGN), which does not hold for seismic data. To overcome these problems, alternative expressions have been proposed, which yield comparable estimates as CRB and ZZB but are solely based on the S/N and the dominant period in the data. Moreover, a recipe to correct the S/N and account for the difference between the seismic noise and AWGN has been provided. For a case study of downhole microseismic monitoring, it is determined that the new expressions provide station-dependent arrival-time uncertainties, which are used as weights to improve source location uncertainties.

Estimation of Hypocentral Parameters of Regional Earthquakes Using a Fuzzy Logic Approach

This study presents improvements on the hypocentral location method using the fuzzy logic approach proposed by Lin and Sanford (Bulletin of the Seismological Society of America 91:82–93, 2001) in resolving regional earthquakes location. In addition to trying to determine epicentral parameters of the earthquake, focal depths are also attempted utilizing this method in a three-dimensional (3D) space. In this study, instead of P- and S- wave models, a trapezoidal membership function constructed by the direct use of travel time difference between the two stations for P- and S- arrival times was utilized. In this procedure, it is not necessary to precisely know the velocity structure of the studied area in order to map the uncertainties in arrival-times into the fuzzy logic space. The location determination process was performed with the defuzzification of only the grid points having the membership value of 1 obtained by normalizing all the maximum fuzzy output values of the highest values which gives more reliable epicentral locations for both real and synthetic earthquakes. The results show that this method for epicentral estimation works well if the depth of the earthquake is sufficiently shallow and according to the distance between the event and seismic network. In this method of hypocenter determination, firstly, epicenter coordinates are determined, then the hypocenter location is found by considering only the epicentral area in 3D. In the case of very shallow events (e.g., h < 10 km), the estimation of the focal depth using this method seems to fail, causing faulty results in the hypocenter coordinates; however, the estimation of epicentral coordinates is successful in giving a more reasonable epicentral location. Otherwise, the obtained results would be reversed. Consequently, the deeper the focal depth, the better the hypocenter location, but a worse epicenter determination. This method is only valid in the hypocentral location of the earthquake occurring in the upper crust because only primarily the arrivals of Pg and Sg on the seismograms and a half-space velocity model are used. The hypocenter locations of some selected shallow earthquakes that occurred in the south east of the Aegean Sea have been determined by this method with results that are highly comparable with those obtained by other seismic agencies.

Implementation of Filter Picker Algorithm For Aftershock Identification of Lombok Earthquake 2018

The ability to identify earthquake events that are consistent, efficient and accurate is increasingly needed along with the increase in the amount of data analyzed. In this paper a filter picker algorithm is implemented to identify aftershock events and determination of arrival time automatically, especially for the P wave phase. Here modifications are made in determining the uncertainty of arrival time and there are additional criteria in determining the time of arrival used. The additional criteria are that in a certain time span, there are at least 5 stations determined by the time the filter picker arrives. This is done to minimize identification errors due to local noise and other practical reasons, namely the minimum number of stations to determine the location and other seismological analysis. To test the filter picker algorithm, aftershock data from the Lombok earthquake occurred on July 29 (M 6.4), August 5 (M 7), and August 19 (M 6.3 and M 6.9) 2018. The aftershock data were used for 30 days, from August 4, 2018 to September 4, 2018 using local seismic station in Lombok Island. The results of the filter picker algorithm were evaluated by comparing the number of earthquake events detected and the accuracy of determining the P wave arrival time automatically to the results of manually arriving time. In addition, a comparison of the results obtained from a broadband type seismometer with a short period is used to find out how much influence the type of tool has on its performance results. The results of the comparison with the manual arrival time show that more than 85 percent of the results of the automatic arrival time have a difference below 0.2 seconds. Therefore, it can be said that the filter picker algorithm is quite effective for identifying events and determining the arrival time of P waves. In this paper it is also shown that this algorithm can be used for broad band and short period seismometer sensor, even without the prior correction of instruments.

Optimal charging of Electric Vehicles in residential area

Abstract Uncoordinated Electric Vehicles (EVs) charging can lead to incremental overloads, power losses and voltage fluctuations which are stressful and harmful for the distribution networks. To overcome these consequences, using EVs charging strategies is becoming of tremendous importance. We propose in this paper a new approach aiming at minimizing the EVs charging cost based on the day-ahead electricity price (DAEP) and battery degradation cost subject to the EVs state of charge (SOC) limits, the EVs maximum power charger, the EVs batteries full charging at the end of the charging period and the distribution feeder subscribed power. Besides, to deal with the EVs arrival and departure time uncertainties, Monte Carlo Simulations (MCS) have been applied based on the probability density functions of these parameters, while the EV’s initial SOC uncertainties are estimated based on their daily mileage. Finally, to show the efficiency of the proposed approach, a single phase Low Voltage (LV) distribution network in a residential area has been deployed with an EVs penetration rate of 50% and 100%. In this study, the optimization problem is solved using linear programming method. The results show that the proposed approach allows to reduce the EVs charging cost by 50% and 38% for 100% and 50% of EVs penetration rate respectively compared to uncoordinated EVs charging.

Minimum Hydraulic Resistance Uncertainty and the Development of a Connectivity‐Based Iterative Sampling Strategy

AbstractThe presence of well‐connected paths is commonly observed in spatially heterogeneous porous formations. Channels consisting of high hydraulic conductivity (K) values strongly affect fate and transport of dissolved species in the subsurface environment. Several studies have established a correlation between connectivity properties of the spatially variable K‐field and solute first arrival times. However, due to limited knowledge of the spatial structure of the K‐field, connectivity metrics are subject to uncertainty. In this work, we utilize the concept of the minimum hydraulic resistance and least resistance path to evaluate the connectivity of a K‐field in a stochastic framework. We employ a fast graph theory‐based algorithm to alleviate the computational burden associated with stochastic computations in order to investigate both the impact of the hydrogeological structural conceptualization and domain dimensionality (2‐D vs. 3‐D) on the uncertainty of the minimum hydraulic resistance. Finally, we propose an iterative data acquisition strategy that can be utilized to identify the least resistance path (which is linked to preferential flow channels) in real sites. A synthetic benchmark test is presented, showing the advantages of the proposed sampling strategy when compared to a regular sampling strategy. By using the iterative data sampling strategy, we were able to reduce first arrival time uncertainty by 47% (when compared to the regular sampling strategy), while maintaining site characterization efforts constant.

Knotted-line: A Visual explorer for uncertainty in transportation system

Abstract Public transport system(PTS) is a complex system, and there are various uncertainties caused by traffic congestion, varying departure interval of buses, etc. In this paper, taking bus as an example, a novel visualization model knotted-line for transportation system uncertainties is proposed. The knotted-line visualization model consists of three parts, the violin plot, multi-layer ring and risk indicator. The violin plot shows the uncertainty of passengers’ waiting time. The multi-layer ring describes the uncertainty of bus arrival time. The risk indicator expresses the possibility of passenger spending time on the trip. A user study is conducted to evaluate the proposed visualization method. The results show that the proposed knotted-line visualization model is helpful for passengers to understand the uncertainty in transportation system and help users to make decisions.

The NANOGrav 12.5 yr Data Set: The Frequency Dependence of Pulse Jitter in Precision Millisecond Pulsars

Abstract Low-frequency gravitational-wave experiments require the highest timing precision from an array of the most stable millisecond pulsars. Several known sources of noise on short timescales in single radio pulsar observations are well described by a simple model of three components: template fitting from a finite signal-to-noise ratio, pulse phase/amplitude jitter from single-pulse stochasticity, and scintillation errors from short-timescale interstellar scattering variations. Currently template-fitting errors dominate, but as radio telescopes push toward higher signal-to-noise ratios, jitter becomes the next dominant term for most millisecond pulsars. Understanding the statistics of jitter becomes crucial for properly characterizing arrival time uncertainties. We characterize the radio frequency dependence of jitter using data on 48 pulsars in the North American Nanohertz Observatory for Gravitational Waves timing program. We detect significant jitter in 43 of the pulsars and test several functional forms for its frequency dependence; we find significant frequency dependence for 30 pulsars. We find moderate correlations of rms jitter with pulse width (R = 0.62) and number of profile components (R = 0.40); the single-pulse rms jitter is typically ≈1% of pulse phase. The average frequency dependence for all pulsars using a power-law model has index −0.42. We investigate the jitter variations for the interpulse of PSR B1937+21 and find no significant deviations from the main pulse rms jitter. We also test the time variation of jitter in two pulsars and find that systematics likely bias the results for high-precision pulsars. Pulsar timing array analyses must properly model jitter as a significant component of the noise within the detector.

Developing a robust optimization model for seaside operations in container terminal under uncertainty environment

This paper discusses the robust optimization model development to integrate tactical decisions on seaside operations in container terminal, that is consist of Tactical Berth Allocation Problem and Specific Quay Crane Assignment Problem by considering the uncertainty of vessel arrival time and the number of containers to be discharged and loaded into the arrived vessels, which also affects the uncertainty of handling time required. The development of optimization model considers container terminal managers and shipping liner owners viewpoints. Thus, this optimization model has two objective functions, i.e. minimizing total costs of seaside operations and maximizing service levels for shipping liners. Since these two tactical decisions for seaside operations under uncertainty conditions are inter-related each other, the robust optimization model is solved by using exact method, which is applied to a numerical example in the small-scale problem for testing the effectiveness of the proposed method.

Application of RFID in Chemical Storage and Logistics Management

According to the object-oriented analysis method, the demand analysis of intelligent monitoring system for hazardous chemicals logistics based on RFID and WSN is carried out, which analyzes the essential use cases of the system, the performance requirements for system design and other requirements. Also, the functional framework and module of the software system and the logical frame of the hardware system are constructed. The composition and structure and the internal logical relationship are introduced; the intelligent decision-making function module is introduced and the overall structure is given, which highlights the key role and composition of the model base and analyzes the core issues of the intelligent decision-making of hazardous chemicals logistics. The warehouse distribution method of hazardous chemicals based on rolling time domain optimization strategy is studied. Considering the drive of the uncertainty of transportation process and arrival time and for the warehouse storage capacity, the storage and distribution model of hazardous chemicals is established based on rolling time domain optimization strategy with the goal of maximizing warehouse utilization efficiency under the constraints of the limited storage conditions of hazardous chemicals, storage capacity of the target warehouse, external environment of the warehouse, warehousing and transportation conditions to solve the problem identification of target warehouse and the distribution of storage capacity in the storage and distribution of hazardous chemicals.

Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil

Stochastic upscaling of flow and reactive solute transport in a tropical soil is performed using real data collected in the laboratory. Upscaling of hydraulic conductivity, longitudinal hydrodynamic dispersion, and retardation factor were done using three different approaches of varying complexity. How uncertainty propagates after upscaling was also studied. The results show that upscaling must be taken into account if a good reproduction of the flow and transport behavior of a given soil is to be attained when modeled at larger than laboratory scales. The results also show that arrival time uncertainty was well reproduced after solute transport upscaling. This work represents a first demonstration of flow and reactive transport upscaling in a soil based on laboratory data. It also shows how simple upscaling methods can be incorporated into daily modeling practice using commercial flow and transport codes.

Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles

The optimal operation of microgrids is challenging due to the presence of various uncertain factors, i.e., renewable energy sources, loads, market price signals, and arrival and departure times of electric vehicles (EVs). In order to incorporate these uncertainties into the operation model of microgrids, an adaptive robust optimization-based operation method is proposed in this paper. In particular, the focus is on the uncertainties in arrival and departure times of EVs. The optimization problem is divided into inner and outer problems and is solved iteratively by introducing column and constraint cuts. The unit commitment status of dispatchable generators is determined in the outer problem. Then, the worst-case realizations of all the uncertain factors are determined in the inner problem. Based on the values of uncertain factors, the generation amount of dispatchable generators, the amount of power trading with the utility grid, and the charging/discharging amount of storage elements are determined. The performance of the proposed method is evaluated using three different cases, and sensitivity analysis is carried out by varying the number of EVs and the budget of uncertainty. The impact of the budget of uncertainty and number of EVs on the operation cost of the microgrid is also evaluated considering uncertainties in arrival and departure times of EVs.