Estimation Of Completion Time Research Articles

Modeling and validating Bayesian accrual models on clinical data and simulations using adaptive priors.

Slow recruitment in clinical trials leads to increased costs and resource utilization, which includes both the clinic staff and patient volunteers. Careful planning and monitoring of the accrual process can prevent the unnecessary loss of these resources. We propose two hierarchical extensions to the existing Bayesian constant accrual model: the accelerated prior and the hedging prior. The new proposed priors are able to adaptively utilize the researcher's previous experience and current accrual data to produce the estimation of trial completion time. The performance of these models, including prediction precision, coverage probability, and correct decision-making ability, is evaluated using actual studies from our cancer center and simulation. The results showed that a constant accrual model with strongly informative priors is very accurate when accrual is on target or slightly off, producing smaller mean squared error, high percentage of coverage, and a high number of correct decisions as to whether or not continue the trial, but it is strongly biased when off target. Flat or weakly informative priors provide protection against an off target prior but are less efficient when the accrual is on target. The accelerated prior performs similar to a strong prior. The hedging prior performs much like the weak priors when the accrual is extremely off target but closer to the strong priors when the accrual is on target or only slightly off target. We suggest improvements in these models and propose new models for future research.

Introduction of an empirical TBM cutter wear prediction model for pyroclastic and mafic igneous rocks; a case history of Karaj water conveyance tunnel, Iran

One of the main cost items in tunneling projects using tunnel boring machines (TBMs) in rock and soil is the cost of changing of damaged or worn cutting tools. Cutter change is a time consuming activity which can have a major impact on the daily advance rate (AR) and therefore, on the time and cost of constructing the tunnel. Accurate estimation of TBM performance and related completion time and cost requires reliable assessment of disc cutter wear by using appropriate methods. This study introduces a new empirical model for predicting cutter wear in moderate to very strong pyroclastic and mafic igneous rocks based on data collected from a long tunnel constructed by mechanized method in Iran. This includes monitoring and analysis of field data including cutter wear, geological parameters, and operational data from Karaj water conveyance tunnel project. The field data compiled in a database was subjected to statistical analysis for development of relationships between disc cutter life and geological parameters. Results of the analyses indicated reasonable correlations between intact rock parameters and tool wear. Some empirical equations are offered for predicting disc cutter wear in similar geological conditions. These equations were developed based on data from very blocky to blocky pyroclastic rocks and blocky to massive mafic igneous rocks with a UCS range of 30 to more than 150MPa.

A unique intelligent approach for forecasting project completion time in oil refineries

This study presents a hybrid approach for accurate forecasting of project completion time with noisy and uncertain safety factors in oil refineries. The hybrid approach is based on artificial neural network (ANN), fuzzy mathematical programming (FMP) and conventional regression. Three indictors, namely, number of occupational injuries, number of employees and ratio of maximum useful hours over useful hour per month are considered as inputs. Also, project completion time is considered as the main output. To achieve the objective of this study, five sets of data with respect to oil refinery construction projects in various cities of Iran are collected and analyzed through statistical methods. It is shown that for the actual case of this study, ANN presents lowest mean absolute percentage error (MAPE). Also, analysis of variance (ANOVA) is used to verify and validate the results of this study. This is the first study that presents a hybrid approach for accurate estimation and forecasting of project completion time with complex, noisy and uncertain occupational factors.

Advance rate simulation for hard rock TBMs

The existing methods for estimating the advance rate of hard rock Tunnel Boring Machine (TBM) often involves estimation of the machine utilization in a direct or an indirect manner. These methods are based on empirical systems and are limited in their capacity for incorporating new machine capabilities or including many of the geological features along the tunnel. As such, these models are not used as often due to their shortcomings. The objective of this study is to offer some improvements by using a new approach for simulating all activities. The basic idea in simulation techniques is to predict the duration of different activities based on their recorded time distributions from past case histories or from the early stages of a project. A simulation model contains a series of operations, which should be repeated for a certain number of cycles. In each cycle, the model selects an anticipated duration time from time distribution curves for each activity and by using a logical relationship between activities, being parallel or in series and the possibility of activity overlaps, it assigns a duration for the whole cycle. This is then continued to finish the task at hand with required number of cycles. In this case, the cycles are the TBM going through each stroke or penetration cycle. This study includes discussion of each activity or failure of each subsystem and assigned and related duration accounts for parallel activities, and offers a distribution of cycle time, and finally estimation of tunnel completion time. A comprehensive study was conducted to evaluate the requirements for California switch final location and also the work arrangements for locomotives and trains for different scenarios. Different arrangements were programmed with Arena© simulation software using field data of a double shield TBM, which recently completed a water conveyance tunnel. The simulation results show a very good agreement with the actual values of TBM advance rate values and also show the possible variation of the time required for completion of different sections of this tunnel. The outcome of this study was to establish a framework for arranging and modeling the main time components of TBM operation. This will provide a useful tool for developing reliable estimates of machine advance rate for specific site, ground condition, and machine type.

ANALISIS SISTEM PENGENDALIAN BIAYA (COST CONTROL FRAMEWORK) PADA PROYEK PELEBARAN JALAN SP.3 LEMPAKE SAMARINDA - SP.3 SAMBERA KALIMANTAN TIMUR

Edward Prana1, M.Ruslin Anwar2 & Yulvi Zaika31,2 & 3Fakultas Teknik Jurusan Teknik Sipil Universitas Brawijaya MalangAlamat Korespondensi: Jl. MT. Haryono No 167 Malang, Indonesiae-mail: edwarprana@ymail.comABSTRACTIn this study, will be analyzed Cost control framework on one road construction project inEast Kalimantan using Earned Value Concept and Work Break Down Structure. As the object ofstudy is Sp.3 Road Widening Project Lempake (Dublin) - Sp.3 Sambera located in the city ofSamarinda, East Kalimantan. way of acquiring primary data through observation and interviewsdirectly to the project, while secondary data from project documents.Performance costs and time by using Earned Value Concept in terms of the reporting week 25CV and SV value value Positive Negative, indicating that the work has been delayed from the timethe work plan, but the cost is still below the budgeted costs. Resource use efficiency using EarnedValue Fund at week-25 values of 1.11 and SPI CPI of 0.93. Cost performance, while showinggood performance for performance efficiency by less well because of a delay of the planned time.Predicted cost and project completion time estimated the cost for the remaining work on theassumption that the trend of the performance of the project will remain until the end of the project(ETC) amounted to Rp 3, 138,305,144.17 while the estimated cost of up project completion (EAC)is equal to = Rp. 9,871,988,500.07, which means there is no advantage or still under the budget plan(PV) in the amount of Rp. 10,968,876,111.19. While the estimated schedule for the completion ofthe aspects of the job completion time estimates obtained (TE) assuming the performance trend ofthe project remains is 212 days. This means that the project will be delayed approximately 14 daysof 198 days plan.Keywords: Control cost (Cost control framework), Work Break Down Structure, Earned ValueConcept.

Do different types of dynamic extrapolation rely on the same mechanism?

Observers can estimate the position of occluded moving objects, and this ability could be mediated by the oculomotor control system. However, other forms of motion cannot be tracked with pursuit eye movements. In Experiment 1, 2 kinds of motion extrapolation tasks were interleaved. In the Position extrapolation trials, participants observed a moving target that became occluded. They attempted to press a button when it arrived at the end of the occluder. In novel Accumulation extrapolation trials, they observed a matrix of Gabors with new elements appearing over time. The participants pressed when Gabors would have filled the entire matrix, had they continued accumulating at the same rate. In both tasks completion time estimates were related to presentation parameters in the same way. Moreover, there were robust intertask correlations: Participants who responded earlier in the position task also responded earlier in the accumulation task. Experiment 2 replicated these results with a third form of extrapolation. Although performance was not identical in all tasks, there were enough similarities to suggest that overlapping systems guide all forms of extrapolation. We propose that a common rate control mechanism guides extrapolation, like the velocity store in oculomotor control, but with a broader function than previously envisaged.

A Randomized Controlled Trial of the Effect of Service Delivery Information on Patient Satisfaction in an Emergency Department Fast Track

The objective was to determine the effect on patient satisfaction of providing patients with predicted service completion times. A randomized controlled trial was conducted in an urban, community teaching hospital. Emergency department (ED) patients triaged to fast track on weekdays between October 26, 2009, and December 30, 2009, from 9 am to 5 pm were eligible. Patients were randomized to: 1) usual care (n = 342), 2) provided ED process information (n = 336), or 3) provided ED process information plus predicted service delivery times (n = 333). Patients in group 3 were given an "average" and "upper range" estimate of their waiting room times and treatment times. The average and upper range predictions were calculated from quantile regression models that estimated the 50th and 90th percentiles of the waiting room time and treatment time distributions for fast track patients at the study site based on 2.5 years of historical data. Trained research assistants administered the interventions after triage. Patients completed a brief survey at discharge that measured their satisfaction with overall care, the quality of the information they received, and the timeliness of care. Satisfaction ratings of very good versus good, fair, poor, and very poor were modeled using logistic regression as a function of study group; actual service delivery times; and other patient, clinical, and temporal covariates. The study also modeled satisfaction ratings of fair, poor, and very poor compared to good and very good ratings as a function of the same covariates. Survey completion rates and patient, clinical, and temporal characteristics were similar by study group. Median waiting room time was 70 minutes (interquartile range [IQR] = 40 to 114 minutes), and median treatment time was 52 minutes (IQR = 31 to 81 minutes). Neither intervention affected any of the satisfaction outcomes. Satisfaction was significantly associated with actual waiting room time, individual providers, and patient age. Every 10-minute increase in waiting room time corresponded with an 8% decrease (odds ratio [OR] = 0.92; 95% confidence interval [CI] = 0.89 to 0.95) in the odds of reporting very good satisfaction with overall care. The odds of reporting very good satisfaction with care were lower for several triage nurses and fast track nurses, compared to the triage nurse and fast track nurse who treated the most study patients. Each 10-minute increase in waiting room time was also associated with a 10% increase in the odds of reporting very poor, poor, or fair satisfaction with overall care (OR = 1.10; 95% CI = 1.06 to 1.14). The odds of reporting very poor, poor, or fair satisfaction with overall care also varied significantly among the triage nurses, fast track doctors, and fast track nurses. The odds of reporting very poor, poor, or fair satisfaction with overall care were significantly lower among patients aged 35 years and older compared to patients aged 18 to 34 years. Satisfaction with overall care was influenced by waiting room time and the clinicians who treated them and not by service completion time estimates provided at triage.

Designing and engineering time: the psychology of time perception in software

Build Applications, Websites, and Software Solutions that Feel Faster, More Efficient, and More Considerate of Users Time! One hidden factor powerfully influences the way users react to your software, hardware, User Interfaces (UI), or web applications: how those systems utilize users time. Now, drawing on the nearly 40 years of human computer interaction researchincluding his own pioneering workDr. Steven Seow presents state-of-the-art best practices for reflecting users subjective perceptions of time in your applications and hardware. Seow begins by introducing a simple model that explains how users perceive and expend time as they interact with technology. He offers specific guidance and recommendations related to several key aspects of time and timingincluding user tolerance, system responsiveness, progress indicators, completion time estimates, and more. Finally, he brings together proven techniques for impacting users perception of time drawn from multiple disciplines and industries, ranging from psychology to retail, animal research to entertainment. Discover how time and timing powerfully impact user perception, emotions, and behavior Systematically make your applications more considerate of users time Avoid common mistakes that consistently frustrate or infuriate users Manage user perceptions and tolerance, and build systems that are perceived as faster Optimize flow to make users feel more productive, empowered, and creative Make reasonable and informed tradeoffs that maximize limited development resources Learn how to test usability issues related to timeincluding actual vs. perceived task duration Designing and Engineering Time is for every technology developer, designer, engineer, architect, usability specialist, manager, and marketer. Using its insights and techniques, technical and non-technical professionals can work together to build systems and applications that provide far more valueand create much happier users. Steven C. Seow has a unique combination of experience in both experimental psychology and software usability. He joined Microsoft as a User Researcher after completing his Ph.D. in Experimental Psychology at Brown University with a research focus on human timing and information theory models of human performance. Seow holds Bachelors and Masters Degrees in Forensic Psychology from John Jay College of Criminal Justice, and wrote his masters thesis on distortions in time perception. For more information about Steven Seow and his research, visit his website at www.StevenSeow.com. informit.com/aw

ESTIMATION OF JOB COMPLETION TIMES IN A DYNAMIC SHOP JOB BASED ON STATIC JOB SHOP SOLUTION

We investigate the temporal behaviour of completion time estimates derived from scheduling algorithms that are based on the set of already known and accepted orders (static job shop problem). In a dynamic job shop environment, these estimates change over time, typically increasing. This leads to a systematic error in the completion time estimation based on static job shop algorithms.We propose an approach for calculating this systematic error. Furthermore, a method for compensating this error is derived. For some simple examples, the theory is worked out and compared with simulation results. In all cases, the estimates based on the static algorithm can substantially be improved.

A GA-based NN approach for makespan estimation

The completion time or makespan estimation of a set of jobs in batch process industries is studied; Because jobs interact with each other at the level of shop floor, five interaction variables are defined and an indication of their influence on the makespan is experimentally investigated. A back-propagation network (BPN) model combined with genetic algorithms (GAs) to makespan estimation is proposed. GAs are adopted in the BPN to determine the parameters of BPN and to improve the accuracy of makespan estimation. Previously, there have been no appropriate rules to determine these parameters, 1000 training instances were used for training and evaluating the performance of the model. The study shows that combine GA with NN approach is more effective and accurate in estimating makespan than the BPN model by using trial and error.

Self-adaptive task allocation and scheduling of meta-tasks in non-dedicated heterogeneous computing

The efforts to construct a national scale grid computing environment have brought unprecedented computing capacity and complexity. Exploiting this complex infrastructure requires efficient middleware to support the execution of distributed applications, which presents the challenge of how to schedule tasks in shared heterogeneous systems. Most existing scheduling systems are based on predetermined estimation of task completion time and resources availability. They may not provide appropriate scheduling if the underlying computing resources present an abnormal usage pattern during an application execution. For solving long-running applications in a large-scale grid environment, abnormal usage of some resource may not be uncommon. We have proposed the development of the Grid Harvest Service (GHS) performance evaluation and task scheduling system in our previous work. In this study, we present a novel dynamic self-adaptive scheduling algorithm and its implementation under GHS. Scheduling and rescheduling algorithms and mechanisms are carefully investigated. Experimental results show that, equipped with these new scheduling mechanisms, GHS outperforms existing systems considerably in scheduling large applications in a non-dedicated heterogeneous environment.

Using Work Time Estimation Methods to Predict Preventive Maintenance Task Completion Times

Work time estimation techniques, as an alternative to direct work time measurement methods, were replicated using preventive maintenance personnel as expert estimators. Two groups of maintainers, those familiar with maintenance activities on specific equipments included in the study, and those with generic preventive maintenance experience, estimated subtask completion times on electronic equipment. Both groups' task completion time estimates correlated very highly with observed task completion times, indicating that general knowledge of maintenance is sufficient to produce strong prediction models. The relationship between generic experts' estimates and observed task completion times was further strengthened by providing more detailed subtask descriptions during the estimation process.

Project scheduling via stochastic programming

If for a project (described by a non-empty set of activities, a relation on this set of activities the transitive closure of which is a strict order, and activity-completion-times assigned to the single activities) the activity-completion-times are assumed to be random variables a two-stage stochastic programming approach can be used for a cost-oriented project scheduling model. Completion-time estimates for the activity-completion-times are computed in such a way that, in order to meet a prescribed time-constraint for the project-completion-time, the expected costs for performing the activities according to the computed time-schedule are minimized. An example is included for illustration.