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About 5,090 papers for “expected completion time”

Technical Note—Expected Completion Time in Pert Networks

Pierre N. Robillard, Michel Trahan · Operations Research · 1976

This note introduces a general approach to obtain lower bounds for the expected completion time in PERT networks where activity durations are random (discrete or continuous) variables. We show that some well-known bounds can be derived as particular cases of this general approach based on Jensen's inequality. We assume that the durations of the activities terminating at a node are independent of the durations of the activities terminating at another node and that all the activity durations have a finite positive range.

Cited by 58

The expected competitive ratio for weighted completion time scheduling

Alexander Souza, Angelika Steger · Repository for Publications and Research Data (ETH Zurich) · 2006

A set of n independent jobs is to be scheduled without preemption on m identical parallel machines. For each job j, a diffuse adversary chooses the distribution Fj of the random processing time Pj from a certain class of distributions Fj. The scheduler is given the expectation μj = E[Pj], but the actual duration is not known in advance. A positive weight wj is associated with each job j and all jobs are ready for execution at time zero. The scheduler determines a list of the jobs, which is then scheduled in a non-preemptive manner. The objective is to minimise the total weighted completion time ∑j wj Cj. The performance of an algorithm is measured with respect to the expected competitive ratio maxF ∈ F E[∑j wj Cj/OPT], where Cj denotes the completion time of job j and OPT the offline optimum value. We show a general bound on the expected competitive ratio for list scheduling algorithms, which holds for a class of so-called new-better-than-used processing time distributions. This class includes, among others, the exponential distribution. As a special case, we consider the popular rule weighted shortest expected processing time first (WSEPT) in which jobs are processed according to the non-decreasing μj/wj ratio. We show that it achieves E[WSEPT/OPT] ≤ 3 - 1/m for exponential distributed processing times

Cited by 0Open access

Reasoning About Expected Job Completion Time in Dynamic Vehicular Clouds

Aida Ghazizadeh, Puya Ghazizadeh, Ravi Mukkamala et al. · IEEE Transactions on Intelligent Transportation Systems · 2023

Roughly a decade ago, inspired by the phenomenal success of cloud computing, a group of researchers have defined Vehicular Clouds as a group of vehicles whose sensing, communication, and computing resources can be coordinated and allocated to authorized users. While both conventional and Vehicular Clouds are instances of utility computing, a number of important characteristics set vehicular clouds apart from their conventional counterparts. These characteristics include the mobility of vehicles and the volatility of resources that fluctuate with the arrival and departure of vehicles. As in the conventional version of cloud computing, approximating job completion time is one of the key performance metrics of interest. Unfortunately, estimating job completion time with any degree of accuracy and confidence requires complete knowledge of the distributions of several relevant random variables. Typically, however, these probability distributions are not known. Luckily, in many cases of practical relevance, accumulated empirical evidence allows to estimate the first few moments of these random variables. The main contribution of this paper is to offer an accurate approximation of the expected job completion time in Dynamic Vehicular Clouds built on top of vehicles on a highway. For this purpose, we rely on empirical estimates of the first moment of the user job execution time in the absence of any overhead attributable to the Dynamic Vehicular Cloud itself. Our extensive simulations have confirmed that our approximations of the expected job execution time are very accurate.

Cited by 9

Bounds on Expected Cognition Completion Time in RFID Networks Employing Static Naive MAC Scheme

CheonWon Choi · The Journal of Korean Institute of Communications and Information Sciences · 2025

Consider an RFID network that consists of single reader and multiple tags residing in the vicinity of the reader. In the RFID network, a collision can occur among the packets which are almost simultaneously sent by two or more tags. For numerous tags to be able to successfully deliver their packets to the reader while arbitrating the collision among some packets, suppose that the RFID network employs a static naive MAC scheme rooted in framed slotted ALOHA. Without prior information about nearby tags, a single-reader multiple-tag RFID is often deployed to cognize neighboring tags, i.e., to gather the identification numbers of the tags attached on various items. Definitely, it is of necessity to cognize all the tags in a limited time. Thus, it is of utmost importance to investigate the cognition completion time, i.e., the time elapsed until the reader cognizes all the tags. In this paper, we first construct a Markov chain to represent the cognition completion time as a hitting time of the Markov chain. As an alternative to the exact value of the expected cognition completion time, which is hardly obtainable in a tractable form, we then develop a lower bound by building a fast-converging Markov chain with first-order stochastically dominating random variables and attain an upper bound by constructing slow-converging Markov chains with first-order stochastically dominated random variables. Numerical examples reveal that the exact value of the expected cognition completion time is tightly bounded above by the upper bound in case a relatively small number of slots comprise each response interval. Further, the examples corroborate that the lower and upper bounds exhibit a parametric characteristic of convexity as similarly as the exact value of the expected cognition completion time does.

Cited by 0

Minimizing the Cost of Servicing a Product Subject to an Expected Completion Time Constraint.

James E. Falk, Marshall Rose · Defense Technical Information Center (DTIC) · 1975

This paper describes a problem in which end products, such as aircraft, are periodically serviced, owing to the failure or preventive maintenance of repairable items. Given the required sequence of item reinstallations, the item resupply times, and item repair probabilities, a relation between the item resupply times and the expected end-product service time is derived. An exact algorithm designed to minimize the total cost of repair subject to a constraint on the expected service time is presented. A heuristic algorithm yielding the entire 'minimum cost per expected service time' curve is also described.

Cited by 0

Disparities in expected driving time to opioid treatment and treatment completion: findings from an exploratory study

Abdullah Alibrahim, Jeanne C. Marsh, Hortensia Amaro et al. · BMC Health Services Research · 2022

This systemwide analysis provides novel time estimates of driving-based experiences and a strong relationship with completion rates in methadone treatment. Specifically, the result showing reduced treatment completion rates for drive times longer than 10 min may inform policies regarding the ideal geographic placement of methadone-based treatment programs and service expansion initiatives.

Cited by 18Open access

Disparities in expected driving time to opioid treatment and treatment completion: findings from an exploratory study

Alibrahim, Abdullah, Marsh, Jeanne C., Amaro, Hortensia et al. · Figshare · 2022

Abstract Background Commuting time to treatment has been shown to affect healthcare outcomes such as engagement and initiation. The purpose of this study is to extend this line of research to investigate the effects of driving time to opioid programs on treatment outcomes. Methods We analyzed discharge survey data from 22,587 outpatient opioid use disorder treatment episodes (mainly methadone) in Los Angeles County and estimated the associated driving time to each episode using Google Maps. We used multivariable logistic regressions to examine the association between estimated driving time and odds of treatment completion after adjusting for possible confounders. Results Findings show an average driving time of 11.32 min and an average distance of 11.18 km. We observed differences in estimated driving time across age, gender, and socioeconomic status. Young, male, less formally educated, and Medi-Cal-ineligible clients drove longer to treatment. A 10-min drive was associated with a 33% reduction in the completion of methadone treatment plans (p

Cited by 0Open access

Disparities in expected driving time to opioid treatment and treatment completion: findings from an exploratory study

Alibrahim, Abdullah, Marsh, Jeanne C., Amaro, Hortensia et al. · Figshare · 2022

Abstract Background Commuting time to treatment has been shown to affect healthcare outcomes such as engagement and initiation. The purpose of this study is to extend this line of research to investigate the effects of driving time to opioid programs on treatment outcomes. Methods We analyzed discharge survey data from 22,587 outpatient opioid use disorder treatment episodes (mainly methadone) in Los Angeles County and estimated the associated driving time to each episode using Google Maps. We used multivariable logistic regressions to examine the association between estimated driving time and odds of treatment completion after adjusting for possible confounders. Results Findings show an average driving time of 11.32 min and an average distance of 11.18 km. We observed differences in estimated driving time across age, gender, and socioeconomic status. Young, male, less formally educated, and Medi-Cal-ineligible clients drove longer to treatment. A 10-min drive was associated with a 33% reduction in the completion of methadone treatment plans (p

Cited by 0Open access

(www.interscience.wiley.com) DOI: 10.1002/acp.1082 Associating a Time-Based Prospective Memory Task with an Expected Context Can Improve or Impair Intention Completion

Gabriel I. Cook, Richard L. Marsh, Jason L. Hicks · 2015

Three experiments investigated time-based prospective memory defined here as remembering to fulfil an intention during a later window of time. When people associated the response window with a future context, time-based responding was better if that context expectation was correct as compared with having no context expectation at all. By contrast, if the response window occurred in a context that preceded the expected context, time-based performance was worse than having no context expectation at all. An explicit reminder about the intention was sufficient to ameliorate the decrement found with an incorrect context association. However, successfully completing a different, event-based prospective memory intention was not sufficient to overcome the incorrect context association. The authors assert that possessing such associations alters normal monitoring of the passage of time which in turn leads to the pattern of results obtained. Copyright # 2004 John Wiley & Sons, Ltd. Human memory is studied in controlled laboratory settings, in part, because the principles that are acquired can be applied to improve everyday cognitive functioning. One area of memory that has the potential for direct and immediate application is understanding how people successfully remember to complete their intentions; and that is the focus of this

Cited by 0

Large pert type networks

Warmoes, Jean-Claude · PolyPublie (École Polytechnique de Montréal) · 1977

ABSTRACT: A special construction -- Expected value of project completion time -- Probability of completion.

Cited by 0

The two charts on the left show mean task completion times within a 95% CI, while the third plots task completion times for each task for each user, compared to the expected maximum time (MTC) to complete each task

Aba-Sah Dadzie, Burger, Albert · Figshare · 2011

<b>Copyright information:</b>Taken from "Providing visualisation support for the analysis of anatomy ontology data"BMC Bioinformatics 2005;6():74-74.Published online 24 Mar 2005PMCID:PMC1087473.Copyright © 2005 Dadzie and Burger; licensee BioMed Central Ltd.

Cited by 0Open access

The two charts on the left show mean task completion times within a 95% CI, while the third plots task completion times for each task for each user, compared to the expected maximum time (MTC) to complete each task

Aba-Sah Dadzie, Burger, Albert · Figshare · 2011

<b>Copyright information:</b>Taken from "Providing visualisation support for the analysis of anatomy ontology data"BMC Bioinformatics 2005;6():74-74.Published online 24 Mar 2005PMCID:PMC1087473.Copyright © 2005 Dadzie and Burger; licensee BioMed Central Ltd.

Cited by 0Open access

INEQUALITIES AND BOUNDS IN STOCHASTIC SHOP SCHEDULING*

Michael Pinedot, Richard Webert · 2016

Abstract. In this paper, stochastic shop models with m machines and n jobs are considered. A job has to be processed on all m machines, while certain constraints are imposed on the order of processing. The effect of the variability of the processing times on the expected completion time of the last job (the makespan) and on the sum of the expected completion times of all jobs (the flow time) is studied. Bounds are obtained for the expected makespan when the processing time distributions are New Better (Worse) than Used in Expectation.

Cited by 0

STOCHASTIC BATCH SCHEDULING AND THE “SMALLEST VARIANCE FIRST” RULE

Michael Pinedo · Probability in the Engineering and Informational Sciences · 2007

Consider a single machine that can process multiple jobs in batch mode. We have n jobs and the processing time of job j is a random variable X j with distribution F j . Up to b jobs can be processed simultaneously by the machine. The jobs in a batch all have to start at the same time and the batch is completed when all jobs have finished their processing (i.e., at the maximum of the processing times of the jobs in that batch). We are interested in two objective functions, namely the minimization of the expected makespan and the minimization of the total expected completion time. We first show that under certain fairly general conditions, the minimization of the expected makespan is equivalent to specific deterministic combinatorial problems, namely the Weighted Matching problem and the Set Partitioning problem. We then consider the case when all jobs have the same mean processing time but different variances. We show that for certain special classes of processing time distributions the Smallest Variance First rule minimizes the expected makespan as well as the total expected completion time. In our conclusions we present various general rules that are suitable for the minimization of the expected makespan and the total expected completion time in batch scheduling.

Cited by 9

Estimating Project Completion Time with Monte Carlo Simulation

Adnan Fadjar, N. Nirmalawati, N Hidayat · REKONSTRUKSI TADULAKO Civil Engineering Journal on Research and Development · 2022

Risk and uncertainty are factors that construction project managers have been increasingly had to deal with. Project completion time is one of the areas where the expected time is often underestimated or shorter than the actual completion time. Monte Carlo simulation is a widely used simulation technique in modeling a process that is difficult to predict due to its random variables. This study provides a practical way to use Monte Carlo simulation to simulate a project using functions available in a spreadsheet application. A project with five activities was simulated 2000 times using minimum, maximum, and expected duration. The mean, mode, and median simulation results were then plugged into their respective precedence diagram networks to compare them. The precedence diagram computations found that mean, mode, and median project completion times were longer than the initially expected completion time. The mean, mode, and median were 50, 53, and 48 days, respectively, which were 8, 11, and 6 days longer, respectively, than the 42 days initially expected. The study showed that the Monte Carlo simulation could assist the project manager in planning a project schedule that deals with risk and uncertainty more realistically.

Cited by 1Open access