An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Activity Distributions. <s> This paper describes the development and application of a technique for measuring and controlling development progress for the Polaris Fleet Ballistic Missile program, Special Projects Office, Bureau of Ordnance, U.S. Navy. Project PERT (Program Evaluation Research Task) was set up to develop, test, and implement a methodology for providing management with integrated and quantitative evaluation of (a) progress to date and the outlook for accomplishing the objectives of the FBM program, (b) validity of established plans and schedules for accomplishing the program objectives, and (c) effects of changes proposed in established plans. In the PERT model, the R and D program is characterized as a network of interrelated events to be achieved in proper ordered sequence. Basic data for the analysis consists of elapsed time estimates for activities which connect dependent events in the network. The time estimates are obtained from responsible technical persons and are subsequently expressed in probability terms. T... <s> BIB001 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Activity Distributions. <s> Among network techniques recently widely employed in program management, Pert is addressed to the problem of assessing the manager's chances of completing a project on time. Theory and monte carlo simulation have shown that the Pert method yields results which are biased high, and this paper discusses a real example involving conditions under which the bias is very large. The manager is thus grossly misled into thinking his chances are very good, when in reality they are very poor. If the manager's network has multiple parallel paths with relatively equal means and large variances, Pert calculations will be considerably biased. Simulation can be utilized to estimate the distribution of completion time in this case, and guide the manager in appraising and controlling his chances of completion. <s> BIB002 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Activity Distributions. <s> The conventional procedure for determining the expected project completion time is to find the critical path; its duration is treated as the expected completion time for the project. It is known among the community of system simulation scholars, however, that deterministic critical path analysis yields certain conclusions that may be disproven via Monte-Carlo simulation of a project network. (Klingel observed this in 1966.) Although the body of literature on network simulation has grown a good deal in recent years, the concepts generally have not found a place in the decision processes of practicing project managers. In view of the erroneous expectations that deterministic network analysis leads to, there seems to be a need to disseminate network simulation truths more widely and effectively. The approach taken in this paper is to explain the phenomenon of projects “always” being late by proving, in the simplest way possible, that the deterministic critical path understates the likely project duration. Th... <s> BIB003 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Activity Distributions. <s> Critical path models concerning project management i.e. PERT/CPM fail to account for work force behavioral effects on the expected project completion time. In this paper, we provide a modelling framework for project management activities, that ultimately accounts for expected worker behavior under Parkinson's Law. A stochastic activity completion time model is used to formally state Parkinson's Law. The developed model helps to examine the effects of information release policies on subcontractors of project activities, and to develop managerial policies for setting appropriate deadlines for series or parallel project activities. <s> BIB004 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Activity Distributions. <s> Construction operations are subject to a wide variety of fluctuations and interruptions. Varying weather conditions, learning development on repetitive operations, equipment breakdowns, management interference, and other external factors may impact the production process in construction. As a result of such interferences, the behavior of construction processes becomes subject to random variations. This necessitates modeling construction operations as random processes during simulation. Random processes in simulation include activity and processing times, arrival processes (e.g. weather patterns) and disruptions. In the context of construction simulation studies, modeling a random input process is usually per- formed by selecting and fitting a sufficiently flexible probability distribution to that process based on sample data. To fit a generalized beta distribution in this context, a computer program founded upon several fast, robust numerical procedures based on a number of statistical-estimation methods is presented. In particular, the fol- lowing methods were derived and implemented: moment matching, maximum like- lihood, and least-square minimization. It was found that the least-square minimi- zation method provided better quality fits in general, compared to the other two approaches. The adopted fitting procedures have been implemented in BetaFit, an interactive, microcomputer-based software package, which is in the public domain. The operation of BetaFit is discussed, and some applications of this package to the simulation of construction projects are presented. <s> BIB005 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Activity Distributions. <s> The beta distribution is seen as a suitable model in risk analysis because it provides a wide variety of distributional shapes over a finite interval. Unfortunately, the beta distribution is not easily understood and its parameters are not easily estimated. This paper explores the possibility of using the more intuitively obvious triangular distribution as a proxy for the beta distribution. It is shown that the differences between the two distribution functions are seldom significant and a procedure is proposed for estimating the parameters of a triangular distribution on the basis of two extreme percentiles and the median. <s> BIB006 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Activity Distributions. <s> Techniques are presented for modeling and generating the univariate probabilistic input processes that drive many simulation experiments. Emphasis is on the generalized beta distribution family, the Johnson translation system of distributions, and the Bézier distribution family. Also discussed are nonparametric techniques for modeling and simulating time-dependent arrival streams using nonhomogeneous Poisson processes. Public-domain software implementations and current applications are presented for each input-modeling technique. Many of the references include live hyperlinks providing online access to the referenced material. <s> BIB007 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Activity Distributions. <s> The interest in activity sensitivity from both the academics and the practitioners lies in the need to focus a project manager's attention on those activities that influence the performance of the project. When management has a certain feeling of the relative sensitivity of the various parts (activities) on the project objective, a better management's focus and a more accurate response during project tracking should positively contribute to the overall performance of the project. In the current research manuscript, a simulation study is performed to measure the ability of four basic sensitivity metrics to dynamically improve the time performance during project execution. We measure the use of sensitivity information to guide the corrective action decision making process to improve a project's time performance, while varying the degree of management's attention. A large amount of simulation runs are performed on a large set of fictitious project networks generated under a controlled design. <s> BIB008 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Activity Distributions. <s> Project scheduling, risk analysis and project tracking are key parameters to a project's success or failure. Research on the relative sensitivity of project activities during the project scheduling phase as well as research on project performance measurement during project progress have been published throughout the academic literature and the popular press. Obviously, the interest in activity sensitivity information and project performance measurement from both the academics and the practitioners lies in the need to focus a project manager's attention on those activities that influence the performance of the project. When management has knowledge about the current project performance and has a certain feeling of the relative sensitivity of the various project activities on the project objective, a better management focus and a more accurate response during project tracking should positively contribute to the overall performance of the project. In this article, two alternative project tracking methods to detect project problems are presented and their efficiency on the quality of corrective actions to bring the project back on track is measured and evaluated. More precisely, a bottom-up and a top-down project tracking approach within a corrective action framework is applied on a large and diverse set of fictitious projects that are subject to Monte-Carlo simulations to simulate fictitious project progress under uncertainty. The top-down tracking approach relies on state-of-the-art earned value management performance metrics, while the bottom-up tracking mechanism makes use of the well-known schedule risk analysis method. A computational experiment shows that a top-down project tracking approach is highly efficient for project networks with a serial activity structure while a bottom-up approach performs better in a parallel structured project network. Moreover, it will also be shown that dynamic thresholds to trigger corrective actions, which gradually increase or decrease the project manager's attention along the project progress, outperform the static thresholds for both tracking approaches. <s> BIB009 </s> An Overview of Recent Research Results and Future Research Avenues Using Simulation Studies in Project Management <s> Activity Distributions. <s> Based on theoretical arguments and empirical evidence we advocate the use of the lognormal distribution as a model for activity times. However, raw data on activity times are often subject to rounding and to the Parkinson effect. We address those factors in our statistical tests by using a generalized version of the Parkinson distribution with random censoring of earliness, ultimately validating our model with field data from several sources. We also confirm that project activities exhibit stochastic dependence that can be modeled by linear association. <s> BIB010
The construction of a project baseline schedule discussed in the previous section relies on activity time and cost estimates as well as on estimates of time lags for precedence relations and use of resources assigned to these activities. However, the constructed baseline schedule assumes that these deterministic estimates are known with certainty. Reality, however, is flavoured with uncertainty, which renders the PERT/CPM methods and their resourceconstrained extensions often inapplicable to many real life projects. Consequently, despite its relevance in practice, the PERT/CPM approach often leads to underestimating the total project duration and costs (see e.g., Klingel BIB002 , Schonberger 4 ISRN Computational Mathematics BIB003 , Gutierrez and Kouvelis BIB004 , and many others), which obviously results in time and cost overruns. This occurs for the following reasons. (i) The activity durations in the critical path method are single point estimates that do not adequately address the uncertainty inherent to activities. The PERT method extends this to a three point estimate, but still relies on a strict predefined way of analysing the critical path. (ii) Estimates about time and cost are predictions for the future, and human beings often tend to be optimistic about it or, on the contrary, often add some reserve safety to protect themselves against unexpected events. (iii) The topological structure of a network often implies extra risk at points where parallel activities merge into a single successor activity. Uncertainty in the activity time and cost estimates or in the presence of project activities, uncertainty in the time lags of precedence relations or in the network structure, and even uncertainty in the allocation and costs of resources assigned to the activities can be easily modelled by defining distributions on the unknown parameters. These stochastic values must be generated from predefined distributions that ideally reflect the real uncertainty in the estimates. The use of activity distributions on activity durations has been investigated in project management research since the early developments of PERT/CPM. From the very beginning, project scheduling models have defined uncertainty in the activity durations by beta distributions. This is mainly due to the fact that the PERT technique has initially used these distributions BIB001 . Extensions to generalised beta distributions are also recommended and used in the literature (see e.g., AbouRizk et al. BIB005 ). However, since these generalised beta distribution parameters are not always easily understood or estimated, variation in activity durations are often simulated using the much more simple triangular distributions  where practitioners often base an initial input model on subjective estimates for the minimum value, the most likely value and the maximum value of the distribution of the activity duration. Although it has been mentioned in the literature that the triangular distribution can be used as a proxy for the beta distribution in risk analysis (see e.g., Johnson BIB006 ), its arbitrary use in case no empirical data is available should be taken with care (see e.g., Kuhl et al. BIB007 ). In the simulation studies of BIB009 BIB008 , the generalised beta distribution has been used to model activity duration variation, but its parameters have been approximated using the approximation rules of Kuhl et al. BIB007 . Other authors have used other distributions or approximations, resulting in a variety of ways to model activity duration variation. This is also mentioned in a quite recent paper written by Trietsch et al. BIB010 , where the authors argue that the choice of a probability distribution seems to be driven by convenience rather than by empirical evidence. Previous research studies have revealed that the choice of distributions to model empirical data should reflect the properties of the data. As an example, AbouRizk et al. BIB005 defend the importance of appropriate input models and state that their inappropriate use is suspect and should be dealt with carefully. To that purpose, Trietsch et al. BIB010 advocate the use of lognormal functions for modelling activity times, based on theoretical arguments and empirical evidence. An overview of stochastic modelling in project scheduling and the proper use of activity times distributions would lead us too far from the dynamic scheduling and project control simulation topic of this paper. Therefore, the reader is referred to the recent paper of Trietsch et al. BIB010 as an ideal starting point on the use of activity time distributions to be used in the stochastic project scheduling literature. In the remainder of this paper, activity distributions will only be used to model variation in the activity time estimates, and hence, no variation is modelled on the cost estimates or on the estimates about precedence relation time lags or resource assignments. This also means that all experiments and corresponding results discussed in the further sections will only hold for controlling the time performance of the projects, and the results can therefore not be generalised to cost controlling.