Calculation of safe float use in risk-analysis-oriented network scheduling

This paper presents the results of a detailed quantitative analysis of performance metrics of a sample consisting of 89 transportation projects sponsored by the World Bank. The sample and a subset consisting of 65 projects were evaluated using the performance metrics of project cost, schedule and scope. The effect of project size (dollar value) and project duration on performance metrics was investigated. Also, the achievement of project goals and potential improvement in planning and estimating over time (the learning effect) was studied. It was found that, in general, in transportation projects sponsored by the World Bank, costs are overestimated and schedules are optimistic. The outcome with respect to cost seems counter‐intuitive because previous work by other researchers had shown a systematic underestimation of project costs. There is significant evidence that there are no efficient controls in place to predict or prevent schedule delays. The study also showed that during the past 15 years, no improvement (learning effect) was evident in project cost and duration estimation as the level of accuracy has not changed significantly. Further, it is observed that project duration did not affect the performance with respect to cost and delay.

In construction projects, the relationship between project cost and duration, which is one of the main factors of the project, are considered the parameters decision-makers. When a construction schedule is planned by the planners, additional resources are allocated to shorten the project duration considering the completion time and cost of the activities so that the most suitable construction schedule is prepared by providing a trade-off between project duration and total cost. In the case of time-cost trade-off problems, the total cost of the project is determined by summing each activities’ cost by adding indirect costs. Moreover, total project duration is determined by obtaining total project duration after CPM (Critical Path Method) calculations. Obtaining optimum duration and costs is such a problematic issue with using conventional methods and common software packages. These techniques consider deterministic project values and include CPM calculations for fitness value calculation. Moreover, current studies defined alternatives as non-variable discrete values. However, construction projects are dynamic processes, and quantities are prone to be changed related to project size. In this study, time-cost trade-off optimization is performed by using harmony search method with introducing different implementation that can be more easily used for different projects. The model is implemented in a real construction project, and the conclusions are interpreted.

Accurate project duration prediction is increasingly important for management because it defines the expected timeline for project realization. This study utilizes an integrated approach combining neural networks with the Taguchi method to forecast the time required to complete projects within predetermined deadlines. The methodology involves modelling and simulating the network of project activities to estimate the total average project duration. ration. The neural network model uses input variables such as success probability, effort, and learning factor to predict the total time necessary for project completion. The total average project duration is the output variable that is critical during the design phase. Subsequently, the Taguchi method optimizes the neural network’s outputs, incorporating mean squared error (MSE) values to enhance predictive accuracy. accuracy. This study underscores the efficacy of artificial neural networks (ANNs) as predictive tools, demonstrating their ability to meticulously estimate project duration. The proposed method’s efficiency and applicability are demonstrated by MATLAB simulation analyses, highlighting its effectiveness in precise deadline estimation. In the realm of engineering, ANNs stand tall as formidable predictive tools, their efficacy underscored by this study’s successful application. By harnessing ANNs and simulation data, this research crafts a predictive model adept at estimating the average total duration of projects. Through meticulous consideration of crucial parameters like the probability of success and effort factors, the model emerges as a beacon of accuracy within the design domain. Future research will explore the effects of additional parameters on activity networks and alternative transfer functions, as well as the potential integration of reinforcement algorithms to improve resource allocation, risk management, and project outcomes through online training data.

Uncertainty regarding the duration of activities is a major factor affecting the reliability of construction project schedules. Variability in resources, field conditions, material supplies, and other technical conditions often cause deviations between planned and actual durations. Therefore, this study aims to analyze project duration using the Program Evaluation and Review Technique (PERT) based on three-point estimates, namely optimistic (a), most likely (m), and pessimistic (b), and to visualize the duration range through Triangular Fuzzy Number representation to clarify the variation in estimates for each activity. The methods used in this study include collecting duration estimates (a, m, b) through structured interviews, calculating the Expected Time (te) for each activity, and compiling a work network using the Precedence Diagram Method (PDM) with Microsoft Project to determine the critical path and total project duration. The results of the study show that the total project duration is 170 days, with several activities on the critical path determining the project completion time, while probabilistic analysis results in a 70% chance of project completion within the target duration. The uncertainty of scheduling analysis can be resolved with a triangular fuzzy number visualization approach to extend the optimistic, most likely, and pessimistic time limits for each activity. Based on these results, this study is expected to encourage the application of PERT analysis based on three-point estimates visualized in the form of triangular distributions for each activity as a basis for setting target durations and making more realistic project acceleration decisions.

Ship maintenance scheduling by genetic algorithm and constraint-based reasoning

Classical scheduling techniques are well-known to underestimate the average project duration, yet they remain widely used in practice due to their simplicity. In this paper, the new Critical Duration Index (CDI) is proposed. This index indirectly allows anticipation for the probability of a project ending late, as well as the average project duration extension compared with a deterministic project duration estimate. The accuracy of two simple regression expressions that use the CDI was tested on two representative data sets of 4,100 artificial and 108 empirical (real) projects. Results show that these regression expressions outperformed the only alternative index found in the literature. Besides allowing enhanced forecasting possibilities, calculating the CDI only requires basic scheduling information that is available at the planning stage. It can thus be easily adopted by project managers to improve their project duration estimates over prior deterministic techniques.

Project Scheduling using Dependency Structure Matrix

On the whole, appropriate scheduling is an extremely paramount factor in project planning. It makes planning items such as allocating resource, determining the number of contractors, maintaining, and delivering order easy. Due to its convoluted nature, scheduling new product development projects is very difficult; managers of these projects deal with complicated factors like uncertain activity duration, information flow and complex relationships. Dependency structure matrix (DSM) has been suggested to cope with these complexities. In the present study, uncertainty in estimating project duration is highlighted; therefore, input information like activity duration, information flow among activities, and the time factors are considered stochastic. Firstly, random numbers are generated based on probability distribution of inputs. Then, project duration and minimised project duration are simulated using DSM method and Monte-Carlo simulation; when simulation turns stable, the mean and variance of durations are obtained. A numerical example is provided and findings are discussed.

Construction projects are data-rich environments. However, those data are usually captured for site-specific reasons, e.g., the filing and approval of inspection requests, with little regard to how they can be leveraged for improved project management. Typically, scheduling techniques rely on general probability estimates, which do not capture the details of the site processes causing schedule deviations. This paper illustrates how machine learning techniques can mine project data to forecast delay in the midst of the project. The proposed method uses concrete pouring requests as an example of a site data stream and implements a random forest predictive model to forecast the likelihood of acceptance for these requests. Embedded in the proposed approach is an analysis that allows for the addition of probabilistic time delays associated with the forecast of rejected requests. The methodology was tested on a real-world case study, allowing for the comparison between a project duration estimate based on critical path method (CPM) with static buffers and a project duration obtained using the proposed method. The results show a difference of 10% between the two durations. The paper shows how using data streams from a construction site with machine learning techniques can enhance project duration estimates in execution.

Traditional Earned Value Management technique in project planning for effort and duration remains low to medium accurate. This study seeks to develop a highly reliable and efficient Integrated Earned Value Method (IEVM) to improve cost and duration prediction accuracy. This experiment compared the performance of five machine learning models across three different datasets and six performance indicators and verified the models with three other types of live project data. The results indicated that IEVM is a highly reliable, effective, consistent, and accurate machine learning-based method with a significant increase in accuracy over the conventional Earned Value Management technique. The finding pointed out a potential gap in the relationship between dataset quality and the performance of the ML model.

The Critical Path Method (CPM) has been the dominant scheduling method for the past several decades. Most State Departments of Transportation (DOTs) in the U.S. currently apply CPM to estimate project duration for major and complex projects. The method, however, has limitations, and one of them is its poor ability to analyze schedule risks. While previous studies have identified various factors that cause uncertainties in schedules, the estimated duration from CPM is deterministic. To quantify schedule risks, the Program Evaluation Review Technique which uses three-point estimates for activity duration and Monte Carlo Simulation (MCS) based methods that assign uncertainties to work activities through the probability distributions of activity durations have been limitedly used in the industry. After running the simulation thousands of times, the probability distribution of the total project duration can be developed to assist risk analysis, such as determining a schedule contingency for the project. In current practices, the use of the MCS method is still limited due to various reasons. One of the most challenging issues is the difficulties in estimating the probability distributions of activity durations objectively. This study aims to leverage historical digital daily work report (DWR) data available in the DOTs’ databases to determine the probability distributions of the production rates of work activities then estimate the distributions of activity durations when quantities of the activities are provided. Since the DWRs record the daily accomplished quantity of each work item in the construction phase, the actual production rates of the work items can be calculated to obtain a more accurate and realistic duration estimate for a future project. DWR data collected from a DOT were used to conduct a case study that demonstrates the value of this new approach for schedule risk analysis.

A framework combining project simulation model with extracted uncertainty parameters from past data is proposed in this paper. In the existing simulation models which take uncertainty elements into consideration, a large number of input parameters are requested. However, most of these parameters are difficult or time-consuming to state. The aim of this paper is to provide a possible solution proposing a new simulation model based on extracted parameters and setting up a protocol to extract uncertainty parameters from past log data. The output of the simulation model will be the project duration, giving feedback to the design of human resource. The proposed protocol includes a definition of necessary past data and how to calculate minimum work amount, delay probability and rework probability. On the other hand, the proposed simulation model includes basic model describing project structures such as task dependency and resource skills, delay model describing variation of task work amount, and rework model describing transition among different tasks. Besides, in the case study, we test the program of the proposed framework constructed of the parameter extracting protocol and the simulation model. After that, we apply the framework on a project introduced in existing research. Two human resource strategies are considered of, on the basis of the duration estimation results. The conclusion of this study is that the proposed framework is able to conduct duration estimation and support the decision-making process around human resource at the early stage of a project.

BackgroundAs offsite prefabrication and modular construction continue to gain momentum into the future, material supply chain becomes increasingly complex for modern construction projects. Pre-engineered material supply presents itself as a driver for planning crew installation operations on site that involve skilled labor and heavy equipment.MethodsThis paper proposes a framework for implementing the material-supply-driven project planning and control optimization approach to deal with material delays that take place at the piping spool fabrication shop. Design drawings, contract deadlines, resources availability and material supply patterns are extracted from a real oil and gas expansion project to validate the proposed implementation methodology.ResultsAn interactive Gantt chart with information on activity start time, duration, and allocated resources is generated to visualize the optimization outcome. In connection with the resource-constrained schedule, material supply-demand patterns over project duration are also visualized.ConclusionsThese two forms of visualizations provide insightful decision-making support in coping with material delay while fulfilling project objectives. Ultimately, material-supply driven crew job schedules in correspondence with particular objectives and implementation strategies can be generated, ready to guide project execution.

CHAPTER V - CRITICAL PATH METHODS

A recent study by the University of Minnesota looked at eight high schools across the U.S. that chose later start times, from 8:00 a.m. to 8:55 a.m. The study found significant decreases in absences and tardiness as well as greater academic benefits for schools with the latest start times. Among the 9,395 students in the study, those who slept eight or more hours each night were less likely to report symptoms of depression and fall asleep in class. Moreover, after the change to a later start time, the number of car crashes in the districts studied decreased by 13%. Included are recommendations for schools and districts considering changing their high school start times.