RESCON: Educational project scheduling software.

In this article we discuss a freely downloadable educational software tool for illustrating project scheduling and project management concepts. The tool features exact and heuristic scheduling procedures and visualizes project networks, project schedules, resource profiles, activity slacks, and project duration distributions.Project scheduling; Project management; Educational software; Visualization; Scheduling algorithms;

A statistical method for estimating activity uncertainty parameters to improve project forecasting

Just like any physical system, projects have entropy that must be managed by spending energy. The entropy is the project’s tendency to move to a state of disorder (schedule delays, cost overruns), and the energy process is an inherent part of any project management methodology. In order to manage the inherent uncertainty of these projects, accurate estimates (for durations, costs, resources, …) are crucial to make informed decisions. Without these estimates, managers have to fall back to their own intuition and experience, which are undoubtedly crucial for making decisions, but are are often subject to biases and hard to quantify. This paper builds further on two published calibration methods that aim to extract data from real projects and calibrate them to better estimate the parameters for the probability distributions of activity durations. Both methods rely on the lognormal distribution model to estimate uncertainty in activity durations and perform a sequence of statistical hypothesis tests that take the possible presence of two human biases into account. Based on these two existing methods, a new so-called statistical partitioning heuristic is presented that integrates the best elements of the two methods to further improve the accuracy of estimating the distribution of activity duration uncertainty. A computational experiment has been carried out on an empirical database of 83 empirical projects. The experiment shows that the new statistical partitioning method performs at least as good as, and often better than, the two existing calibration methods. The improvement will allow a better quantification of the activity duration uncertainty, which will eventually lead to a better prediction of the project schedule and more realistic expectations about the project outcomes. Consequently, the project manager will be able to better cope with the inherent uncertainty (entropy) of projects with a minimum managerial effort (energy)

On the use of schedule risk analysis for project management

The purpose of this paper is to give an overview on the existing literature and recent developments on the research on Schedule Risk Analysis (SRA) in Project Management (PM) to measure the sensitivity of activities and resources in the project network. SRA is a technique that relies on Monte-Carlo simulation runs to analyze the impact of changes in activity durations and costs on the overall project time and cost objectives. First, the paper gives an overview of the most commonly known sensitivity metrics from literature that are widely used by PM software tools to measure the time and cost sensitivity of activities as well as sensitivity for project resources. Second, the relevance of these metrics in an integrated project control setting is discussed based on some recent research studies. Finally, a short discussion on the challenges for future research is given. All sections in this paper are based on research studies done in the past for which references will be given throughout the manuscript

Forecasting the Project Duration Average and Standard Deviation from Deterministic Schedule Information

[EN] Most construction managers use deterministic scheduling techniques to plan construction projects and estimate their duration. However, deterministic techniques are known to underestimate the project duration. Alternative methods, such as Stochastic Network Analysis, have rarely been adopted in practical contexts as they are commonly computer-intensive, require extensive historical information, have limited contextual/local validity and/or require skills most practitioners have not been trained for. In this paper, we propose some mathematical expressions to approximate the average and the standard deviation of a project duration from basic deterministic schedule information. The expressions¿ performance is successfully tested in a 4100-network dataset with varied activity durations and activity durations variability. Calculations are quite straightforward and can be implemented manually. Furthermore, unlike the Project Evaluation and Review Technique (PERT), they allow drawing inferences about the probability of project duration in the presence of several critical and subcritical paths with minimal additional calculation.The first author acknowledges the Spanish Ministry of Science, Innovation, and Universities for his Ramon y Cajal contract (RYC-2017-22222) co-funded by the European Social Fund. The first two authors also acknowledge the help received by the research project PIN-0053-2019 funded by the Fundación Pública Andaluza Progreso y Salud (Junta de Andalucía, Spain). The first four authors also acknowledge the help received by the research group TEP-955 from the PAIDI (Junta de Andalucía, Spain). Finally, the fifth author, acknowledges the support from the National Natural Science Foundation of China (No. 71301013), the National Social Science Fund Post-financing projects (No.19FJYB017), the List of Key Science and Technology Projects in China¿s Transportation Industry in 2018-International Science and Technology Cooperation Project (No.2018-GH-006), and the Humanity and Social Science Program Foundation of the Ministry of Education of China (No. 17YJA790091).Ballesteros-Pérez, P.; Cerezo-Narváez, A.; Otero-Mateo, M.; Pastor-Fernández, A.; Zhang, J.; Vanhoucke, M. (2020). Forecasting the Project Duration Average and Standard Deviation from Deterministic Schedule Information. Applied Sciences. 10(2):1-22. https://doi.org/10.3390/app1002065412210

The impact of applying effort to reduce activity variability on the project time and cost performance

During project execution, deviations from the baseline schedule are inevitable due to the presence of uncertainty and variability. To assure successful project completion, the project’s progress should be monitored and corrective actions should be taken to get the project back on track. This paper presents an integrated project control procedure for measuring the project’s progress and taking corrective actions when necessary. We apply corrective actions that reduce the activity variability to improve the project outcome. Therefore, we quantify the relation between the applied managerial effort and the reduction in activity variability. Moreover, we define three distinct control strategies to take corrective actions on activities, i.e. an interventive strategy, a preventive strategy and a hybrid strategy. A computational experiment is conducted to evaluate the performance of these strategies. The results of this experiment show that different strategies are preferred depending on the topological network structure of projects. More specifically, the interventive strategy and hybrid strategy are preferred for parallel projects, while the preventive strategy is preferred for serial projects

A branch-and-bound algorithm for stable scheduling in single-machine production systems.

Robust scheduling aims at the construction of a schedule that is protected against uncertain events. A stable schedule is a robust schedule that will change little when variations in the input parameters arise. This paper proposes a branch-and-bound algorithm for optimally solving a single-machine scheduling problem with stability objective, when a single job is anticipated to be disrupted.Branch-and-bound; Construction; Event; Job; Robust scheduling; Robustness; Scheduling; Single-machine scheduling; Stability; Systems; Uncertainty;

Synthetic Sweden Mobility (SySMo) Model Documentation

This document describes a decision support framework using a combination of several state-of-the-art computing tools and techniques in synthetic information systems, and large-scale agent-based simulations. In this work, we create a synthetic population of Sweden and their mobility patterns that are composed of three major components: population synthesis, activity generation, and location assignment. The document describes the model structure, assumptions, and validation of results

Agent-based Transport Models as a Tool for Evaluating Mobility

The transportation system is undergoing fundamental transformations through emerging technologies. Some of these innovations have the potential to contribute to the sustainable transformation of the transportation system, such as electric vehicles (EVs) and shared autonomous electric vehicles (SEAVs). Before enacting policies to support these technologies or limit the use of undesirable ones, decision-makers need to better understand these innovations and the consequences of the policy to be implemented. This insight can be provided with models that are capable of reflecting the dynamics of new mobility, and interactions of travelers with each other and the infrastructure. This thesis describes the development of the Synthetic Swedish Mobility (SySMo) model that represents the travel behavior of an advanced synthetic population of Sweden, using an agent-based framework. The SySMo model provides a scaffold to build decision support tools through which present and future mobility scenarios can be analyzed and thus aid decision-makers in formulating informed policies. The SySMo model comprises a series of modules that utilize a stochastic approach combined with Neural Networks, a machine learning technique to generate a synthetic population and behaviorally realistic daily activity-travel schedules for each agent.The model first generates a synthetic replica of the population characterized by various socio-economic attributes using zone-level statistics and the national travel survey as input data. Then, daily heterogeneous activity patterns showing activity and trip features are assigned to each individual in the population with a high spatio-temporal resolution. To assess the SySMo model performance in each module, in-sample evaluations (i.e., comparing the model outputs with input data to measure the similarity of the results) and out-of-sample (i.e., comparing the model outputs with data never used in the model) evaluations are performed. The current model offers a valuable planning and visualization tool to illustrate mobility patterns of the Swedish population. The methodology can also be broadly applied to other regions with other relevant data and carefully calibrated parameters

Critical Duration Index: Anticipating Project Delays From Deterministic Schedule Information

[EN] 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.This research is supported by the National Social Science Fund projects (No. 20BJY010); National Social Science Fund Post-Financing Projects (No. 19FJYB017); Sichuan-Tibet Railway Major Fundamental Science Problems Special Fund (No. 71942006); Qinghai Natural Science Foundation (No. 2020-JY-736); List of Key Science and Technology Projects in China's Transportation Industry in 2018-International Science and Technology Cooperation Project (Nos. 2018-GH-006 and 2019-MS5-100); Emerging Engineering Education Research and Practice Project of Ministry of Education of China (No. E-GKRWJC20202914); Shaanxi Province Higher Education Teaching Reform Project (No. 19BZ016); and Humanities and Social Sciences Research Project of the Ministry of Education (21XJA752003).González-Cruz, M.; Ballesteros-Pérez, P.; Lucko, G.; Zhang, J. (2022). Critical Duration Index: Anticipating Project Delays From Deterministic Schedule Information. Journal of Construction Engineering and Management. 148(11):1-12. https://doi.org/10.1061/(ASCE)CO.1943-7862.00023871121481

Condition based maintenance optimization for multi-state wind power generation systems under periodic inspection

As the wind power system moves toward more efficient operation, one of the main challenges for managers is to determine a cost effective maintenance strategy. Most maintenance optimization studies for wind power generation systems deal with wind turbine components separately. However, there are economic dependencies among wind turbines and their components. In addition, most current researches assume that the components in a wind turbine only have two states, while condition monitoring techniques can often provide more detailed health information of components. This study aims to construct an optimal condition based maintenance model for a multi-state wind farm under the condition that individual components or subsystems can be monitored in periodic inspection. The results are demonstrated using a numerical example.info:eu-repo/semantics/publishedVersio