Simulation has played an important role in project-management studies of the last decades, but in order for them to produce practical results, a realistic distribution model for activity durations is indispensable. The construction industry often has needed historical records of project executions, to serve as inputs to the distribution models, but a clearly outlined calibration procedure is not always readily available, nor are their results readily interpretable. This study seeks to illustrate how data from the construction industry can be used to derive realistic input distributions. Therefore, the Parkinson simulation model with a lognormal core is applied to a large empirical dataset from the literature and the results are described. From a discussion of these results, an empirical classification of project executions is presented. Three possible uses are presented for the calibration procedure and the classification in project management simulation studies. These were validated using a case study of a construction company.
When scheduling projects under resource constraints, assumptions are typically made with respect to the resource availability. In resource scheduling problems important assumptions are made with respect to the resource requirements. As projects are typically labour intensive, the underlying (personnel) resource scheduling problems tend to be complex due to different rules and regulations. In this paper, we aim to integrate these two interrelated scheduling problems to minimise the overall cost. For that purpose, we propose an exact algorithm for the project staffing with resource scheduling constraints. Detailed computational experiments are presented to evaluate different branching rules and pruning strategies and to compare the proposed procedure with other optimisation techniques.
This paper explores the use of multivariate regression methods for project schedule control within a statistical project control framework. These multivariate regression methods monitor the activity level performance of an ongoing project from the earned value management/earned schedule (EVM/ES) observations that are made at a high level of the work breakdown structure (WBS). These estimates can be used to calculate the longest path in the project and to produce warning signals for project schedule control. The effort that is spent by the project manager is thereby reduced, since a drill-down of the WBS is no longer required for every review period. An extensive computational experiment was set up to test and compare four distinct multivariate regression methods on a database of project networks. The kernel principal component regression method, when used with a radial base function kernel, was found to outperform the other presented regression methods.
This paper presents five Artificial Intelligence (AI) methods to predict the final duration of a project. A methodology that involves Monte Carlo simulation, Principal Component Analysis and cross-validation is proposed and can be applied by academics and practitioners. The performance of the AI methods is assessed by means of a large and topologically diverse dataset and is benchmarked against the best performing Earned Value Management/Earned Schedule (EVM/ES) methods. The results show that the AI methods outperform the EVM/ES methods if the training and test sets are at least similar to one another. Additionally, the AI methods report excellent early and mid-stage forecasting results. A robustness experiment gradually increases the discrepancy between the training and test sets and demonstrates the limitations of the newly proposed AI methods.
Export search results
The export option will allow you to export the current search results of the entered query to a file. Different
formats are available for download. To export the items, click on the button corresponding with the preferred download format.
By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.
To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export.
The amount of items that can be exported at once is similarly restricted as the full export.
After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.