An Overview of Project Data for Integrated Project Management and Control

Authors

  • Mario Vanhoucke Faculty of Economics and Business Administration, Ghent University; Belgium Technology and Operations Management Area, Vlerick Business School, Ghent, Belgium; University College London, United Kingdom Belgium
  • José Coelho Universidade Aberta - Lisbon, Portugal INESC – Technology and Science - Porto, Portugal Portugal
  • José Coelho Universidade Aberta - Lisbon, Portugal INESC – Technology and Science - Porto, Portugal Portugal
  • Jordy Batselier Ghent University - Belgium Belgium

Keywords:

Project Data, Dynamic Scheduling, Monte Carlo Simulation, Baseline Scheduling, Schedule Risk Analysis, Project Control

Abstract

In this paper, an overview is given of the project data instances available in the literature to carry out academic research in the field of integrated project management and control. This research field aims at integrating static planning methods and risk analyses with dynamic project control methodologies using the state-of-the-art knowledge from literature and the best practices from the professional project management discipline. Various subtopics of this challenging discipline have been investigated from different angles, each time using project data available in literature, obtained from project data generators or based on a sample of empirical case studies. This paper gives an overall overview of the wide variety of project data that are available and are used in various research publications. It will be shown how the combination of artificial data and empirical data leads to improved knowledge on and deeper insights into the structure and characteristics of projects useful for academic research and professional use. While the artificial data can be best used to test novel ideas under a strict design in a controlled academic environment, empirical data can serve as the necessary validation step to translate the academic research results into practical ideas, aiming at narrowing the bridge between the theoretical knowledge and practical relevance. A summary of the available project data discussed in this paper can be downloaded from http://www.projectmanagement.ugent.be/research/data

Author Biographies

  • Mario Vanhoucke, Faculty of Economics and Business Administration, Ghent University; Belgium Technology and Operations Management Area, Vlerick Business School, Ghent, Belgium; University College London, United Kingdom Belgium

    Biography Prof. Dr. Mario Vanhoucke is professor at the Ghent University and head of the department of Information Science and Operations Management. He is also part-time professor at Vlerick Leuven Gent Management School (Belgium) and University College of London (UK). He teaches Project Management, Business Statistics and Applied Operations Research. Read more. Teaching Mario teaches “Project Management” in the MSc course module CEGEG041 at University College of London (UK) and Ghent University (Belgium). He also teaches “Decision Sciences” and “Business Statistics” at Vlerick Leuven Gent Management School (Belgium) and Beijing University (China), “Applied Operations Research” at Ghent University (Belgium), and Read more. Research Mario‘s research interests include project management, project risk management and project control (Earned Value Management) as well as health-care optimisation and machine scheduling. Here you can see some of his recent research projects. http://www.ucl.ac.uk/msi/profile/mario-vanhoucke/research Publications “New computational results for the discrete time/cost trade-off problem in project networks”, Journal of the Operational Research Society, 1998, 49 (11), 1153-1163 (with E. Demeulemeester, B. Foubert, W. Herroelen and M. Vanhoucke). “An exact procedure for the resource-constrained weighted earliness-tardiness project scheduling problem”, Annals of Operations Research, 2001, 102, 179-196 (with E. Demeulemeester and W. Herroelen). “On maximizing the net present value of a project under renewable resource constraints”, Management Science, 2001, 47, 1113-1121 (with E. Demeulemeester and W. Herroelen). "Scheduling projects with linearly time-dependent cash flows to maximize the net present value", International Journal of Production Research, 2001, 39, 3159-3181 (with E. Demeulemeester and W. Herroelen). “Discrete time/cost trade-offs in project scheduling with time-switch constraints”, Journal of the Operational Research Society, 2002, 53, 741-751 (with E. Demeulemeester and W. Herroelen). “A random network generator for activity-on-the-node networks”, Journal of Scheduling, 2003, 6, 13-34 (with E. Demeulemeester and W. Herroelen). “Progress payments in project scheduling problems”, European Journal of Operational Research, 2003, 148, 604-620 (with E. Demeulemeester and W. Herroelen). “New computational results for the discrete time/cost trade-off problem with time-switch constraints”, European Journal of Operational Research, 2005, 165, 359-374. “A bi-population based genetic algorithm for the RCPSP”, Lecture Notes in Computer Science, 2005, 3483, 378-387 (with D. Debels). “A hybrid scatter search/electromagnetism meta-heuristic for project scheduling”, European Journal of Operational Research, 2006, 169, 638-653 (with D. Debels, B. De Reyck, and R. Leus). “Work continuity constraints in project scheduling”, Journal of Construction Engineering and Management, 2006, 132, 14-25. “A simulation and evaluation of earned value metrics to forecast the project duration”, Journal of the Operational Research Society, 2007, 58, 1361–1374 (with S. Vandevoorde). “A decomposition-based genetic algorithm for the resource-constrained project scheduling problem”, Operations Research, 2007, 55, 457-469 (with D. Debels). “An electromagnetism meta-heuristic for the nurse scheduling problem”, Journal of Heuristics, 2007, 13, 359-385 (with B. Maenhout). “A simulation analysis of errors in the design of costing systems”, The Accounting Review, 2007, 82, 939-962 (with E. Labro). “The discrete time/cost trade-off problem: extensions and heuristic procedures”, Journal of Scheduling, 2007, 10, 311-326 (with D. Debels). “A comparison and hybridization of crossover operators for the nurse scheduling problem”, Annals of Operations Research, 2008, 159, 333 – 353 (with B. Maenhout). “An evaluation of the adequacy of project network generators with systematically sampled networks”, European Journal of Operational Research, 2008, 187, 511–524 (with J. Coelho, D. Debels, B. Maenhout and L. Tavares). “The impact of various activity assumptions on the lead-time and resource utilization of resource-constrained projects”, Computers and Industrial Engineering, 2008, 54, 140–154 (with D. Debels). “Setup times and fast tracking in resource-constrained project scheduling”, Computers and Industrial Engineering, 2008, 54, 1062-1070. “Diversity in resource consumption patterns and costing system robustness to errors”, Management Science, 2008, 54, 1715 – 1730 (with E. Labro). “On the characterisation and generation of nurse scheduling problem instances”, European Journal of Operational Research, 2009, 196, 457–467 (with B. Maenhout). “The impact of incorporating nurse-specific characteristics in a cyclical scheduling approach”, Journal of the Operational Research Society, 2009, 60, 1683-1698 (with B. Maenhout). “A finite capacity production scheduling procedure for a Belgian steel company”, International Journal of Production Research, 2009, 47, 561 – 584 (with D. Debels). “A scatter search heuristic for maximizing the net present value of a resource-constrained project with fixed activity cash flows”, International Journal of Production Research, 2010, 48, 1983-2001. “A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem”, European Journal of Operational Research, 2010, 201, 409-418 (with V. Van Peteghem). “Using activity sensitivity and network topology information to monitor project time performance”, Omega - International Journal of Management Science, 2010, 38, 359-370. “Introducing optimization techniques to students: An exam case distribution model”, INFORMS Transactions on Education, 2010, 206, 155-167. “A hybrid scatter search heuristic for personalized crew rostering in the airline industry”, European Journal of Operational Research, 2010, 206, 155-167 (with B. Maenhout). “Branching strategies in a branch-and-price approach for a multiple objective nurse scheduling problem”, Journal of Scheduling, 2010, 13, 77-93 (with B. Maenhout). “On the dynamic use of project performance and schedule risk information during project tracking”, Omega - International Journal of Management Science, 2011, 39, 416-426. “Using resource scarceness characteristics to solve the multi-mode resource-constrained project scheduling problem”, Journal of Heuristics, 2011, To appear (with V. Van Peteghem) “An evolutionary approach for the nurse rerostering problem”, Computers and Operations Research, 2011, 38, 1400-1411 (with B. Maenhout). “Multi-mode resource-constrained project scheduling using RCPSP and SAT solvers”, European Journal of Operational Research, 2011, to appear (with J. Coelho) “On maximizing the net present value of a project under renewable resource constraints”, Management Science. “A decomposition-based genetic algorithm for the resource-constrained project scheduling problem”, Operations Research. “On maximizing the net present value of a project under renewable resource constraints”, Management Science, 2001, 47, 1113-1121 (with E. Demeulemeester, E. and W. Herroelen). “A simulation analysis of errors in the design of costing systems”, The Accounting Review, 2007, 82, 939-962 (with E. Labro). “A decomposition-based genetic algorithm for the resource-constrained project scheduling problem”, Operations Research, 2007, 55, 457-469 (with D. Debels). “Diversity in resource consumption patterns and costing system robustness to errors”, Management Science, 2008, 54, 1715 – 1730 (with E. Labro).

  • José Coelho, Universidade Aberta - Lisbon, Portugal INESC – Technology and Science - Porto, Portugal Portugal

    Prof Dr José Coelho is a researcher at the Technical University of Lisbon (Portugal) and Professor in the Universidade Aberta (Open University in Lisbon (Portugal)) in the Department of Sciences and Technology where he teaches courses on computer science such as “Programming”, “Introduction to Artificial Intelligence”, “Programming of Digital Artefacts”, and “Modern Heuristics”. His main research areas are on project scheduling, e-learning and digital art. He has published 6 papers in international journals, 10 papers in national journals and conferences, has produced 11 software packages, and developed 8 didactic and pedagogical tools. He is a regular reviewer for international journals and member of the coordinating council of the Department of Sciences and Technology, the commission of the Doctor’s Degree in Digital Media Arts, and vice-coordinator of the Master Degree in Graphical and Audiovisual Expression

  • José Coelho, Universidade Aberta - Lisbon, Portugal INESC – Technology and Science - Porto, Portugal Portugal

    Prof Dr José Coelho is a researcher at the Technical University of Lisbon (Portugal) and Professor in the Universidade Aberta (Open University in Lisbon (Portugal)) in the Department of Sciences and Technology where he teaches courses on computer science such as “Programming”, “Introduction to Artificial Intelligence”, “Programming of Digital Artefacts”, and “Modern Heuristics”. His main research areas are on project scheduling, e-learning and digital art. He has published 6 papers in international journals, 10 papers in national journals and conferences, has produced 11 software packages, and developed 8 didactic and pedagogical tools. He is a regular reviewer for international journals and member of the coordinating council of the Department of Sciences and Technology, the commission of the Doctor’s Degree in Digital Media Arts, and vice-coordinator of the Master Degree in Graphical and Audiovisual Expression

  • Jordy Batselier, Ghent University - Belgium Belgium

    Jordy Batselier holds Master's degrees in Civil Engineering (2011) and Business Economics (2012) from Ghent University (Belgium). Since 2012 he is working as a PhD researcher at the Operations Research & Scheduling research group of the Faculty of Economics and Business Administration of Ghent University. His research interest lies in project management, more particularly, in performing project control by means of earned value management. His specific research actions are focused on the empirical evaluation and development of forecasting techniques for project duration and cost, on which he has published several papers in international journals.

References

Anbari, F. (2003). Earned value project management method and extensions. Project Management Journal, 34(4):12-23.

Batselier, J. and Vanhoucke, M. (2015a). Construction and evaluation framework for a real-life project database. International Journal of Project Management, 33:697-710.

Batselier, J. and Vanhoucke, M. (2015b). Project regularity: Development and evaluation of a new project characteristic. Submitted to Journal of Systems Science and Systems Engineering.

Boctor, F. (1993). Heuristics for scheduling projects with resource restrictions and several resource-duration modes. International Journal of Production Research, 31:2547-2558.

Browning, T. and Yassine, A. (2010). A random generator of resource-constrained multi-project network problems. Journal of Scheduling, 13:143-161.

Colin, J. and Vanhoucke, M. (2014). Setting tolerance limits for statistical project control using earned value management. Omega The International Journal of Management Science, 49:107-122.

Colin, J. and Vanhoucke, M. (2015a). Developing a framework for statistical process control approaches in project management. International Journal of Project Management, 33(6):1289-1300.

Colin, J. and Vanhoucke, M. (2015b). Empirical perspective on activity durations for project management simulation studies. Journal of Construction Engineering and Management, 04015047, 1-13.

Cooper, D. (1976). Heuristics for Scheduling Resource-constrained Projects: An Experimental Investigation. Management Science, 22:1186-1194.

Dar-El, E. (1973). MALB - A heuristic technique for balancing large single-model assembly lines. IIE Transactions, 5:343-356.

Davies, E. (1974). An experimental investigation of resource allocation in multiactivity projects. Operational Research Quarterly, 24:587-591.

Davis, E. (1975). Project network summary measures constrained-resource scheduling. AIIE Transactions, 7:132-142.

De Reyck, B. and Herroelen, W. (1996). On the use of the complexity index as a measure of complexity in activity networks. European Journal of Operational Research, 91:347-366.

Debels, D. and Vanhoucke, M. (2007). A decomposition-based genetic algorithm for the resource-constrained project scheduling problems. Operations Research, 55:457-469.

Demeulemeester, E., Dodin, B., and Herroelen, W. (1993). A random activity network generator. Operations Research, 41:972-980.

Demeulemeester, E., Vanhoucke, M., and Herroelen, W. (2003). RanGen: A random network generator for activity-on-the-node networks. Journal of Scheduling, 6:17-38.

Drexl, A., Nissen, R., Patterson, J., and Salewski, F. (2000). ProGen/πx - an instance generator for resource-constrained project scheduling problems with partially renewable resources and further extensions. European Journal of Operational Research, 125:59-72.

Elmaghraby, S. and Herroelen, W. (1980). On the measurement of complexity in activity networks. European Journal of Operational Research, 5:223-234.

Elshaer, R. (2013). Impact of sensitivity information on the prediction of project's duration using earned schedule method. International Journal of Project Management, 31:579-588.

Fleming, Q. and Koppelman, J. (2010). Earned value project management. Project Management Institute, Newton Square, Pennsylvania, 4th edition.

Hahn, E. (2008). Mixture densities for project management activity times: A robust approach to PERT. European Journal of Operational Research, 188:450-459.

Hartmann, S. and Briskorn, D. (2010). A survey of variants and extensions of the resource-constrained project scheduling problem. European Journal of Operational Research, 207:1-15.

Hazır, Ö. (2015). A review of analytical models, approaches and decision support tools in project monitoring and control. International Journal of Project Management, 33(4):808-815.

Herroelen, W. and De Reyck, B. (1999). Phase transitions in project scheduling. Journal of the Operational Research Society, 50:148-156.

Hulett, D. (1996). Schedule risk analysis simplified. Project Management Network, 10:23-30.

Kaimann, R. (1974). Coefficient of network complexity. Management Science, 21:172-177.

Kaimann, R. (1975). Coefficient of network complexity: Erratum. Management Science, 21:1211-1212.

Kao, E. and Queyranne, M. (1982). On dynamic programming methods for assembly line balancing. Operations Research, 30:375-390.

Kolisch, R. and Sprecher, A. (1996). PSPLIB - A project scheduling problem library. European Journal of Operational Research, 96:205-216.

Kolisch, R., Sprecher, A., and Drexl, A. (1995). Characterization and generation of a general class of resource-constrained project scheduling problems. Management Science, 41:1693-1703.

Kotiah, T. and Wallace, N. D. (1973). Another look at the pert assumptions. Management Science, 20(1):44-49.

Kuhl, M. E., Lada, E. K., Steiger, N. M., Wagner, M. A., and Wilson, J. R. (2007). Introduction to modeling and generating probabilistic input processes for simulation. In Henderson, S., Biller, B., Hsieh, M., Shortle, J., Tew, J., and Barton, R., editors, Proceedings of the 2007 Winter Simulation Conference, pages 63-76. New Jersey: Institute of Electrical and Electronics Engineers.

Kwak, Y. H. and Ingall, L. (2007). Exploring monte carlo simulation applications for project management. Risk Management, 9(1):44-57.

Mastor, A. (1970). An experimental and comparative evaluation of production line balancing techniques. Management Science, 16:728-746.

Mohan, S., Gopalakrishnan, M., Balasubramanian, H., and Chandrashekar, A. (2007). A lognormal approximation of activity duration in pert using two time estimates. Journal of the Operational Research Society, 58(6):827-831.

Pascoe, T. (1966). Allocation of resources - CPM. Revue Française de Recherche Opérationnelle, 38:31-38.

Patterson, J. (1976). Project scheduling: The effects of problem structure on heuristic scheduling. Naval Research Logistics, 23:95-123.

Ragsdale, C. (1989). The current state of network simulation in project management theory and practice. Omega The International Journal of Management Science, 17(1):21-25.

Schonberger, R. (1981). Why projects are "always" late: A rationale based on manual simulation of a PERT/CPM network. Interfaces, 11:65-70.

Schwindt, C. (1995). A new problem generator for different resource-constrained project scheduling problems with minimal and maximal time lags. WIOR-Report-449. Institut für Wirtschaftstheorie und Operations Research, University of Karlsruhe.

Tavares, L. (1999). Advanced models for project management. Kluwer Academic Publishers, Dordrecht, 1999.

Tavares, L., Ferreira, J., and Coelho, J. (1999). The risk of delay of a project in terms of the morphology of its network. European Journal of Operational Research, 119:510-537.

Tavares, L., Ferreira, J., and Coelho, J. (2002). A comparative morphologic analysis of benchmark sets of project networks. International Journal of Project Management, 20:475-485.

Thesen, A. (1977). Measures of the restrictiveness of project networks. Networks, 7:193-208.

Trietsch, D., Mazmanyan, L., Govergyan, L., and Baker, K. R. (2012). Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation. European Journal of Operational Research, 216:386-396.

Uyttewaal, E. (2005). Dynamic Scheduling With Microsoft Office Project 2003: The book by and for professionals. Co-published with International Institute for Learning, Inc.

Van Peteghem, V. and Vanhoucke, M. (2014). An experimental investigation of metaheuristics for the multi-mode resource-constrained project scheduling problem on new dataset instances. European Journal of Operational Research, 235:62-72.

Vanhoucke, M. (2010a). Measuring Time - Improving Project Performance using Earned Value Management, volume 136 of International Series in Operations Research and Management Science. Springer.

Vanhoucke, M. (2010b). A scatter search heuristic for maximising the net present value of a resource-constrained project with fixed activity cash flow. International Journal of Production Research, 48:1983-2001.

Vanhoucke, M. (2010c). Using activity sensitivity and network topology information to monitor project time performance. Omega The International Journal of Management Science, 38:359-370.

Vanhoucke, M. (2011). On the dynamic use of project performance and schedule risk information during project tracking. Omega The International Journal of Management Science, 39:416-426.

Vanhoucke, M. (2012). Project Management with Dynamic Scheduling: Baseline Scheduling, Risk Analysis and Project Control, volume XVIII. Springer.

Vanhoucke, M. (2014). Integrated Project Management and Control: First comes the theory, then the practice. Management for Professionals. Springer.

Vanhoucke, M., Coelho, J., Debels, D., Maenhout, B., and Tavares, L. (2008). An evaluation of the adequacy of project network generators with systematically sampled networks. European Journal of Operational Research, 187:511-524.

Vanhoucke, M., Demeulemeester, E., and Herroelen, W. (2001). On maximizing the net present value of a project under renewable resource constraints. Management Science, 47:1113-1121.

Wauters, M. and Vanhoucke, M. (2014). Support vector machine regression for project control forecasting. Automation in Construction, 47:92-106.

Willems, L. and Vanhoucke, M. (2015). Classification of articles and journals on project control and earned value management. International Journal of Project Management, 33:1610-1634.

Williams, T. (1995). A classified bibliography of recent research relating to project risk management. European Journal of Operational Research, 85:18-38.

Downloads

Published

2022-05-20

Issue

Vol. 3 No. 3 (2016): The Journal of Modern Project Management

Section

Articles-JMPM

License

Copyright (c) 2022 Mario Vanhoucke, José Coelho, José Coelho, Jordy Batselier

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

An Overview of Project Data for Integrated Project Management and Control. (2022). The Journal of Modern Project Management, 3(3), 158. https://journalmodernpm.com/manuscript/index.php/jmpm/article/view/218

Similar Articles

1-10 of 479

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

1 2 > >>