Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-23T13:17:56.976Z Has data issue: false hasContentIssue false

Framework for FAMD-Based Identification of RCPSP-Constraints for Improved Project Scheduling

Published online by Cambridge University Press:  26 May 2022

M. Riesener
Affiliation:
RWTH Aachen University, Germany
M. Kuhn
Affiliation:
RWTH Aachen University, Germany
A. Keuper
Affiliation:
RWTH Aachen University, Germany
B. Lender*
Affiliation:
RWTH Aachen University, Germany
G. Schuh
Affiliation:
RWTH Aachen University, Germany

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Product development in today's manufacturing companies is characterized by multiple development projects under intense time constraints. This means that the success of projects impacts the company's success significantly. However, industrial practices show that many projects fail to meet their time targets. This paper presents a methodology to systematically improve project schedule adherence of development projects by combining exploratory data analysis of historic project data with project scheduling optimizations to enhance the project schedules and enable more successful projects.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

References

Artigues, C. (2010), Resource-constrained Project Scheduling, ISTE, v.37, John Wiley & Sons Inc, Hoboken.Google Scholar
Cavalcante, V.F., Cardonha, C.H. and Herrmann, R.G. (2013), “A Resource Constrained Project Scheduling Problem with Bounded Multitasking”, IFAC Proceedings Volumes, Vol. 46 No. 24, pp. 433437.CrossRefGoogle Scholar
Chan, A.P.C., Scott, D. and Chan, A.P.L. (2004), “Factors Affecting the Success of a Construction Project”, Journal of Construction Engineering and Management, Vol. 130 No. 1, pp. 153155.CrossRefGoogle Scholar
Demeulemeester, E.L. and Herroelen, W.S. (2002), Project Scheduling: A Research Handbook, Springer eBook Collection, Vol. 49, 1st ed. 2002, Springer US; Imprint Springer, New York, NY.Google Scholar
Denton, H.G. (1997), “Multidisciplinary team-based project work: planning factors”, Design Studies, Vol. 18 No. 2, pp. 155170.CrossRefGoogle Scholar
DIN 69909-1:2013-03, Multiprojektmanagement - Management von Projektportfolios, Programmen und Projekten - Teil 1: Grundlagen (2013), Beuth Verlag GmbH, Berlin.Google Scholar
Dubois, D. and Prade, H. (2008), “Handling Bipolar Queries in Fuzzy Information Processing”, in Galindo, J. (Ed.), Handbook of research on fuzzy information processing in databases, IGI Global (701 E. Chocolate Avenue Hershey Pennsylvania 17033 USA), Hershey, Pa, pp. 97114.Google Scholar
Escofier, B. (1979), “Traitement simultané de variables qualitatives et quantitatives en analyse factorielle”, Les cahiers de l'analyse des données, Vol. 4 No. 2, pp. 137146.Google Scholar
Fayyad, U.M., Piatetsky-Shapiro, G. and Smyth, P. (1996), “From Data Mining to Knowledge Discovery in Databases”, AI Magazine, No. 17, pp. 3754.Google Scholar
Feldhusen, J. and Grote, K.-H. (Eds.) (2013), Pahl/Beitz Konstruktionslehre: Methoden und Anwendung erfolgreicher Produktentwicklung, 8. Aufl. 2013, Springer Berlin Heidelberg, Berlin, Heidelberg.Google Scholar
Greenacre, M.J. (1991), “Interpreting multiple correspondence analysis”, Applied Stochastic Models and Data Analysis, Vol. 7 No. 2, pp. 195210.CrossRefGoogle Scholar
Habibi, F., Barzinpour, F. and Sadjadi, S.J. (2018), “Resource-constrained project scheduling problem: review of past and recent developments”, Journal of Project Management, pp. 5588.CrossRefGoogle Scholar
Haroune, M., Dhib, C., Néron, E., Soukhal, A., Babou, H.M. and Nanne, M. (2021), “Multi-project scheduling problems with shared multi-skill resource constraints”, Toulouse.Google Scholar
Hosseinian, A.H., Baradaran, V. and Bashiri, M. (2019), “Modeling of the time-dependent multi-skilled RCPSP considering learning effect”, Journal of Modelling in Management, Vol. 14 No. 2, pp. 521558.Google Scholar
Khan, M.E.E., Bouchard, G., Murphy, K.P. and Marlin, B.M. (2010), “Variational bounds for mixed-data factor analysis”, in Lafferty, J., Williams, C., Shawe-Taylor, J., Zemel, R. and Culotta, A. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.Google Scholar
Kroker, M. (2016), “Digitale Transformation: 40 Prozent der Fortune-500-Firmen verschwinden in nächster Dekade”, available at: https://blog.wiwo.de/look-at-it/2016/08/24/digitale-transformation-40-prozent-der-fortune-500-firmen-verschwinden-in-naechster-dekade/ (accessed 26 October 2021).Google Scholar
Islam, Mainul, and Faniran, M.D., O.O. (2005), “Structural equation model of project planning effectiveness”, Construction Management and Economics, Vol. 23 No. 2, pp. 215223.Google Scholar
Müncheberg, H. (2015), “Projektarbeit in Unternehmen weiter auf dem Vormarsch”, available at: https://www.hays.de/personaldienstleistung-aktuell/presse-mitteilung/projektarbeit-in-unternehmen-weiter-auf-dem-vormarsch (accessed 26 October 2021).Google Scholar
Neumann, K. (2003), Project Scheduling with Time Windows and Scarce Resources: Temporal and Resource-Constrained Project Scheduling with Regular and Nonregular Objective Functions, 1st ed., Springer Berlin Heidelberg, Berlin/Heidelberg.Google Scholar
Pagès, J. (2004), “Analyse factorielle de données mixtes”, Revue de statistique appliquée, Vol. 52 No. 4, pp. 93111.Google Scholar
Pawiński, G. and Sapiecha, K. (2012), “Resource Allocation Optimization in Critical Chain Method”, Annales UMCS, Informatica, Vol. 12 No. 1.Google Scholar
Phillips, J. (2003), PMP Project Management Professional Study Guide, McGraw-Hill Osborne Media.Google Scholar
Pinto, J.K. and Prescott, J.E. (1990), “PLANNING AND TACTICAL FACTORS IN THE PROJECT IMPLEMENTATION PROCESS”, Journal of Management Studies, Vol. 27 No. 3, pp. 305327.CrossRefGoogle Scholar
Institute, Project Management (2021), “Pulse of the Profession 2021”, available at: https://www.pmi.org/learning/thought-leadership/pulse/pulse-of-the-profession-2021 (accessed 26 October 2021).Google Scholar
PwC (2016), “Global Data and Analytics Survey”, available at: https://www.pwc.co.uk/issues/data-analytics/insights/big-decisions-2016.html (accessed 26 October 2021).Google Scholar
Reinsel, D., Gantz, J. and Rydning, J. (2018), Data Age 2025: The Digitization of the World from Edge to Core, available at: https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf (accessed 26 October 2021).Google Scholar
Riesener, M., Kuhn, M., Keuper, A. and Schuh, G. (2021), “Concept for competency-based resource allocation in multi-project environments”, in Proceedings of The 4th International Conference on Management, Economics and Finance, 10.-12.09.2021, Zurich, Switzerland, Diamond Scientific Publishing, 5770.Google Scholar
Saporta, G. (1990), “Simultaneous Analysis of Qualitative and Quantitative Data”, Atti della XXXV riunione scientifica; società italiana di Statistica, pp. 6372.Google Scholar
Schuh, G., Dölle, C., Becker, A., Jank, M.-H., Kress, J., Kuhn, M., Lauf, H., Menges, A., Schloesser, S. and Tittel, J. (2021), Sustainable Innovation: Nachhaltig Werte schaffen, 2. Aufl. 2021, Springer Berlin Heidelberg, Berlin, Heidelberg.Google Scholar
Snauwaert, J. and Vanhoucke, M. (2021), “A new algorithm for resource-constrained project scheduling with breadth and depth of skills”, European Journal of Operational Research, Vol. 292 No. 1, pp. 4359.Google Scholar
Solak, S., Clarke, J.-P.B., Johnson, E.L. and Barnes, E.R. (2010), “Optimization of R&D project portfolios under endogenous uncertainty”, European Journal of Operational Research, Vol. 207 No. 1, pp. 420433.CrossRefGoogle Scholar
Tönnes, C. (2021), “Datenbasierte Informationsmodelle zur explorativen Analyse von Anlagenkonfigurationen”, Dissertation, Werkzeugmaschinenlabor (WZL), RWTH Aachen, Aachen, 2021.Google Scholar
Wysocki, R.K. (2014), Effective project management: Traditional, agile, extreme, 7th ed., Wiley, Indianapolis, Indiana.Google Scholar
Yazici, H.J. (2020), “An exploratory analysis of the project management and corporate sustainability capabilities for organizational success”, International Journal of Managing Projects in Business, Vol. 13 No. 4, pp. 793817.CrossRefGoogle Scholar