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Title
Abstract
Figures
Introduction
Literature Review
Illustration of the Method
Results and Discussion
Conclusions and Future Works
References
All Topics
Business and Management
Risk Analysis and Management

On the Project risk baseline R3

Profile image of Fernando AcebesFernando AcebesProfile image of José Manuel González-VaronaJosé Manuel González-Varona

2021, Computers & Industrial Engineering

https://doi.org/10.1016/J.CIE.2021.107537
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38 pages

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Abstract

Obtaining a viable schedule baseline that meets all project constraints is one of the main issues for project managers. The literature on this topic focuses mainly on methods to obtain schedules that meet resource restrictions and, more recently, financial limitations. The methods provide different viable schedules for the same project, and the solutions with the shortest duration are considered the best-known schedule for that project. However, no tools currently select which schedule best performs in project risk terms. To bridge this gap, this paper aims to propose a method for selecting the project schedule with the highest probability of meeting the deadline of several alternative schedules with the same duration. To do so, we propose integrating aleatory uncertainty into project scheduling by quantifying the risk of several execution alternatives for the same project. The proposed method, tested with a wellknown repository for schedule benchmarking, can be applied to any project type to help managers to select the project schedules from several alternatives with the same duration, but the lowest risk.

Figures (11)
Fig. 1. A simplified scheme of the steps to obtain a feasible schedule for a project mainly the availability of both resources and funds. project scheduling is concerned, obtaining a feasible schedule requires considering
Fig. 1. A simplified scheme of the steps to obtain a feasible schedule for a project mainly the availability of both resources and funds. project scheduling is concerned, obtaining a feasible schedule requires considering
project to its end. By joining the consecutive points, we obtain the schedule risk
project to its end. By joining the consecutive points, we obtain the schedule risk
Acebes et al. (2020), which is defined as the area under the Schedule Risk Baseline
Acebes et al. (2020), which is defined as the area under the Schedule Risk Baseline
the project is underway and, thus, the more chances of not meeting the project end date.
the project is underway and, thus, the more chances of not meeting the project end date.
the same duration (same TMS) (Fig. 6.). schedule), we find different feasible schedules that meet the resource constraints with
the same duration (same TMS) (Fig. 6.). schedule), we find different feasible schedules that meet the resource constraints with
allows us to calculate the value corresponding to the total project risk (SRV) as the area Fig. 7. Graphical representation of the SRB for the problem project "mp_j30_a2_nr3" After calculating the total risk (SRV) for each solution to the problem (i.e. each viable
allows us to calculate the value corresponding to the total project risk (SRV) as the area Fig. 7. Graphical representation of the SRB for the problem project "mp_j30_a2_nr3" After calculating the total risk (SRV) for each solution to the problem (i.e. each viable
same duration for the same project (problem mp_j30_a2_nr3 from MPSPLib) Table. 1: Results obtained after simulating eight different feasible schedules with the average duration by considering the uncertainty of activities and the total risk (SRV). duration of activities and performing the simulation, we observe that the average
same duration for the same project (problem mp_j30_a2_nr3 from MPSPLib) Table. 1: Results obtained after simulating eight different feasible schedules with the average duration by considering the uncertainty of activities and the total risk (SRV). duration of activities and performing the simulation, we observe that the average
time units with scheduling method WPR_GA to 66.76 time units with the scheduling
time units with scheduling method WPR_GA to 66.76 time units with the scheduling
than in the other alternative schedules. schedule implies that the deviation from the expected project finish date is more limited
than in the other alternative schedules. schedule implies that the deviation from the expected project finish date is more limited

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References (97)

  1. References
  2. Acebes, F., Pajares, J., Galán, J.M., López-Paredes, A., 2014a. A new approach for project control under uncertainty. Going back to the basics. Int. J. Proj. Manag. 32, 423-434.
  3. Acebes, F., Pajares, J., Galán, J.M., López-Paredes, A., 2014b. Exploring the influence of Seasonal Uncertainty in Project Risk Management. Procedia -Soc. Behav. Sci. 119, 329-338.
  4. Acebes, F., Pajares, J., González-Varona, J.M., López-Paredes, A., 2020. Project risk management from the bottom-up: Activity Risk Index. Cent. Eur. J. Oper. Res.
  5. Acebes, F., Pereda, M., Poza, D., Pajares, J., Galán, J.M., 2015. Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques. Int. J. Proj. Manag. 33, 1597-1609.
  6. Afshar-Nadjafi, B., 2016. A new proactive approach to construct a robust baseline schedule considering quality factor. Int. J. Ind. Syst. Eng. 22, 63-72.
  7. Alavipour, S.M.R., Arditi, D., 2019. Time-cost tradeoff analysis with minimized project financing cost. Autom. Constr. 98, 110-121.
  8. Allahi, F., Cassettari, L., Mosca, M., 2017. Stochastic Risk Analysis and Cost Contingency Allocation Approach for Construction Projects Applying Monte Carlo Simulation, in: World Congress on Engineering. WCE 2017. London.
  9. Alleman, G.B., Coonce, T.J., Price, R.A., 2018. What is Risk? Meas. News 01. Association for Project Management, 2004. Project Risk Analysis and Management (PRAM) Guide, 2nd ed. ed. APM, High Wycombe, Bucks UK.
  10. Ballesteros-Pérez, P., Narváez, A.C., Mateo, M.O., Fernández, A.P., Vanhoucke, M., P r e p r i n t 2020. Forecasting the Project Duration Average and Standard Deviation from Deterministic Schedule Information. Appl. Sci. 10.
  11. Blazewicz, J., Lenstra, J.K., Rinnooy Kan, A.H.G., 1983. Scheduling subject to resource constraints: classification and complexity. Discret. Appl. Math. 5, 11-24.
  12. Caron, F., Fumagalli, M., Rigamonti, A., 2007. Engineering and contracting projects: A value at risk based approach to portfolio balancing. Int. J. Proj. Manag. 25, 569- 578.
  13. Chapman, C.B., 2006. Key points of contention in framing assumptions for risk and uncertainty management. Int. J. Proj. Manag. 24, 303-313.
  14. Chapman, C.B., Ward, S., 2003. Project Risk Management: Processes, Techniques and Insights, 2nd ed. ed. Chichester, New York.
  15. Chapman, C.B., Ward, S., 2000. Estimation and evaluation of uncertainty: a minimalist first pass approach. Int. J. Proj. Manag. 18, 369-383.
  16. Colin, J., Vanhoucke, M., 2016. Empirical Perspective on Activity Durations for Project-Management Simulation Studies. J. Constr. Eng. Manag. 142, 04015047-1.
  17. Cox, L.A., 2008. What's wrong with risk matrices? Risk Anal. 28, 497-512.
  18. Crawford, L., Pollack, J., England, D., 2006. Uncovering the trends in project management : Journal emphases over the last 10 years. Int. J. Proj. Manag. 24, 175-184.
  19. Dasović, B., Galić, M., Klanšek, U., 2020. A survey on integration of optimization and project management tools for sustainable construction scheduling. Sustain. 12.
  20. Deshmukh, G.K., Mukerjee, H.S., Prasad, U.D., 2020. Risk Management in Global CRM IT Projects. Bus. Perspect. Res.
  21. Dey, P.K., Clegg, B., Cheffi, W., 2013. Risk management in enterprise resource planning implementation: A new risk assessment framework. Prod. Plan. Control 24, 1-14.
  22. Doskočil, R., 2015. An Evaluation of Total Project Risk Based on Fuzzy Logic. Bus. Theory Pract. 17, 23-31.
  23. Dowie, J., 1999. Against risk. Risk Decis. Policy 4, 57-73.
  24. El-Kholy, A.M., Tahwia, A.M., Elsayed, M.M., 2020. Prediction of simulated cost contingency for steel reinforcement in building projects: ANN versus regression- based models. Int. J. Constr. Manag. 0, 1-15.
  25. El-Sayegh, S.M., Manjikian, S., Ibrahim, A., Abouelyousr, A., Jabbour, R., 2018. Risk identification and assessment in sustainable construction projects in the UAE. Int. J. Constr. Manag. 0, 1-10.
  26. Elazouni, A.M., Gab-Allah, A.A., 2004. Finance-based scheduling of construction projects using integer programming. J. Constr. Eng. Manag.
  27. Eldosouky, I.A., Ibrahim, A.H., Mohammed, H.E.D., 2014. Management of construction cost contingency covering upside and downside risks. Alexandria Eng. J. 53, 863-881.
  28. Elms, D.G., 2004. Structural safety: Issues and progress. Prog. Struct. Eng. Mater. 6, 116-126.
  29. Emblemsvåg, J., Kjølstad, L.E., 2006. Qualitative risk analysis: Some problems and remedies. Manag. Decis. 44, 395-408.
  30. European Commission, 2018. Project Management Methodology. Guide 3.0, Conference Record -IEEE Machine Tools Industry Conference. Publications P r e p r i n t Office of the European Union, Brussels / Luxembourg,.
  31. Farooq, M.U., Thaheem, M.J., Arshad, H., 2018. Improving the risk quantification under behavioural tendencies: A tale of construction projects. Int. J. Proj. Manag. 36, 414-428.
  32. Fendley, L.G.G., 1968. Towards the development of a complete multi-project scheduling system. J. Ind. Eng. 19, 505-515.
  33. Fergany, M., El-Nawawy, O., Badawy, M., 2020. Estimation of the Overall risk in Residential Building in Egypt. Int. J. Sci. Eng. Res. 11, 1568-1574.
  34. Frank, M., 1999. Treatment of uncertainties in space nuclear risk assessment with examples from Cassini mission implications. Reliab Eng Syst Safe 66, 203-221.
  35. Gavrysh, O., Melnykova, V., 2019. Project risk management of the construction industry enterprises based on fuzzy set theory. Probl. Perspect. Manag. 17, 203- 213.
  36. Ghaffari, M., Sheikhahmadi, F., Safakish, G., 2014. Modeling and risk analysis of virtual project team through project life cycle with fuzzy approach. Comput. Ind. Eng. 72, 98-105.
  37. Hazir, Ö., 2015. A review of analytical models, approaches and decision support tools in project monitoring and control. Int. J. Proj. Manag. 33, 808-815.
  38. Hillson, D., 2014a. How to manage the risks you didn't know you were taking. PMI® Glob. Congr. 1-8.
  39. Hillson, D., 2014b. Managing Overall Project Risk, in: PMI Global Congress Proceedings -Dubai, EAU. pp. 1-9.
  40. Hillson, D., 2014c. How risky is your project -and what are you doing about it ? PMI P r e p r i n t Glob. Congr. Proc. -Phoenix, Arizona, USA 1-10.
  41. Hillson, D., 2009. Managing Risk in Projects. Gower Publishing, Ltd.
  42. Hillson, D., 2002a. Defining risk: a debate. J. Inf. Technol. Manag. 15, 11.
  43. Hillson, D., 2002b. Extending the risk process to manage opportunities. Int. J. Proj. Manag. 20, 235-240.
  44. Hillson, D., Simon, P., 2012. Practical Project Risk Management: The ATOM Methodology, Second Edi. ed. Management Concepts Inc, Tysons Corner, Virginia.
  45. Homberger, J., 2007. Multi project scheduling problems [WWW Document]. URL http://www.mpsplib.com/ (accessed 2.15.20).
  46. Hoseini, E., Bosch-Rekveldt, M., Hertogh, M., 2020a. Cost Contingency and Cost Evolvement of Construction Projects in the Preconstruction Phase. J. Constr. Eng. Manag. 146, 05020006.
  47. Hoseini, E., van Veen, P., Bosch-Rekveldt, M., Hertogh, M., 2020b. Cost Performance and Cost Contingency during Project Execution: Comparing Client and Contractor Perspectives. J. Manag. Eng. 36, 05020006.
  48. Howell, D., Windahl, C., Seidel, R., 2010. A project contingency framework based on uncertainty and its consequences. Int. J. Proj. Manag. 28, 256-264.
  49. Hsieh, M.Y., Hsu, Y.C., Lin, C.T., 2018. Risk assessment in new software development projects at the front end: a fuzzy logic approach. J. Ambient Intell. Humaniz. Comput. 9, 295-305.
  50. Hulett, D.T., 2011. Integrated Cost-Schedule Risk Analysis. Gower, Farnham, UK. International Standards Organisation, 2018. ISO31000:2018 Risk management - P r e p r i n t Guidelines. Iso 31000.
  51. Jaafari, A., 2001. Management of risks , uncertainties and opportunities on projects : time for a fundamental shift. Int. J. Proj. Manag. 19, 89-101.
  52. Jordan, G.B., Hage, J., Mote, J., Hepler, B., 2005. Investigating differences among research projects and implications for managers. R&D Manag. 35, 501-512.
  53. Karam, A., Lazarova-Molnar, S., 2013. Recent trends in solving the deterministic resource constrained Project Scheduling Problem. 2013 9th Int. Conf. Innov. Inf. Technol. IIT 2013 124-129.
  54. Kelley, J.E., Walker, M.R., 1959. Critical-Path Planning and Scheduling. Pap. Present. December 1-3, 1959, East. Jt. IRE-AIEE-ACM Comput. Conf. -IRE-AIEE-ACM '59 32, 160-173.
  55. Khedr, M.K., 2006. Project Risk Management Using Monte Carlo Simulation. AACE Int. Trans.
  56. Kimiagari, S., Keivanpour, S., 2019. An interactive risk visualisation tool for large- scale and complex engineering and construction projects under uncertainty and interdependence. Int. J. Prod. Res. 57, 6827-6855.
  57. Kolisch, R., Sprecher, A., 1996. PSPLIB -A Project Scheduling Problem Library.pdf. Eur. J. Oper. Res. 96, 205-216.
  58. Kwak, Y.H., Ingall, L., 2007. Exploring Monte Carlo Simulation Applications for Project Management. Risk Manag. 9, 44-57.
  59. Kwon, H., Kang, C.W., 2019. Improving Project Budget Estimation Accuracy and Precision by Analyzing Reserves for Both Identified and Unidentified Risks. Proj. Manag. J. 50, 86-100.
  60. Leopoulos, V.N., Kirytopoulos, K.A., Malandrakis, C., 2006. Risk management for SMEs: Tools to use and how. Prod. Plan. Control 17, 322-332.
  61. Li, F., Xu, Z., Li, H., 2021. A multi-agent based cooperative approach to decentralized multi-project scheduling and resource allocation. Comput. Ind. Eng. 151, 106961.
  62. Little, T., 2005. Context adaptive agility: managing complexity and uncertainty. IEEE Softw. 22, 28-35.
  63. Liu, G., Zhang, J., Zhang, W., Zhou, X., 2007. Risk assessment of virtual enterprise based on the fuzzy comprehensive evaluation method. IFIP Adv. Inf. Commun. Technol. 251 VOLUME, 58-66.
  64. Malcolm, D.G., Roseboom, J.H., Clark, C.E., Fazar, W., 1959. Application of a technique for research and development program evaluation. Oper. Res. 7, 646- 669.
  65. Markowitz, H.M., 1959. Portfolio Selection: Efficient Diversification of Investments.
  66. Millington, D., Stapleton, J., 2005. Developing a RAD standard. IEEE Softw. 12, 54- 55.
  67. Mohamed, E., Jafari, P., Abourizk, S., 2020. Fuzzy-based multivariate analysis for input modeling of risk assessment in wind farm projects. Algorithms 13, 1-28.
  68. OGC, 2009. Managing Successful Projects with PRINCE2, 2009th ed.
  69. Pajares, J., López-Paredes, A., 2011. An extension of the EVM analysis for project monitoring: The Cost Control Index and the Schedule Control Index. Int. J. Proj. Manag. 29, 615-621.
  70. Pearson, A.W., 1990. Innovation strategy. Technovation 10, 185-192.
  71. Pellerin, R., Perrier, N., 2019. A review of methods, techniques and tools for project P r e p r i n t planning and control. Int. J. Prod. Res. 57, 2160-2178.
  72. Pellerin, R., Perrier, N., Berthaut, F., 2020. A survey of hybrid metaheuristics for the resource-constrained project scheduling problem. Eur. J. Oper. Res. 280, 395-416.
  73. Perminova, O., Gustafsson, M., Wikström, K., 2008. Defining uncertainty in projects - a new perspective. Int. J. Proj. Manag. 26, 73-79.
  74. Pinha, D.C., Ahluwalia, R.S., 2019. Flexible resource management and its effect on project cost and duration. J. Ind. Eng. Int. 15, 119-133.
  75. Project Management Institute, 2017. A Guide to the Project Management Body of Knowledge: PMBoK(R) Guide. Sixth Edition. Project Management Institute Inc.
  76. Project Management Institute, 2009. Practice Standard for Project Risk Management. Project Management Institute, Inc., Newtown Square, Pennsylvania 19073-3299 USA. Ratbe, D., King, W.R., Kim, Y.-G., 1999. The fit between project characteristics and application development methodologies: a contingency approach. J. Comput. Info. Syst 40, 26.
  77. Rezaei, F., Najafi, A.A., Ramezanian, R., 2020. Mean-conditional value at risk model for the stochastic project scheduling problem. Comput. Ind. Eng. 142, 106356.
  78. Rezaie, K., Amalnik, M.S., Gereie, A., Ostadi, B., Shakhseniaee, M., 2007. Using extended Monte Carlo simulation method for the improvement of risk management: Consideration of relationships between uncertainties. Appl. Math. Comput. 190, 1492-1501.
  79. Ruiz-Martin, C., Poza, D., 2015. Project configuration by means of network theory. Int. J. Proj. Manag. 33, 1755-1767.
  80. Schafer, G., 1976. A Mathematical Theory of Evidence, Princeton. ed. Princeton, NY.
  81. Shenhar, A.J., Dvir, D., 2007. Reinventing Project Management: The Diamond Approach to Successful Growth and Innovation. Harvard Business School Press, Boston.
  82. Taylor, J.C., 2008. Project scheduling and cost control: Planning, monitoring and controlling the baseline. J. Ross Publishing.
  83. Traynor, B.A., Mahmoodian, M., 2019. Time and cost contingency management using Monte Carlo simulation. Aust. J. Civ. Eng. 17, 11-18.
  84. Trietsch, D., Mazmanyan, L., Gevorgyan, L., Baker, K.R., 2012. Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation. Eur. J. Oper. Res. 216, 386-396.
  85. Turner, J.R., Cochrane, R.A., 1993. Goals and methods matrix: coping with projects with ill defined goals and/or methods of achieving them. Int. J. Proj. Manag. 11, 93-102.
  86. Vanhoucke, M., 2012. Project Management with Dynamic Scheduling: Baseline Scheduling, Risk Analysis and Project Control. Springer.
  87. Vanhoucke, M., 2011. On the dynamic use of project performance and schedule risk information during project tracking. Omega 39, 416-426.
  88. Vanhoucke, M., 2010. Measuring Time. Improving Project Performance Using Earned Value Management, International Series in Operations Research & Management Science: Vol. 136. Springer.
  89. Villafáñez, F.A., Poza, D., López-Paredes, A., Pajares, J., 2018. A unified nomenclature for project scheduling problems (RCPSP and RCMPSP). Dir. y Organ. 64, 56-60.
  90. Villafáñez, F.A., Poza, D., López-Paredes, A., Pajares, J., Acebes, F., 2020. Portfolio scheduling: an integrative approach of limited resources and project prioritization. J. Proj. Manag. 5, 103-116.
  91. Villafáñez, F.A., Poza, D., López-Paredes, A., Pajares, J., Olmo, R. del, 2019. A generic heuristic for multi-project scheduling problems with global and local resource constraints (RCMPSP). Soft Comput. 23, 3465-3479.
  92. Vose, D., 2008. Risk Analysis: a Quantitative Guide, 3rd ed. ed. Wiley, Chichester, U.K. Ward, S.C., 1999. Assessing and managing important risks. Int. J. Proj. Manag. 17, 331-336.
  93. Wauters, T., Verbeeck, K., De Causmaecker, P., Vanden Berghe, G., 2015. A learning- based optimization approach to multi-project scheduling. J. Sched. 18, 61-74.
  94. Williams, T.M., 1995. A classified bibliography of recent research relating to project risk management. Eur. J. Oper. Res. 85, 18-38.
  95. Wirawan, J.A.B., Garniwa, I., 2018. Risk analysis development of solar floating power plant in the sea with Monte Carlo method. Proc. -2018 3rd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2018 396-401.
  96. Xie, G., Zhang, J., Lai, K.K., 2006. Risk avoidance in bidding for software projects based on life cycle management theory. Int. J. Proj. Manag. 24, 516-521.
  97. Zhang, W.G., Mei, Q., Lu, Q., Xiao, W.L., 2011. Evaluating methods of investment project and optimizing models of portfolio selection in fuzzy uncertainty. Comput. Ind. Eng. 61, 721-728.

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Slawomir Zadrozny

2015 IEEE Symposium Series on Computational Intelligence, 2015

In this paper we continue research into Risk-Aware Project Scheduling Problem (RAPSP), a new class of project scheduling problems defined in [16], that includes risk management issues typical for real-life scenarios. We introduce a new RAPSP solver inspired by GRASP algorithm used for solving traditional stochastic project scheduling problems and compare it with previously defined methods. We also define a new set of benchmark problems, including a class of projects that may fail completely, and verify the efficacy of all our proactive and reactive approaches (both new and existing) for problems of varying complexity and characteristics.

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Project Scheduling: Improved approach to incorporate uncertainty using Bayesian Networks
Norman Fenton

2007

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Approaches for Estimating Threats to Project Schedule
Alankrita Aggarwal

Risk management is perilous to the success of any software project. The project schedule is the core of the project planning. In the process of software project development, risk scheduling is one of the most significant disciplines that cannot be grasped by anyone. So, evaluating risks to the schedule is complex. This paper presents different strategies for schedule risk analysis.

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Towards a model for selection and scheduling of risky projects
Andres Medaglia

Systems and Information Engineering …, 2005

The planning process of public and private companies relies on optimal project selection and scheduling and the efficient allocation of scarce resources. This process is complicated due in part to the fact that project investment must consider multiple criteria, project cash flows are uncertain, and there are several operational business and technical constraints. The proposed mixed-integer programming model assists the planning manager/analyst by choosing from a bank of projects in which projects to invest and when to invest. The model maximizes the sum of net present values of the chosen projects while minimizing their variance. The model satisfies simultaneously a set of precedence relations among projects; early and tardy project starting dates; exogenous budget limits; and endogenous project cash flow generation. Finally, by quantifying the opportunity cost, the model shows how arbitrary project selection and sequencing can reflect non-desirable solutions for the company and the society.

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