Multicriteria decision aiding in project planning using dynamic programming and simulation
AbstractThe concept of “project planning” is not uniformly understood. Some authors reduce it to scheduling – determining the dates for performing schedule activities and deadlines for reaching milestones. In this paper, as Nicholas and Steyn (2008) we assume that planning is a process that includes a number of phases and starts shortly after business need, contract request, or request for proposal has been received. We propose a methodology, that can be used for solving problems that the decision maker (DM) is faced when basic assumptions defining the project are made. According to A Guide to the Project Management Body of Knowledge (2004) project management is accomplished through processes that can be aggregated into five groups: initiating process group, planning process group, executing process group, monitoring and controlling group, and closing process group. As our approach refers to early stages of project life cycle, it can be used in both initiating processes, as well as planning ones. Although we do not focus on scheduling, the decisions considered here determine the project’s profitability and define the framework for preparing a project schedule. A variety of decisions have to be made while project scope is defined and project plan is prepared. Selection of a new project or a group of projects, as well as decisions how to implement them involve prediction and comparison of future outcomes. In real world, however, these values are not known with certainty. Thus, the DM faced with such decision, has also to face some degree of uncertainty. Moreover, while evaluating project’s options,he/she has to take into account multiple criteria. Although financial analysis plays the key role in project planning, other issues are also important. The purpose for project is to achieve a set of objectives, that cannot be attained by standard operational work. Projects are, therefore, utilized as a means of achieving organization’s strategic plan. As profitability is not the only goal considered when the strategy is formulated, so various criteria should be taken into account when alternate ways of project completion are compared. The aim of this paper is to present a simple, yet comprehensive, methodology for project planning that permits the consideration of both multiple criteria and risk. Our approach combines dynamic programming, simulation, stochastic dominance rules and lexicographic approach. The paper is organized as follows. Section 1 describes the project planning process and defines problems considered in this paper. Next section gives a literature overview. In section 3 new methodology for project planning decisions is introduced. Section 4 presents a numerical example. We finish with some conclusions and suggestions for future research in the last section.
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Bibliographic InfoPaper provided by Département des sciences administratives, UQO in its series RePAd Working Paper Series with number UQO-DSA-wp2202011.
Length: 32 pages
Date of creation: 21 Oct 2011
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Project management; Multicriteria decision;
Find related papers by JEL classification:
- M10 - Business Administration and Business Economics; Marketing; Accounting - - Business Administration - - - General
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- G. Edward Fox & Norman R. Baker, 1985. "Project Selection Decision Making Linked to a Dynamic Environment," Management Science, INFORMS, vol. 31(10), pages 1272-1285, October.
- Richard F. Hespos & Paul A. Strassmann, 1965. "Stochastic Decision Trees for the Analysis of Investment Decisions," Management Science, INFORMS, vol. 11(10), pages B244-B259, August.
- Eric Wong & George Norman & Roger Flanagan, 2000. "A fuzzy stochastic technique for project selection," Construction Management and Economics, Taylor & Francis Journals, vol. 18(4), pages 407-414.
- Dominique Foray, 2006. "The Economics of Knowledge," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262562235, January.
- Martel, Jean M. & d'Avignon, Gilles R., 1982. "Projects ordering with multicriteria analysis," European Journal of Operational Research, Elsevier, vol. 10(1), pages 56-69, May.
- Heidenberger, Kurt, 1996. "Dynamic project selection and funding under risk: A decision tree based MILP approach," European Journal of Operational Research, Elsevier, vol. 95(2), pages 284-298, December.
- Daniel Granot & Dror Zuckerman, 1991. "Optimal Sequencing and Resource Allocation in Research and Development Projects," Management Science, INFORMS, vol. 37(2), pages 140-156, February.
- Hadar, Josef & Russell, William R, 1969. "Rules for Ordering Uncertain Prospects," American Economic Review, American Economic Association, vol. 59(1), pages 25-34, March.
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