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Seboni, L; Tutesigensi, A (2015)
Publisher: Association of Researchers in Construction Management
Languages: English
Types: Other
In multi-project environments, the decision of which project manager to allocate to which project directly affects organizational performance and therefore, it needs to be taken in a fair, robust and consistent manner. We argue that such a manner can be facilitated by a mathematical model that brings together all the relevant factors in an effective way. Content and thematic analyses of extant literature on optimization modelling were conducted to identify the major issues related to formulating a relevant mathematical model. A total of 200 articles covering the period 1959 to 2015 were reviewed. A deterministic integer programming model was formulated and implemented in OpenSolver. The utility of the model was demonstrated with an illustrative example to optimize the allocation of six project managers to six projects. The results indicate that the model is capable of making optimal allocations in less than one second, with a solution precision of 99%. These results compare well with some intuitive verification checks on certain expectations. For example, the most competent project manager was allocated to the highest priority project while the least competent project manager was allocated to the lowest priority project. Through this study, we have proposed a comprehensive and balanced approach by incorporating both hard and soft issues in our mathematical modelling, to address gaps in existing project manager-to-project (PM2P) allocation models as well as extending applications of mathematical modelling of the PM2P allocation problem to a “new” country and industry, with a view to complement managerial intuition. In an attempt to address gaps in existing mathematical models associated with challenges related to acceptance by industry practitioners, future work includes developing a graphical user interface to separate the model base and optimization software details from users, as part of a complete product to be validated as an industry application.
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    • Adair, J (2007) “Decision Making and Problem Solving Strategies”. London: Kogan Page.
    • Berry, J and Houston, K (1995) “Mathematical modelling”. London: Edward Arnold.
    • Brown, S L and Eisenhardt, K M (1995) Product Development: Past research, present findings, and future directions. “Academy of Management Journal”, 20, 343-378.
    • Burghes, D N and Wood, A D (1980) “Mathematical models in the social, management and life sciences”. New York: Wiley and Sons.
    • Ragsdale, C T (2015) “Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics”. Stamford: Cengage Learning.
    • Choothian, W, Khan, N, Mupemba, K Y, Robinson, K and Tunnitisupawong, V (2009) A Decision Support Model for Project Manager Assignments 2.0. In: Kocaoglu, D F (Ed.), Proceedings of PICMET '09, 2-6 August 2009, Portland, Oregon, 1415-1424.
    • Kocaoglu, D F (1983) A participative approach to program evaluation. IEEE Transactions on Engineering Management, EM-30, 112-118.
    • LeBlanc, L J, Randels, D J and Swann, T (2000) Heery International's Spreadsheet Optimization Model for Assigning Managers to Construction Projects. Interfaces, 30, 95-106.
    • Mason, A J (2011) OpenSolver - An Open Source Add-in to Solve Linear and Integer Programmes in Excel. In: Klatte, D (Ed.) Operations Research Proceedings, 401-406.
    • Meindl, B and Templ, M (2013) Analysis of Commercial and Free and Open Source Solvers for the Cell Suppression Problem. Transactions on Data Privacy, 6, 147-159.
    • Murthy, D N P, Page, N W and Rodin, E Y (1990) Mathematical modelling: a tool for problem solving in engineering, physical, biological and social sciences. Oxford: Pergamon.
    • Patanakul, P, Milosevic, D and Anderson, T R (2007) A Decision Support Model for Project Manager Assignments. IEEE Transactions on Engineering Management, 54, 548- 564.
    • Pinto, J K and Slevin, D P (1988) Critical success factors across the project life cycle. Project Management Journal, 19, 67-74.
    • Saaty, T L (1980) The Analytic Hierarchy Process. New York: McGraw-Hill.
    • Saaty, T L and Alexander, J M (1981) Thinking with models: mathematical models in the physical, biological, and social sciences. Oxford: Pergamon Press.
    • Seboni, L and Tutesigensi, A (2014) Development and verification of a conceptual framework for project manager-to-project (PM2P) allocations in multi-project environments. In: Kocaoglu, D F (Ed.), Proceedings of PICMET '14, 27-31 July 2014, Kanazawa, Japan. Infrastructure and Service Integration, 2477 - 2496.
    • Skabelund, J (2005) Are Nonperformers Killing Your Bottom Line? Credit Union Executive Newsletter, 31, 13.
    • Triantaphyllou, E (2000) Multi-Criteria Decision Making Methods: A Comparative Study. Dordrecht: Kluwer Academic Publishers.
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