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A multi-criteria prediction model for project risk classifications
Stockholm University.
2013 (engelsk)Inngår i: International Journal of Decision Sciences, Risk and Management, ISSN 1753-7169, E-ISSN 1753-7177, Vol. 5, nr 1Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Project distress predictions are essential in project management. Developing appropriate methods to classify projects and building prediction models for multi-criteria decisions requires empirical methods to minimise misclassification errors. This paper carries out multi-criteria analysis to classify projects risks using a preference disaggregation method, utilités additives discriminantes – UTADIS. The UTADIS requires predefined classification which is implemented using critical path analysis. The methods are applied on three projects and result in no misclassification error and an effective prediction model.

sted, utgiver, år, opplag, sider
2013. Vol. 5, nr 1
Emneord [en]
project risk, uncertainty, multi-criteria, classifications, utilités additives discriminantes, UTADIS, critical paths, decisions
HSV kategori
Identifikatorer
URN: urn:nbn:se:du-28645DOI: 10.1504/IJDSRM.2013.057536OAI: oai:DiVA.org:du-28645DiVA, id: diva2:1252270
Tilgjengelig fra: 2018-10-01 Laget: 2018-10-01 Sist oppdatert: 2018-10-01bibliografisk kontrollert
Inngår i avhandling
1. A data-driven decision support system for coherency of experts’ judgment in complex classification problems: The case of food security as a UN sustainable development goal
Åpne denne publikasjonen i ny fane eller vindu >>A data-driven decision support system for coherency of experts’ judgment in complex classification problems: The case of food security as a UN sustainable development goal
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Everyday humans need to make individual or collective decisions. Often the decisions aim at achieving multiple goals (thus involving multiple criteria) and rely on the decision maker(s)’ intuition, internal data, as well as external sources of data. Faced with a complex decision problem of this kind, it is a great challenge to decisionmakers to be logically coherent over time with regard to their preferences. To aid in achieving coherency, operation researchers and decision analysts have developed formal methods to support decision makers. One such method is the UTADIS method that serves as the workhorse for this thesis. I received the request from UN officials who had to manage the sustainable development goals while addressing the issue of food security. They wished for a decision support system (DSS) that could aid in their classification of countries to mitigate the risk of failing on food security. The virtue of the DSS should be that their expert judgment was complemented by formal methods for better risk classification. The UTADIS method was fitting for the purpose, but it lacked implementability. In particular, it required an iterative approach engaging the experts multiple times, while not readily lending itself to making use of external data, making it inefficient as a DSS. The fundamental contribution of this thesis is that I have solved these shortcomings of the UTADIS method, such that it now readily can be used in a functionally efficient way for the desired purpose of the UN. In solving these problems, it is also more broadly implementable as a DSS, as I have validated the artifact to a DSS, by use of several demonstrations and exposed it to sensitivity analysis.

sted, utgiver, år, opplag, sider
Borlänge: Dalarna University, 2018
Serie
Dalarna Doctoral Dissertations ; 8
Emneord
Coherence, Efficiency, Decision Support System, Multi-Criteria, Risk, Classification Model, Decision Makers, Judgment, Alternatives, Prediction, Data, Integrate, Imprecision, Food Security, UTADIS
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-28649 (URN)978-91-85941-79-7 (ISBN)
Disputas
2018-11-28, B311, Borlänge, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-10-30 Laget: 2018-10-01 Sist oppdatert: 2020-01-10bibliografisk kontrollert

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