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Project outcome classification with imprecise criteria information
Stockholm University.
2013 (English)In: International Journal of Applied Decision Sciences, ISSN 1755-8077, E-ISSN 1755-8085, Vol. 6, no 4Article in journal (Refereed) Published
Abstract [en]

A case in which managers have to make project outcome classification decisions with uncertainty in independently related criteria values is considered in this paper. A multi-criteria decision model is developed in this paper by selecting methods which delved into data analysis to help managers make informed classification decisions. Uncertainty in the criteria values is resolved using linear programming which enables managers to know the profit outcome of their projects for efficient resource allocation. The classification scheme from the linear programming process is used as predefined classification inputs for use in the UTilités Additives DIScriminantes (UTADIS) method, which further produces a classification model. The analysis presented a no misclassification error in the predefined classifications from the linear programming and the classifications in the UTADIS method thus further boosting the confidence managers can entrust in the resulting classification model.

Place, publisher, year, edition, pages
2013. Vol. 6, no 4
Keywords [en]
multi-criteria, classification, project outcomes, imprecision, linear programming.
National Category
Other Computer and Information Science Information Systems, Social aspects
Identifiers
URN: urn:nbn:se:du-28644DOI: 10.1504/IJADS.2013.056867OAI: oai:DiVA.org:du-28644DiVA, id: diva2:1252267
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-01Bibliographically approved
In thesis
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
Open this publication in new window or tab >>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 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2018
Series
Dalarna Doctoral Dissertations in Microdata Analysis ; 8
Keywords
Coherence, Efficiency, Decision Support System, Multi-Criteria, Risk, Classification Model, Decision Makers, Judgment, Alternatives, Prediction, Data, Integrate, Imprecision, Food Security, UTADIS
National Category
Other Computer and Information Science Economics and Business
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28649 (URN)978-91-85941-79-7 (ISBN)
Public defence
2018-11-28, B311, Borlänge, 13:00 (English)
Opponent
Supervisors
Available from: 2018-10-30 Created: 2018-10-01 Last updated: 2019-06-17Bibliographically approved

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Laryea, Rueben

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