5 research outputs found
Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule
In this paper, a likelihood based evidence acquisition approach is proposed
to acquire evidence from experts'assessments as recorded in historical
datasets. Then a data-driven evidential reasoning rule based model is
introduced to R&D project selection process by combining multiple pieces of
evidence with different weights and reliabilities. As a result, the total
belief degrees and the overall performance can be generated for ranking and
selecting projects. Finally, a case study on the R&D project selection for the
National Science Foundation of China is conducted to show the effectiveness of
the proposed model. The data-driven evidential reasoning rule based model for
project evaluation and selection (1) utilizes experimental data to represent
experts' assessments by using belief distributions over the set of final
funding outcomes, and through this historic statistics it helps experts and
applicants to understand the funding probability to a given assessment grade,
(2) implies the mapping relationships between the evaluation grades and the
final funding outcomes by using historical data, and (3) provides a way to make
fair decisions by taking experts' reliabilities into account. In the
data-driven evidential reasoning rule based model, experts play different roles
in accordance with their reliabilities which are determined by their previous
review track records, and the selection process is made interpretable and
fairer. The newly proposed model reduces the time-consuming panel review work
for both managers and experts, and significantly improves the efficiency and
quality of project selection process. Although the model is demonstrated for
project selection in the NSFC, it can be generalized to other funding agencies
or industries.Comment: 20 pages, forthcoming in International Journal of Project Management
(2019
A hybrid machine learning and text-mining approach for the automated generation of early warnings in construction project management.
The thesis develops an early warning prediction methodology for project failure prediction by analysing unstructured project documentation. Project management documents contain certain subtle aspects that directly affect or contribute to various Key Performance Indicators (KPIs). Extracting actionable outcomes as early warnings (EWs) from management documents (e.g. minutes and project reports) to prevent or minimise discontinuities such as delays, shortages or amendments is a challenging process. These EWs, if modelled properly, may inform the project planners and managers in advance of any impending risks. At presents, there are no suitable machine learning techniques to benchmark the identification of such EWs in construction management documents.
Extraction of semantically crucial information is a challenging task which is reflected substantially as teams communicate via various project management documents. Realisation of various hidden signals from these documents in without a human interpreter is a challenging task due to the highly ambiguous nature of language used and can in turn be used to provide decision support to optimise a project’s goals by pre-emptively warning teams. Following up on the research gap, this work develops a “weak signal” classification methodology from management documents via a two-tier machine learning model.
The first-tier model exploits the capability of a probabilistic Naïve Bayes classifier to extract early warnings from construction management text data. In the first step, a database corpus is prepared via a qualitative analysis of expertly-fed questionnaire responses that indicate relationships between various words and their mappings to EW classes. The second-tier model uses a Hybrid Naïve Bayes classifier which evaluates real-world construction management documents to identify the probabilistic relationship of various words used against certain EW classes and compare them with the KPIs. The work also reports on a supervised K-Nearest-Neighbour (KNN) TF-IDF methodology to cluster and model various “weak signals” based on their impact on the KPIs.
The Hybrid Naïve Bayes classifier was trained on a set of documents labelled based on expertly-guided and indicated keyword categories. The overall accuracy obtained via a 5-fold cross-validation test was 68.5% which improved to 71.5% for a class-reduced (6-class) KNN-analysis. The Weak Signal analysis of the same dataset generated an overall accuracy of 64%. The results were further analysed with Jack-Knife resembling and showed consistent accuracies of 65.15%, 71.42% and 64.1% respectively.PhD in Manufacturin