A methodology for large-scale R&amp;D planning based on cluster analysis

Mathieu, Richard G.; Gibson, J. E.

doi:10.1109/17.233190

Cited by 50 publications

(17 citation statements)

References 14 publications

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“…That is, algorithms should not be used to prescribe solutions without allowing for the judgment, experience, and insight of the decision makers (Mathieu and Gibson, 1993). Project portfolio selection should be considered as a process that includes several steps, rather than just solving an optimization problem.…”

Section: Summary and Discussionmentioning

confidence: 99%

A zero-one model for project portfolio selection and scheduling

Ghasemzadeh

Archer

Iyogun

1999

Journal of the Operational Research Society

129

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A zero-one integer linear programming model is proposed for selecting and scheduling an optimal project portfolio, based on the organization's objectives, and constraints such as resource limitations and interdependence among projects. The major contribution of the paper is that the proposed model not only suggests projects that should be incorporated in the optimal portfolio, but it also determines the starting period for each project. Scheduling considerations can have a major impact on the combination of projects that can be incorporated in the portfolio, and may all ow the addition of certain projects to the portfolio that could not have been selected otherwise. Moreover, the model handles situations that frequently happen in real world applications, in which the amount of available and consumed resources varies in different periods. An example problem is described and solved with the proposed model to ill ustrate the approach, and some areas for future research are discussed .

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Section: Summary and Discussionmentioning

confidence: 99%

A zero-one model for project portfolio selection and scheduling

Ghasemzadeh

Archer

Iyogun

1999

Journal of the Operational Research Society

129

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“…Kim et al [8] postulated the fuzzy centroid method which is an extension of fuzzy k-modes, where the hardtype centroid used in the fuzzy K-modes algorithm is modified. Mathieu et al [9] identified the programs to participate in and determined the resource allocation by using cluster analysis. High scale research and development planning were a part of the decision enhancement module.…”

Section: Introductionmentioning

confidence: 99%

MMeMeR: An Algorithm for Clustering Heterogeneous Data using Rough Set Theory

Tripathy¹,

Goyal²,

Chowdhury³

et al. 2017

IJISA

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Abstract-In recent times enumerable number of clustering algorithms have been developed whose main function is to make sets of objects having almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and so have stability issues. Thus handling categorical data along with uncertainty has been made necessary owing to such difficulties. So, in 2007 MMR algorithm was developed which was based on basic rough set theory. MMeR was proposed in 2009 which surpassed the results of MMR in taking care of categorical data and it could also handle heterogeneous values as well. SDR and SSDR were postulated in 2011 which were able to handle hybrid data. These two showed more accuracy when compared to MMR and MMeR. In this paper, we further make improvements and conceptualize an algorithm, which we call MMeMeR or Min-Mean-Mean-Roughness. It takes care of uncertainty and also handles heterogeneous data. Standard data sets have been used to gauge its effectiveness over the other methods.

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“…It can be defined as a process of partitioning a given data set of multiple attributes into groups. Clustering has been used in many areas such as gene data processing [3], transactional data processing [4], decision support [5], mobile ad-hoc networks (MANETs) [6], study anxiety [7] and radar signals processing [8]. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes [9,10].…”

Section: Introductionmentioning

confidence: 99%