A Grey Theory Based Multiple Attribute Approach for R&amp;D Project Portfolio Selection

Bhattacharyya, Rupak

doi:10.1016/j.fiae.2015.05.006

Cited by 31 publications

(28 citation statements)

References 19 publications

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“…Eksik bilgilerden kaynaklanan belirsizlik durumlarında ilişkilerin analizinde gri sistem teorisi başarılı olmaktadır. Gri sistem teorisine göre kusursuz ve tam bilginin olduğu sistemler beyaz sistem, tam tersi durumdaki sistemler ise siyah sistem olarak tanımlanmaktadır (Köse vd., 2013: 462 (Bhattacharyya, 2015).…”

Section: Gri Sistem Teorisiunclassified

Gri İlişkisel Temelli TOPSIS Yöntemi ile Depo Yeri Seçimi

Kabadayı

Esen

2021

Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi

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Depo yeri seçimi, birçok kriterin aynı anda değerlendirilerek işletmenin maksimum fayda sağlayacağı en uygun lokasyonun belirlendiği bir problemdir. Problemin bu yapısından dolayı, çok kriterli karar verme (ÇKKV) yöntemleri bu problemin çözümünde etkili sonuçlar vermektedir. Bu çalışmada bir 4. parti lojistik (4PL) firmasının yeni depo yeri seçimi kararı için bir model geliştirilmiştir. Geliştirilen modelde çözüm yöntemi olarak Gri Temelli TOPSIS yöntemi kullanılmıştır. Yapılan literatür çalışmasına göre bu yöntem daha önce depo yeri seçimi problemlerinin çözümünde kullanılmamıştır. Bu çalışmada depo yeri seçimi problemlerinin çözümünde kullanılmak üzere alternatif bir yöntemin önerilmesi ve geniş bir depo yer seçimi kriter setinin oluşturulması ile literatüre fayda sağlamak amaçlanmaktadır.

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Section: Gri Sistem Teorisiunclassified

Gri İlişkisel Temelli TOPSIS Yöntemi ile Depo Yeri Seçimi

Kabadayı

Esen

2021

Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi

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“…A project-oriented firm is presented with 20 independent candidate R&D projects with different investment capitals (2, 4, 5, 7; 0.9, 0.9)) ((1, 3, 6, 8; 1, 1), (2, 4, 5, 7; 0.9, 0.9)) ((1, 3, 6, 8; 1, 1), (2, 4, 5, 7; 0.9, 0.9)) C ( (16, 18, 21, 23; 1, 1), (17, 19, 20, 22; 0.9, 0.9)) ( (16, 18, 21, 23; 1, 1), (17, 19, 20, 22; 0.9, 0.9)) ( (16, 18, 21, 23; 1, 1), (17, 19, 20, 22; 0.9, 0.9)) D ( (18, 20, 23, 25; 1, 1), (19, 21, 22, 24; 0.9, 0.9)) ( (18, 20, 23, 25; 1, 1), (19, 21, 22, 24; 0.9, 0.9)) ( (18, 20, 23, 25; 1, 1), (19, 21, 22, 24; 0.9, 0.9)) 1 ((2, 5, 9, 12; 1, 1), (4, 6, 8, 10; 0.9, 0.9)) ((3, 6, 10, 12; 1, 1), (5,7,9,11; 0.9, 0.9)) ((2, 5, 9, 12; 1, 1), (4, 6, 8, 10; 0.9, 0.9)) ((11, 13, 16, 18; 1, 1), (12, 14, 15, 17; 0.9, 0.9)) 2 ((4, 7, 11, 13; 1, 1), (6, 8, 10, 12; 0.9, 0.9)) ((2, 5, 9, 12; 1, 1), (4, 6, 8, 10; 0.9, 0.9)) ((3, 6, 10, 12; 1, 1), (5,7,9,11; 0.9, 0.9)) ((14, 16, 19, 21; 1, 1), (15, 17, 18, 20; 0.9, 0.9)) 3 ((3, 6, 10, 12; 1, 1), (5,7,9,11; 0.9, 0.9)) ((6, 9, 13, 15; 1, 1), (8, 10, 12, 14; 0.9, 0.9)) ((4, 7, 11, 13; 1, 1), (6, 8, 10, 12; 0.9, 0.9)) ((18, 20, 23, 25; 1, 1), (19, 21, 22, 24; 0.9, 0.9)) 4 ((6, 9, 13, 15; 1, 1), (8, 10, 12, 14; 0.9, 0.9)) ((2, 5, 9, 12; 1, 1), (4, 6, 8, 10; 0.9, 0.9)) ((3, 6, 10, 12; 1, 1), (5,7,9,11; 0.9, 0.9)) ( …”

Section: Practical Examplementioning

confidence: 99%

“…Future events and opportunities have undeniable impact on R&D decisions. Therefore, most of the information used in portfolio decision-makings is at the best situation uncertain and at the worst condition very unreliable [7]. Mitigating risk and managing uncertainty is a practical subject in R&D studies.…”

Section: Introductionmentioning

confidence: 99%

R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach

Mohagheghi

Mousavi

Vahdani

et al. 2016

Neural Comput & Applic

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In today's ever-changing and highly uncertain environment, organizations depend on research and development (R&D) activities to adapt intensive growth of technology. One of the most important and trickiest tasks of any developing firms is to define new projects. This process gets difficult when it comes to choosing an appropriate portfolio from a set of candidate projects. Since organizations are faced with limited resources of R&D and budget constraints, they have to choose a project portfolio that mitigates the corresponding risk and enhances the overall value of portfolio. Therefore, the purpose of this study is to introduce a practical model to select the best and the most proper project portfolio while considering project investment capital, return rate, and risk. The ever-changing and highly uncertain environment of projects is addressed by utilizing interval type-2 fuzzy sets (IT2FSs). In this paper, a new model of R&D project evaluation is first introduced. This model includes a new risk-return index. This model is then extended in project portfolio selection, and as a result, a new model of R&D project portfolio selection is proposed under uncertainty. Constraints and limitations of R&D project portfolio selection are comprehensively addressed. In this model, lower semi-variance is applied to consider risk of proposed projects. Therefore, this paper offers a new model that applies IT2FSs to handle uncertainty, uses semi-variance to assess risk, synchronously considers risk and return in its selection process, and addresses the considerations and limits of realworld problems. Eventually, to verify the proposed model, a numerical example of the existing literature is solved with the model, and the results are compared. The first proposed model is used to prioritize proposed R&D projects of a gas and oil development holding firm as a real case study. To illustrate further, a practical example is also provided to demonstrate the applicability of the proposed project portfolio selection model.

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“…It is an old reference, but the problem and the numbers persist until today, at least for the pharmaceutical segment, which registered a 88.3% of clinical failure rate in 2007 [4]. To avoid project failure, companies have to find better ways of managing and selecting their projects portfolios, using scarce resources with the objective to maximize some utility measure or benefit or, in other cases, minimize the risk or costs of their projects [5].…”

Section: Introductionmentioning

confidence: 99%

Selecting Projects on the Brazilian R&D Energy Sector: A Fuzzy-Based Approach for Criteria Selection

2020

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Many companies around the world rely on R&D to better their chances for profit and permanence in a dynamic market. To keep up with ongoing changes, many ideas arise and some transform into projects. Since resources are limited, organizations seek to select only the most suitable projects to meet their objectives. This is an old practice. However, project portfolio characteristics have changed. The portfolio objectives of today go beyond profit: strategy, environment and society have also become important, along with many other decision criteria. Computational power has also been enhanced, making multi-data decision approaches more feasible, even for small-profit organizations. In the last half century, many authors have proposed multicriteria decision making (MCDM) methods for project portfolio selection (PPS) for Research and Development (R&D). Nevertheless, only a few paid due importance to the criteria used, which would be a central issue on any multicriteria decision. Thus, in order to contribute to the R&D PPS field of study, this work investigates the hypothesis that the criteria used in R&D PPS can be selected in an uncertain environment, according to their influence and importance. To do so, we propose a novel MCDM approach for criteria selection, that integrates Fuzzy-based DEMATEL (Decision Making Trial and Evaluation Laboratory) and Fuzzy-AHP (Analytic Hierarchy Process) Extend Analysis methods. We are also presenting a case study for a representative electrical-public Brazilian R&D organization, that from 2008 to 2018 managed R&D project portfolios valued at around US$ 1.2 billion. The results reflect current Brazilian concerns on the prioritized criteria, such as environmental, social and technical criteria, especially on the face of the disaster occurrences that took place on the media on the last years. In an overall manner, the results provide guidance on the topic and facilitate knowledge accumulation and creation concerning the criteria selection process in MCDM-based R&D PPS.

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A Grey Theory Based Multiple Attribute Approach for R&D Project Portfolio Selection

Cited by 31 publications

References 19 publications

Gri İlişkisel Temelli TOPSIS Yöntemi ile Depo Yeri Seçimi

Gri İlişkisel Temelli TOPSIS Yöntemi ile Depo Yeri Seçimi

R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach

Selecting Projects on the Brazilian R&D Energy Sector: A Fuzzy-Based Approach for Criteria Selection

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