The properties of a stochastic optimal sequential strategy for purchasing information in a project selection problem

1996 ◽  
Vol 43 (1) ◽  
pp. 121-136
Author(s):  
Abraham Mehrez ◽  
Lea Friedman
Keyword(s):  
Project Selection ◽  
Selection Problem ◽  
Sequential Strategy

scholarly journals New Closeness Coefficients for Fuzzy Similarity Based Fuzzy TOPSIS: An Approach Combining Fuzzy Entropy and Multidistance

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Mikael Collan ◽  
Mario Fedrizzi ◽  
Pasi Luukka
Keyword(s):  
Research And Development ◽  
Fuzzy Topsis ◽  
Project Selection ◽  
Development Project ◽  
Fuzzy Entropy ◽  
Selection Problem ◽  
Aggregation Process ◽  
Owa Operator ◽  
Fuzzy Similarity

This paper introduces new closeness coefficients for fuzzy similarity based TOPSIS. The new closeness coefficients are based on multidistance or fuzzy entropy, are able to take into consideration the level of similarity between analysed criteria, and can be used to account for the consistency or homogeneity of, for example, performance measuring criteria. The commonly known OWA operator is used in the aggregation process over the fuzzy similarity values. A range of orness values is considered in creating a fuzzy overall ranking for each object, after which the fuzzy rankings are ordered to find a final linear ranking. The presented method is numerically applied to a research and development project selection problem and the effect of using two new closeness coefficients based on multidistance and fuzzy entropy is numerically illustrated.

The R & D project selection problem with fuzzy coefficients

1988 ◽  
Vol 26 (3) ◽  
pp. 299-316 ◽  
Author(s):  
Oscar Pereira Dias
Keyword(s):  
Project Selection ◽  
Selection Problem

A pinch analysis approach to project selection problem

2017 ◽  
Author(s):  
Pritam S. Roychaudhuri ◽  
Santanu Bandyopadhyay ◽  
Dominic C. Y. Foo ◽  
Raymond R. Tan ◽  
Vasiliki Kazantzi
Keyword(s):  
Project Selection ◽  
Selection Problem ◽  
Analysis Approach ◽  
Pinch Analysis

scholarly journals SOLVING CONSTRUCTION PROJECT SELECTION PROBLEM BY A NEW UNCERTAIN WEIGHTING AND RANKING BASED ON COMPROMISE SOLUTION WITH LINEAR ASSIGNMENT APPROACH

2019 ◽  
Vol 25 (3) ◽  
pp. 241-251 ◽  
Author(s):  
Reza Davoudabadi ◽  
Seyed Meysam Mousavi ◽  
Jonas Šaparauskas ◽  
Hossein Gitinavard
Keyword(s):  
Fuzzy Sets ◽  
Alternative Assessment ◽  
Construction Project ◽  
Project Selection ◽  
Selection Problem ◽  
Uncertain Information ◽  
Compromise Solution ◽  
Additional Information ◽  
Criteria Weights ◽  
Linear Assignment

Selecting a suitable construction project is a significant issue for contractors to decrease their costs. In real cases, the imprecise and uncertain information lead to decisions made based on vagueness. Fuzzy sets theory could help decision makers (DMs) to address incomplete information. However, this article develops a new integrated multi-criteria group decision-making model based on compromise solution and linear assignment approaches with interval-valued intuitionistic fuzzy sets (IVIFSs). IVIFSs by presenting a membership and non-membership degree for each candidate based on appraisement criteria could decrease the vagueness of selection decisions. The proposed algorithm involves a new decision process under uncertain conditions to determine the importance of criteria and DMs, separately. In this regard, no subjective or additional information is needed for this process; only the input information required is an alternative assessment matric. In this approach, weights of criteria and DMs are specified based on novel indexes to increase the reliability of obtained results. In this respect, the criteria’ weights are computed regarding entropy concepts. The basis for calculating the weight of each DM is the distance between each DM and an average of the DMs’ community. Furthermore, the linear assignment model is extended to rank the candidates. A case study about the construction project selection problem (CPSP) is illustrated to indicate the application of proposed model.

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