A Model for Project Selecting with Limited Resources in Data Envelopment Analysis with Input and Output Fuzzy
Subject Areas : Statistics
1 - Department of Mathematics, Zahedan Branch, Islamic Azad University, Zahedan, Iran
Keywords: تحلیل پوششی دادهها, انتخاب پروژه, کارایی, برنامهریزی دودویی,
Abstract :
In Evaluating Performance, Selecting a Subset from a Set of Solutions with Limited Resources is Essential. If There Is More Than One Input and Output, the Data Rnvelopment Analysis Optimization Models Are Evaluated and Performance Measurement Based on the Weighted Output Is Divided Weighted Input. In This Research, Two Models of Optimization with Limited Resources Present from Data Envelopment Analysis Models. Each Project Produces a Set of Outputs Using Different Input Sources. In This Method, a Subset of Projects Is Selected that Can be Applies to the Resource Constraints as a Composite Project. These Composite Projects Are Defined and Evaluated by Available Projects and with Production Technology in Data Envelopment Analysis. In Fact, Evaluation and Selection Are Combined in the New Model, which is Done by Inserting a Data Envelopment Analysis Model into a Binary-Hybrid Linear Programming. The Second Model, Involves Choosing a Set of the Best or Most Preferred Places for New Facilities. Again, the Second Proposed Model Also Relates to Choices with Limited Resources.
[1] Amiri P. M., (2010), “Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods”, Expert Systems with Applications, 37, 6218–6224.
[2] Baker N. and Freeland J., (1975), “Recent Advances in R&D Benefit Measurement and Project Selection Methods”, Management Science, 21, 10, 1164-1175.
[3] Jafarzadeh H., Akbari P. and Abedin B., (2018), “A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency combination of fuzzy QFD and DEA”, Expert Systems With Applications, 110, 237–249.
[4] Liu F., Chen Y., Yang J., Xu D. and Liu W., (2019), “Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule”, International Journal of Project Management, 37, 87–97.
[5] Mehrez A. and Sinuany-Stern Z., (1983), “An Interactive Approach for Project Selection”, Operational Research Society, 34, 7, 621-626.
[6] Salehi K., (2015), “A hybrid fuzzy MCDM approach for project selection problem”, Decision Science Letters, 4, 109–116.
[7] Wang F., Chen-Hsoung H. and Tzeng G., (2014), “Applying a Hybrid MCDM Model for Six Sigma Project Selection”, Mathematical Problems in Engineering, 2014, 1-13.
[8] Yamami A. E., Mansouri K., Qbadou M. and Illousamen E. H., (2018), “Multi-objective IT Project Selection Model for Improving SME Strategy Deployment”, International Journal of Electrical and Computer Engineering (IJECE), 8, 2, 1102-1111.
