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Atefeh Amindoust   Dr.  Senior Scientist or Principal Investigator 
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Atefeh Amindoust published an article in January 2017.
Top co-authors See all
Shamsuddin Ahmed

1898 shared publications

M. Nasir Uddin

173 shared publications

Keith Case

99 shared publications

Loughborough University

Farzad Tahriri

21 shared publications

University of Malaya

Morteza Yazdani

20 shared publications

Publication Record
Distribution of Articles published per year 
(2012 - 2017)
Total number of journals
published in
Publications See all
Article 1 Read 2 Citations NONLINEAR GENETIC-BASED MODEL FOR SUPPLIER SELECTION: A COMPARATIVE STUDY Alireza Fallahpour, Atefeh Amindoust, Jurgita Antuchevičienė... Published: 22 January 2017
Technological and Economic Development of Economy, doi: 10.3846/20294913.2016.1189461
DOI See at publisher website
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Evaluation and selection of candidate suppliers has become a major decision in business activities around the world. In this paper, a new hybrid approach based on integration of Gene Expression Programming (GEP) with Data Envelopment Analysis (DEA) (DEA-GEP) is presented to overcome the supplier selection problem. First, suppliers’ efficiencies are obtained through applying DEA. Then, the suppliers’ related data are utilized to train GEP to find the best trained DEA-GEP algorithm for predicting efficiency score of Decision Making Units (DMUs). The aforementioned data is also used to train Artificial Neural Network (ANN) to predict efficiency scores of DMUs. The proposed hybrid DEA-GEP is compared to integrated approach of Artificial Neural Network with Data Envelopment Analysis (DEA-ANN) to support the validity of the proposed model. The obtained results clearly show that the model based on GEP not only is more accurate than the DEA-ANN model, but also presents a mathematical function for efficiency score based on input and output data set. Finally, a real-life supplier selection problem is presented to show the applicability of the proposed hybrid DEA-GEP model.
Article 1 Read 0 Citations Textile supplier selection in sustainable supply chain using a modular fuzzy inference system model Ali Saghafinia, Atefeh Amindoust Published: 03 October 2016
The Journal of The Textile Institute, doi: 10.1080/00405000.2016.1238130
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BOOK-CHAPTER 1 Read 0 Citations Development of Fuzzy Applications for High Performance Induction Motor Drive Ali Saghafinia, Atefeh Amindoust Published: 18 November 2015
Induction Motors - Applications, Control and Fault Diagnostics, doi: 10.5772/61071
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Article 2 Reads 6 Citations Empirical evidence of AMT practices and sustainable environmental initiatives in malaysian automotive SMEs Siti Zawiah Md Dawal, Farzad Tahriri, Yap Hwa Jen, Keith Cas... Published: 01 June 2015
International Journal of Precision Engineering and Manufacturing, doi: 10.1007/s12541-015-0154-6
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Article 1 Read 0 Citations Learning Improvement of DEA Technique in Decision Making for Manufacturing Applications Using DEA Excel-Solver Atefeh Amindoust, Ahmed Shamsuddin, Ali Saghafinia Published: 01 February 2014
Advanced Materials Research, doi: 10.4028/
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DEA (Data Envelopment Analysis) is the optimization method of mathematical programming to measure the relative efficiencies of decision making units (DMUs). Due to its wide applicability, the DEA has been studied extensively for the last 30 years to solve decision making problems. Since, there are a lot of selection decisions in manufacturing, DEA as an appropriate tool to be necessary-especially for engineers-to improve learning for decision making. In this paper, the DEA method is applied in decision making process through DEA Excel-Solver software and the required processes are explained step by step to help academics and practitioners to get appropriate results in making decision.
BOOK-CHAPTER 0 Reads 2 Citations Supplier Evaluation Using Fuzzy Inference Systems Atefeh Amindoust, Ali Saghafinia Published: 01 January 2014
Studies in Fuzziness and Soft Computing, doi: 10.1007/978-3-642-53939-8_1
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Supplier selection is an important area of decision making in manufacturing and service industries, mainly for large and medium companies—either multinational (MNCs) or local. As sustainability in terms of economic, environmental, and social aspects has gained world-wide focus in supply chain management, this dimension deserves due attention in supplier selection decision. In real life applications, the importance of supplier selection criteria is different and depends on the circumstances and situations and each organization may consider its individual relative importance of the criteria. The relative importance of the criteria and also the suppliers’ performance with respect to these criteria would be verified with the relevant decision makers. So, the supplier selection decision involves a high degree of vagueness and ambiguity in practice. This chapter takes the aforesaid issues into account and proposes a modular FIS method for supplier selection problem. To handle the subjectivity of decision makers’ preferences, fuzzy set theory is applied. The applicability and feasibility of the proposed method are tested through a real-life supplier selection problem.