Alireza, Fallahpour (2016) Development of multi criteria decision making model for supplier selection using gene expression programming / Alireza Fallahpour. PhD thesis, University of Malaya.
Abstract
Sustainable Supply Chain Management (SSCM) is a developing concept recently applied by organizations, due to the growth in awareness about sustainability in firms. The literature reports that a significant way to implement responsible SSCM is to ensure that the supplier of goods successfully incorporates sustainable attributes. However, it is seen that the previous studies in this field did not adequately discern the sustainability criteria and sub-criteria and put the sustainable issues in a form of generic model. Generally, in supplier selection process, two issues are very important: 1) selecting correct evaluative criteria which are important and applicable in the real world; 2) using accurate model for performance evaluation and ranking.This study takes the aforementioned issues into account, develops a comprehensive list of criteria and their corresponding sub-criteria and also, a new intelligent approach known as Gene Expression Programming (GEP) is used to overcome the shortcoming of the previous proposed intelligent models in the field of supplier selection. A comprehensive list of criteria and sub-criteria was developed. Investigation of the developed criteria and sub-criteria in terms of their importance and applicability was carried out through a questionnaire survey, using experts’ opinions from the different industry and the academia. To show the validity of the collected data set by the questionnaire, Cronbach’s alpha and Mann-Whitney U-test were carried out. Following this, GEP was performed to overcome any drawback developed by previously proposed models (called black box). To verify the validity of the GEP model, different statistical methods were applied. In addition, the derived results were compared with both previous intelligent model such as Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) to show the accuracy of the proposed model in performance evaluation. Furthermore, to demonstrate GEP’s great capability in ranking, the ranking result of the model was compared to the result obtained by one of the most common methods in ranking, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).
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