Atefeh Amindoust

Using Data Envelopment Analysis for Green Supplier Selection in Manufacturing under Vague Environment

In these days, considering the growth of knowledge about environmental protection and green issues in manufacturing, green supplier selection would be the central component in the management of supply chain. This paper intends to apply data envelopment analysis for supplier selection considering environmental merits. The suppliers’ performances with respect to criteria are not pure numbers and considered in linguistic terms according to decision makers’ opinion. To handle the subjectivity of decision makers’ assessments, fuzzy logic has been applied. A case study is done to present the application of the method.

Nonlinear genetic-based model for supplier selection: a comparative study

AbstractEvaluation 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 efficien...

Supplier Evaluation Using Fuzzy Inference Systems

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.

Supplier Evaluation and Selection Using a FDEA Model

Due to the growth of global outsourcing, supplier evaluation and selection is one of the strategic decisions for purchasing management in the supply chain. In this chapter, we address the important attributes through the literature that constituent suppliers should possess in order to achieve the successful supply chain. These attributes (criteria) are obtained using an Affinity Diagram. Then, a committee of decision makers is formed to provide linguistic ratings to the candidate suppliers for the selected criteria. The linguistic ratings are then transformed into fuzzy numbers and fed into a fuzzy DEA (FDEA) model based on the α- cut approach assessment of candidate suppliers. A hypothetical application is provided to demonstrate the applicability and feasibility of the method.

Development of Fuzzy Applications for High Performance Induction Motor Drive

This chapter develops a sliding mode and fuzzy logic-based speed controller, which is named adaptive fuzzy sliding-mode controller (AFSMC) for an indirect fieldoriented control (IFOC) of an induction motor (IM) drive. Essentially, the boundary layer approach is the most popular method to reduce the chattering phenomena, which leads to trade-off between control performances, and chattering elimination for uncertain nonlinear systems. For the proposed AFSMC, a fuzzy system is assigned as the reaching control part of the fuzzy sliding-mode controller so that it improves the control performances and eliminates the chattering completely despite large and small uncertainties in the system. A nonlinear adaptive law is also implemented to adjust the control gain with uncertainties of the system. The adaptive law is developed in the sense of Lyapunov stability theorem to minimize the control effort. The applied adaptive fuzzy controller acts like a saturation function in the thin boundary layer near the sliding surface to guarantee the stability of the system. The proposed AFSMCbased IM drive is implemented in real-time using digital signal processor (DSP) board TI TMS320F28335. The experimental and simulation results show the effectiveness of the proposed AFSMC-based IM drive at different operating conditions such as load disturbance, parameter variations, etc.

Learning Improvement of DEA Technique in Decision Making for Manufacturing Applications Using DEA Excel-Solver

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.

Textile supplier selection in sustainable supply chain using a modular fuzzy inference system model

Abstract Today’s fashion clothing market is highly competitive and the textile and clothing industry is a significant area of the world’s economy. In addition, the sustainability issues have received a lot of attention in the textile supply chains. Since supplier evaluation and selection is a crucial decision in supply chain management, this paper proposes a framework for textile suppliers’ sustainability evaluation criteria and a new model based on this framework onto ranking a given list of suppliers. The relative importance of criteria and the suppliers’ performance with respect to criteria are considered based on decision-makers’ preferences in the model. To cope with the subjectivity of decision-makers’ opinions, fuzzy set theory has been applied and a modular model on the basis of Fuzzy Inference System is proposed. A real-life supplier selection problem for the textile industry is utilized to show the feasibility of the proposed model. Validation of the proposed model is studied through an existing literature model. The results show the effectiveness of the proposed model.