Despite the importance of decision-making (DM) techniques for construction of effective decision models for supplier selection, there is a lack of a systematic literature review for it. This paper provides a systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection. By using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches, we finally selected and reviewed 123 journal articles. To examine the research trend on uncertain supplier selection, these articles are roughly classified into seven categories according to different uncertainties. Under such classification framework, 26 DM techniques are identified from three perspectives: (1) Multicriteria decision making (MCDM) techniques, (2) Mathematical programming (MP) techniques, and (3) Artificial intelligence (AI) techniques. We reviewed each of the 26 techniques and analyzed the means of integrating these techniques for supplier selection. Our survey provides the recommendation for future research and facilitates knowledge accumulation and creation concerning the application of DM techniques in supplier selection.

Application of decision-making techniques in supplier selection: A systematic review of literature

Highlights? This study proposes a hybrid fuzzy multi criteria decision making model that can assist in evaluating green suppliers. ? The proposed model integrates DEMATEL, ANP and TOPSIS methods in a fuzzy context. ? Ford Otosan is selected as a case company in this study for the evaluation of green supplier alternatives. ? The supplied case study provides additional insights for research and practical applications. It is well known that "green" principles and strategies have become vital for companies as the public awareness increased against their environmental impacts. A company's environmental performance is not only related to the company's inner environmental efforts, but also it is affected by the suppliers' environmental performance and image. For industries, environmentally responsible manufacturing, return flows, and related processes require green supply chain (GSC) and accompanying suppliers with environmental/green competencies. During recent years, how to determine suitable and green suppliers in the supply chain has become a key strategic consideration. Therefore this paper examines GSC management (GSCM) and GSCM capability dimensions to propose an evaluation framework for green suppliers. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The identified components are integrated into a novel hybrid fuzzy multiple criteria decision making (MCDM) model combines the fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), the Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) in a fuzzy context. A case study is proposed for green supplier evaluation in a specific company, namely Ford Otosan.

A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers

In these days, considering the growth of knowledge about sustainability in enterprise, the sustainable supplier selection would be the central component in the management of a sustainable supply chain. In this paper the sustainable supplier selection criteria and sub-criteria are determined and based on those criteria and sub-criteria a methodology is proposed onto evaluation and ranking of a given set of suppliers. In the evaluation process, decision makers' opinions on the importance of deciding the criteria and sub-criteria, in addition to their preference of the suppliers' performance with respect to sub-criteria are considered in linguistic terms. To handle the subjectivity of decision makers' assessments, fuzzy logic has been applied and a new ranking method on the basis of fuzzy inference system (FIS) is proposed for supplier selection problem. Finally, an illustrative example is utilized to show the feasibility of the proposed method.

/pdf/sustainable-supplier-selection-a-ranking-model-based-on-1eyl039fne.pdf

Sustainable supplier selection: A ranking model based on fuzzy inference system

A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method

Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method

Supplier selection attained the state of paramount importance for companies in the current scenario because of increasing global competition. Improper selection of suppliers will have an adverse impact on the overall performance of the company. The number of available alternatives in the current market is on a rise, and hence it becomes difficult to select a supplier from among a large lot. An attempt has been made to develop a new composite model using structural equation modeling and fuzzy analytic hierarchy process technique, based on the results of a survey of 151 respondents. Based on the criteria that influence the selection of suppliers, the model has been developed. The study also examines the supplier selection criteria and measures the supplier selection score using the developed model.

http://www.isihome.ir/freearticle/ISIHome.ir-25001.pdf

A strategic model using structural equation modeling and fuzzy logic in supplier selection

Sustainable transportation systems performance evaluation using fuzzy logic

Many factors in the current scenario have influenced manufacturing organisations to have a competitive edge by concentrating on entire supply chains. Sourcing decisions are one of the strategic decisions because they enable companies to reduce costs and improve profit figures. The main task in sourcing is vendor selection. Recent challenges such as shortened product life cycle, just-in-time environment, and the importance of strategic partnerships in upstream chains always influence companies to prioritise vendor selection. In addition, outsourced parts and components account for a significant contribution in the cost of finished goods. Thus evaluating and selecting the right vendor is the key to business. Vendors are selected merely on the basis of cost factors in the traditional approach. However, companies eventually have understood that their approach which emphasises costs as the sole criterion is inefficient and needs to be changed. To deal with the complex process of vendor evaluation, multiple cri...

Vendor selection problem: a multi-criteria approach based on strategic decisions

A Multi Criteria Decision Making Approach for Suppliers Selection

This paper deals with generating of an optimized route for multiple Traveling Sales- man Problems. We used a methodology of clustering the given cities depending upon the num- ber of salesmen and each cluster is allotted to a salesman. "�� -Means clustering" algorithm has been used for easy clustering of the cities. In this way the �� TSP has been converted into TSP which is simple in computation compared to mTSP. After clustering, an optimized route is generated for each salesman in his allotted cluster. To achieve this, we first generated a parent route using "Shrink Wrap" algorithm and this parent string is further optimized by using two other optimizing algorithms. For this purpose, "Tabu Search" and "Simulated Annealing" were extensively used. From the results, we observed that Simulated Annealing generate optimized route covering less distance than Tabu search.

/pdf/optimization-of-non-linear-multiple-traveling-salesman-1sq6vb8zvd.pdf

P. Parthiban

Papers

A strategic model using structural equation modeling and fuzzy logic in supplier selection

Sustainable transportation systems performance evaluation using fuzzy logic

Vendor selection problem: a multi-criteria approach based on strategic decisions

A Multi Criteria Decision Making Approach for Suppliers Selection

Optimization of Non-Linear Multiple Traveling Salesman Problem Using K-Means Clustering, Shrink Wrap Algorithm and Meta-Heuristics