The main goal of this paper is to develop a framework to support the supplier selection process in an Iranian automotive industry. Although numerous criteria are being used for the selection of suitable supplier, selection of the critical factors in conformance to the specification of the automotive industries is less investigated. In order to fill this gap, this research was carried out to systematically propose a framework comprising of the most critical factors for the aim of supplier selection. A literature survey was conducted and measures for assessing the suppliers were extracted. Nominated Group Technique (NGT) was deployed to extract the most critical performance measures from the initial list. Seven measures were found to be proper for the supplier selection process. A Fuzzy Analytical Network Process (FANP) was then proposed to weight the extracted measures and determine their importance level. The model was then implemented to assist an automotive company for the aim of its supplier selection

This study proposed an integrated Balanced Scorecard–Fuzzy Analytic Hierarchical Process (BSC–FAHP) model to select suppliers in the automotive industry. In spite of the vast amount of studies on supplier selection, the evaluation and selection of suppliers using the specific measures of the automotive industry are less investigated. In order to fill this gap, this research proposed a new BSC for supplier selection of automobile industry. Measures were gathered using a literature survey and accredited using Nominal Group Technique (NGT). Finally, a fuzzy AHP was used to select the best supplier

This study developed specific criteria and a fuzzy analytic network process (FANP) to assess and select portfolios on the Tehran Stock Exchange (TSE). Although the portfolio selection problem has been widely investigated, most studies have focused on income and risk as the main decision-making criteria. However, there are many other important criteria that have been neglected. To fill this gap, first, a literature review was conducted to determine the main criteria for portfolio selection, and a Likert-type questionnaire was then used to finalize a list of criteria. Second, the finalized criteria were applied in an FANP to rank 10 different TSE portfolios. The results indicated that profitability, growth, market, and risk are the most important criteria for portfolio selection. Additionally, portfolios 6, 7, 2, 4, 8, 1, 5, 3, 9, and 10 (A6, A7, A2, A4, A8, A1, A5, A3, A9, and A10) were found to be the best choices. Implications and directions for future research are discussed.

The main objective of this paper is to develop an integrated multiobjective and multiperiod mathematical model for sustainable supplier selection and order allocation of an automotive manufacturer. In spite of the vast quantity of research on this area, integrated consideration of risk and inflation in sustainable supplier selection and order allocation problem is less investigated. To fill this gap, this paper aims to develop a multiobjective sustainable supplier selection and order allocation model considering risk and inflation. Particularly, six objective functions of total cost, economic score, environmental score, social score, inflation rate, and risk level along with related constraints are optimized in the model. The developed model is solved using weighted sum approach and augmented $\varepsilon $ -constraint method. In addition, a sensitivity analysis has been conducted. The results provide the best sustainable suppliers and the optimum quantity of products to be purchase from each supplier in each period.

In spite of the vast amount of studies on green supplier selection and related methods and approaches, the evaluation of green supply chain performance indicators aligned with classic measures is less investigated. Therefore, this research attempted to provide an integrated step by step procedure to consider both classical and green key performance indicators within the supplier selection framework. A literature survey was conducted and measures for assessing the green suppliers were extracted. Nominal Group Technique (NGT) is deployed to extract the most critical performance measures. Ten performance measures were selected as a substitute for green supplier selection. A Fuzzy Analytical Network Process (FANP) was then deployed to weight the extracted measures and determine their importance level

Purpose: The purpose of this paper is to develop a fuzzy analytic network process (FANP) model to select the maintenance policy of an acid manufacturing company. Design/methodology/approach: Four maintenance strategies of Corrective Maintenance (CM), Time-Based Maintenance (TBM), Condition-Based Maintenance (CBM) and Shutdown Maintenance (SM) are investigated to be considered for seven equipment of the case study. These equipment are almost new and include boiler, molten sulfur ponds, cooling towers, absorption tower, converter, sulfur fuel furnace and heat exchanger. Chang’s extended analysis has been employed to deal with fuzzy data and analyze the fuzzy decision matrices. The proposed approach is applied to a sulfuric acid production plant and the suitable maintenance policy is found for all seven equipment of the company. Findings: Based on the obtained results, the CBM policy is appropriate for high-risk (cooling tower) and high added value equipment (absorption tower). In addition, TBM is selected for boiler and converters while SM is selected for molten sulfur ponds. Finally, high-cost, low-risk and low added value equipment (sulfur fuel furnace and heat exchanger) are more appropriate with CM policy. Originality/value: This research presents a novel idea to consider cost, risk and added value in the context of maintenance policy selection. From the methodological and theoretical features, this research offers new insights in this area since, to the best of the authors’ knowledge, no comparable study has been conducted before.