In practice, the judgments of decision-makers are often uncertain and thus cannot be represented by accurate values. In this study, the opinions of decision-makers are collected based on grey linguistic variables and the data retains the grey nature throughout all the decision-making process. A grey best-worst method (GBWM) is developed for multiple experts multiple criteria decision-making problems that can employ grey linguistic variables as input data to cover uncertainty. An example is solved by the GBWM and then a sensitivity analysis is done to show the robustness of the method. Comparative analyses verify the validity and advantages of the GBWM.

The main purpose of this research is the allocation of orders to suppliers in an agile and flexible manner suitable to the automobile industry. In this paper, parts supplied by a single source were eliminated from the set of parts. In this paper, using mathematical modeling and through Interval-Valued Fuzzy-Rough Numbers Best Worst Method (IVFRNBWM), we try to achieve the results that can meet the proposed model’s needs and provide ideal results by introducing new modes. The present study addressed some new aspects of the subject and achieved robust results by considering five objective functions. These five functions are as follows: minimization of production line disruptions due to the performance of suppliers, minimization of the complaints of production line about supplied parts, minimization of defective parts received from suppliers (PPM), maximization of on-time delivery services and minimization of overall costs of supplied parts. Reviewing the literature, the originality of this study are as follows: 1. Identifying the structure of a supply chain (SC) in general and particularly in an automobile industry SC. 2. Investigating the modeling techniques of the existing SC models for coordinating all the members of a product SC. 3. Building a hybrid model of IVFRN-BWM and a robust goal programming agile and flexible supply chain in uncertain situation. 4. Identifying the suitable scenarios/cases for testing the proposed models to validate the models. This paper can help decision makers and managers to opt the best suppliers and also allocate right numbers of parts to those supplier(s) based on a real situation of each firm.

Project managers normally investigate how to reduce the total completion time of projects undertaken subject to the pre-determined objectives. The purpose of this paper is compression of total projects’ duration subject to influencing factors such as cost, time, quality and risk. In the present study, by considering factors affecting project success such as cost, time, quality and risk, project crashing and fast tracking are both employed then a fuzzy multi-objective non-linear model is proposed. Each project is associated with uncertainty and the lack of consideration of these uncertainties might lead to project failure. In addition, a fuzzy approach has been employed for incorporating the uncertainties associated with activities. In the proposed model, crashing and fast tracking of projects were considered simultaneously for the first time. According to a real case study considered in this paper, after using the proposed model, the cost of compression is also reduced. In addition, we determined which activities have to be crashed or fast tracked in order to attain the objective. The proposed methodology can be practically applied through mega projects such as construction and “Engineering, Procurement, and Construction” (EPC) projects where the deadline closes to being achieved. As another contribution, a unique feature of the proposed model is its capability for taking both crashing and fast-tracking simultaneously into consideration.