عنوان مقاله [English]
Team formation, selection, design, and composition is still a critical success or failure factor in any business within a company and organization. Criteria, parameters, various qualitative and quantitative methods, approaches, and techniques have been presented by several studies in TF so far. This study developed a hybrid approach to team formation (TF) and reliable supplier network design, focusing on a multi-objective model integrating fuzzy-set theory and social network analysis. Furthermore,this study addressed the relative importance value of precise relationships between members using fuzzy logic (expert workshop and fuzzy inference), backup team, capabilities (skills, expertise or knowledge), capacity, and order allocation. It also carefully considers the relationships between team members using expert workshops and fuzzy inferences. Also, social network analysis metrics are used to suggest team leader(s). We used the augmented epsilon constraint (AUGMECON2) method to validate the model and solve small-scale problems with exact solutions. The model aimed to form a reliable team and supplier network with a maximum level of reliability, maximize the network weight of collaboration, and maximize the knowledge level of the main members(suppliers), simultaneously. The approach was evaluated through a numerical study of the actual data of the electro-optical camera for team formation, design and selection of a network of reliable suppliers, and order allocation. The results showed that the approach carefully selects the optimal supplier network and team based on all assumptions and suggests team leaders with social network analysis. One of the advantages of our model is simultaneously considering supplier network, reliability, FIS and SNA in team formation. The use of uncertain data and combined methods and MADM for preselection can also be effective. The strategy of the optimal number of modules and product subsystems can also be included in the model. In future studies, other variables and parameters such as time, design phases, and the total cost can be considered. Also, because the problem is NP-hard; the use of meta-heuristic algorithms is suggested. Modeling a multi-product multi-period supply chain problem is suggested.