A new risk quantification method in project-driven supply chain by MABACODAS method under interval type-2 fuzzy environment with a case study. (March 2023)

• Record Type: Journal Article
• Authors: Dorfeshan, Yahya; Jolai, Fariborz; Mousavi, Seyed Meysam
• Is Part Of: Engineering applications of artificial intelligence. Volume 119(2023)
• Publication Date: 2023-03
• Subjects: Project-driven supply chain -- Risk quantification method -- MABACODAS decision model -- Dynamic critical path -- Resilience index; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105729
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Simultaneous decision-making in the supply chain and project management has attracted much attention. The integration of these two areas is called the project-driven supply chain. Timely supply of materials and services from the supply chain is significant for project activities. For this purpose, this paper presents a new method for the first time in the literature to quantify the supply risks in project activities and objectives. At first, the project's critical path is specified according to the time, cost, risk, and quality criteria through a new decision model, namely MABACODAS, under parametric fuzzy values. The MABACODAS consists of two parts; One is about the degree of criticality of the paths, and the other is about determining the weight of experts in group decision-making. Then, a dynamic view is investigated under type-2 fuzzy numbers in the project, and it is observed that the critical path changes for different alpha values. The project suppliers' resilience and each activity's resilience are determined through the proposed MABACODAS model to obtain the appropriate alpha value. In other words, by using the resilience of each activity, the alpha value is selected, and the critical path is specified by considering the risk of the supply chain. Notably, the MABACODAS model is extended under interval type-2 fuzzy sets for uncertainty considerations. The main contribution of this study is introducing a risk quantification method. This method uses the MABACODAS method for specifying the criticality index of paths and resilience scores of suppliers. Also, this method is expanded under the IT2FSs. Finally, a real case study of the construction project is presented to show the applicability of the introduced model. Highlights: Proposing a new IT2F risk quantification method for project-driven supply chain. Considering dynamic nature of critical paths using IT2F parametric numbers. Presenting a new decision-making method to determine suppliers' resiliency index. Introducing a new MABACODAS method for finding critical path by effective criteria. Determining weights of experts in group decision-making by a new decision approach.