Designing an Agile, flexible and resilient disaster supply chain network using a hybrid group decision-making robust optimization framework

Natural disasters are a part of human history worldwide. To mitigate after-disaster damages, it is imperative to create an effective disaster supply chain. This research proposes a stochastic multi-objective mixed-integer linear programming model for an agile, flexible disaster supply chain network. Selecting domestic first-level suppliers and foreign second-level suppliers is a complicated process. We propose a novel group decision-making (GDM) framework that utilizes k-means clustering, the Borda count method, a consensus-reaching process, and the bestworst method. This framework results in the selection of qualified suppliers at the first and second levels. The GDM process utilizes agility indicators to reduce the delivery time of commodities. A robust probabilistic programming approach is used because of the uncertainty in some critical parameters in the crisis. To solve the large-scale problems, three well-known metaheuristic algorithms are employed. Metaheuristics parameters are then set to their optimum levels using the Taguchi method. Validations of the model and the solution methodologies used are based on a case study with twelve datasets. The obtained results indicate the applicability of both our group decision-making framework and the solution methodologies employed to solve the robust optimization model.

Automated summary

This research proposes a stochastic multi-objective mixed-integer linear programming model for an agile, flexible disaster supply chain network, and utilizes a novel group decision-making framework to select qualified suppliers. A robust probabilistic programming approach and metaheuristic algorithms are used to handle uncertainty and large-scale problems. The results of the study demonstrate the applicability of the proposed framework and methodologies.