A BI-OBJECTIVE ROBUST POSSIBILISTIC COOPERATIVE GRADUAL MAXIMAL COVERING MODEL FOR RELIEF SUPPLY CHAIN WITH UNCERTAINTY

Abstract

The occurrence of natural and artificial disasters due to their unexpected nature requires precise planning and management in the relief supply chain. A major measure in times of crisis is to assist the damaged points. Due to the limitations in the relief process at the time of the accident, relief centers should be opened in appropriate locations that cover the needs of the damaged points in the shortest possible time. Initially, a nonlinear two-level cooperative gradual maximal covering model in relief supply chain is proposed first. The chain includes supply centers, relief, and damaged points under uncertainty of some key parameters. The major goal is to locate the relief centers and determine the allocations and transfer of goods between the two levels. The bi-objective model minimizes the high logistical costs and maximizes damaged points’ coverages with uncertain costs. Different robust possibilistic programming approaches have utilized the given approaches’ performances, and some suitable recommendations are given. The robust possibilistic model provides the best results among all models. The results show that the robust possibilistic programming model outperforms the possibilistic programming model.