Developing a bi-objective resilience relief logistic considering operational and disruption risks: a post-earthquake case study in Iran

Today, according to the occurrence of numerous disasters in allover over the world, designing the proper and comprehensive plan for relief logistics has received a lot of attention from crisis managers and people. Besides, considering resilience capability along with operational and disruption risks leads to the robustness of the humanitarian relief chain (HRC), and this comprehensive framework ensures the essential supplies delivery to the beneficiaries and is close to real-world problems. The resilience parameters used for the second objective are obtained by a strong Best Worst Method (BWM). Another supposition of the model is the consideration of uncertainty in all stages of the proposed problem. Moreover, the multiple disasters (sub-sequent minor post disasters) which can increase the initial demand are considered. Furthermore, the proposed model is solved using three well-known metaheuristic algorithms includes non-dominated sorting genetic algorithm (NSGA-II), network reconfiguration genetic algorithm (NRGA), and multi-objective particle swarm optimization (MOPSO), and their performance is compared by several standard multi-objective measure metrics. Finally, the obtained results show the robustness of the proposed approaches, and some directions for future researches are provided.

Automated summary

Given the occurrence of numerous disasters worldwide, the design of proper and comprehensive relief logistics plans has become a priority for crisis managers and the general population. This study proposes a comprehensive framework that considers resilience, operational and disruption risks to ensure timely delivery of essential supplies to beneficiaries. The resilience parameters are obtained using a strong Best Worst Method (BWM) and uncertainty is considered at all stages of the proposed problem. Three well-known metaheuristic algorithms are used to solve the model and their performance is compared using standard multi-objective measure metrics. The results demonstrate the robustness of the proposed approaches and provide directions for future research.