A credibility-based multi-objective temporary logistics hub location-allocation model for relief supply and distribution under uncertainty - Maharjan, Rajali; Hanaoka, Shinya (Socio-economic planning sciences, 2020)
Disaster response operations revolve around uncertainties. While uncertainties arising due to randomness can be avoided for post-disaster location problem, those arising because of impreciseness may persist long after the disaster's occurrence. Despite the uncertainties and lack of sufficient information about the extent of the damage, disaster response facilities must be established quickly after the occurrence of the disaster. Moreover, the decisions of whether to open, where to locate, and when to open disaster response facilities are based on the amount and quality of information available during the decision-making period. To address these issues, we develop a multi-objective location-allocation model for relief supply and distribution that accounts for the imprecise and time-varying nature of different parameters and time-varying coverage, while also accommodating the subjective attributes necessary to enable establishment and operation of the temporary logistics hubs (TLHs). A credibility-based fuzzy chance-constrained programming model is employed to account for the impreciseness inherent in predicting parameter values during disaster response. The results show where, when, and how many TLHs to open and how to allocate relief supplies. Meanwhile, the sensitivity analysis provides a broader understanding of the impact of limiting the number of TLHs as well as the confidence level and the spread of the symmetric triangular fuzzy numbers on the attainment of the model objectives. •Disaster response operations are complicated because of uncertain and time-varying nature of parameters.•The uncertainty during disaster response is often epistemic which arises due to impreciseness.•We develop a multi-objective location model for relief supply and distribution.•Our model accounts for the epistemic uncertainty in demand, costs, and available relief.•A credibility-based fuzzy chance-constrained programming model is employed to account for epistemic uncertainty.