A bi-objective two-stage stochastic optimization for designing a resilient humanitarian relief chain considering hybrid contracts, public donations, and item perishability

This paper presents a bi-objective, two-stage stochastic programming (TSSP) model for designing a resilient humanitarian relief chain (HRC). The model considers uncertainties in demand, public donations, and disruption risks while integrating decisions in both the pre- and post-crisis stages. This model optimizes critical decisions such as supplier selection, establishing contracts, pre-positioning items, inventory management, and distribution of perishable and non-perishable items. It aims to minimize the total network costs and the maximum average travel time to each demand point. To tackle disruptions in network facilities and links, several resilience strategies were adopted, including inventory pre-positioning and holding safety stock, as well as backup suppliers. Additionally, a hybrid supply contract was established, consisting of quantity flexibility contracts (QFCs) and option contracts (OPCs) between the governmental relief organization (GRO) and the main and backup suppliers, ensuring an acceptable level of responsiveness in the relief network. The epsilon-constraint method is utilized in GAMS software to illustrate different numerical instances and a case study of Tabriz City in Iran, demonstrating its applicability. The findings reveal that resilience strategies significantly enhance cost efficiency and response times, with pre-positioning improving overall costs by 28.42% and response times by 84.79% as the most influential strategy. The results review and sensitivity analysis provided valuable managerial insights for designing a functional and effective HRC.