In the aftermath of a disaster, acquiring and fulfilling blood demand is essential to prevent further loss of lives. Consequently, in recent years, the concept of blood supply chain design for disaster relief has gained immense importance. This article addresses this issue by developing a novel bi-objective scenario-based mathematical model to minimize the system's costs while enhancing the blood supply rate. The proposed framework encompasses three echelons of blood centers, hospitals, and backup blood centers. The model is developed using a set of techniques, including backup coverage, lateral transshipment, gift card, buffer storage, and blood transfusion. This specific combination aims to reduce blood shortages in the system by improving the coordination among different echelons of the network and encouraging more eligible individuals to donate blood. Next, the model is evaluated by applying it to a case study concerning a probable dangerous earthquake in Tehran capital of Iran. Furthermore, a Lagrangian relaxation method is implemented on the model to improve its capability in dealing with larger-scale problems with higher efficiency. Finally, the results and analysis demonstrate our approach's validity and advantages in satisfying the blood demand in a disaster time.

Purpose Globalization of markets and pace of technological change have caused the growing importance of paying attention to supplier selection problem. Therefore, this study aims to choose the best suppliers by providing a mathematical model for the supplier selection problem considering the green factors and stochastic parameters. This paper aims to propose a multi-objective model to identify optimal suppliers for a green supply chain network under uncertainty. Design/methodology/approach The objective of this model is to select suppliers considering total cost, total quality parts and total greenhouse gas emissions. Also, uncertainty is tackled by stochastic programming, and the multi-objective model is solved as a single-objective model by the LP-metric method. Findings Twelve numerical examples are provided, and a sensitivity analysis is conducted to demonstrate the effectiveness of the developed mathematical model. Results indicate that with increasing market numbers and final product numbers, the total objective function value and run time increase. In case that decision-makers are willing to deal with uncertainty with higher reliability, they should consider whole environmental conditions as input parameters. Therefore, when the number of scenarios increases, the total objective function value increases. Besides, the trade-off between cost function and other objective functions is studied. Also, the benefit of the stochastic programming approach is proved. To show the applicability of the proposed model, different modes are defined and compared with the proposed model, and the results demonstrate that the increasing use of recyclable parts and application of the recycling strategy yield more economic savings and less costs. Originality/value This paper aims to present a more comprehensive model based on real-world conditions for the supplier selection problem in green supply chain under uncertainty. In addition to economic issue, environmental issue is considered from different aspects such as selecting the environment-friendly suppliers, purchasing from them and taking the probability of defective finished products and goods from suppliers into account.

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