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.
The ever-growing stream of waste production has become a critical issue for many metropolitan areas. An effective strategy to address this problem has been the concept of reverse logistics (RL). This paper seeks to develop an appropriate product recovery approach for electronic waste generated in an urban area. Consequently, we have proposed an integrated fuzzy RL model with buyback (BB) offers based on the condition of used-products (UPs) at the time of return. However, this strategy contains a significant challenge, which derives from unpredictability surrounding the return rate of UPs due to its dependency on multiple external factors. Hence, a novel fuzzy probability function is developed to approximate UPs’ chance of return. Besides that, the mathematical RL network’s inherent uncertainty prompted us to employ the fuzzy credibility-based method in the model. Afterward, the model’s objectives are locating and allocating collection centres to customer zones, determining flow between facilities and finding the optimal amount of gathered UPs and BB offers. Finally, we applied the model to a case study concerning product recovery in Mashhad city, Iran, and the results have proven its validity and utility.
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