No abstract

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 In this paper, the authors aim to investigate the relationship between buyback policy and the potential number of used products that could be collected by developing a robust fuzzy reverse logistics network. Design/methodology/approach In this approach, the authors seek to determine the amount of buyback based on the condition of used products at the time of return. In this process, the authors also take into account that apart from the condition of used products, other factors exist that the actual return rate could be dependent on them. This matter propelled us to make a novel distinction between the probability of return estimated from appropriate buybacks offered to consumers, and the actual return rate of used products using fuzzy mathematical methods. Besides that, a compatible robust fuzzy optimization method has been implemented on the model to deal with uncertain properties of it and simultaneously fortifying its responses against any possible effect of return rate fluctuation. Findings To analyze and evaluate the model performance, the authors decided to apply a series of exhaustive randomly generated experiments onto it. Also, the authors introduced a Lagrangian relaxation solution methodology to facilitate and improve the solving process of the model. Then, the evaluation of the results enabled us to demonstrate the model validity, and underscore its utility to deal with problems with more sophisticated used product collection process that practitioners tend to encounter in the real-world circumstances. Originality/value This study suggests a novel way to design the return rate of used products in a reverse logistics network with buyback offers through a complete set of factors affecting it. Furthermore, the procedure of developing the model encompasses several important aspects that significantly decrease its complexity and improve its applicability.

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.