Disaster management is one of the most important actions to protect the property and lives of the victims. Failure to pay attention to logistical decisions of disaster can have irreversible consequences. Therefore, a multiechelon mathematical model for blood supply chain management in disaster situations is proposed in this research. The proposed supply chain includes supplier, central warehouse, reliable distributor, unreliable distributor, distributor, and affected areas. How the proposed model performs is explained as follows: blood is sent from the supplier to warehouses and distribution centers. Also, the capacity of suppliers is limited. The main objective of the mathematical model is to minimize supply chain costs while maximizing the level of satisfaction in order to meet the demand of the affected area. Hence, this research seeks to decide whether or not to establish a reliable distributor, unreliable distributor, and central warehouse. The amount of blood sent to the centers will also be calculated. One of the contributions of the proposed model is to consider the pre- and postdisaster modes simultaneously. Locating and investigating the flow between centers are also the other contributions of this study. Solving the proposed model using a robust optimization approach is another innovation taken into account in this research. The proposed model is solved using robust optimization, and finally, the results indicate the proper performance of the proposed model.
In the green supply chain approach, all the links that are put together to provide a product or service are considered, and strategic and operational decisions are made to increase the efficiency and effectiveness of the entire chain. At the same time, the environmental effects should be minimized. In this research, a nonlinear mixed-integer multiobjective model is developed to design a green closed-loop supply chain for medical products. In this supply chain, the echelons include supplier, manufacturer, warehouse, and customer in the forward supply chain and collection centers, repair services, and disposal centers in the reverse supply chain. In the proposed model, four objectives of customer satisfaction, environmental effects, supply risk, and total costs of the supply chain were considered. The developed model is implemented in a supply chain of medical products, and after optimizing the model, the main results including location and capacity of facilities, planning for flexible production, purchase of materials, service and maintenance plan, product transfer, and inventory level are determined and analyzed.
In recent decades, green aspects became a key priority for governments worldwide, as sustainable policies are able to promote a more equitable society and a healthier economy from the social, economic, and environmental perspectives, in addition to preserving natural resources for future generations. As an essential context in information technology management, green information technology (GIT) has been developed to cope with the existing environmental problems through organizations. The present study is aimed at identifying the influential factors of decision-making on the adoption of GIT. To collect the required data, interviews were performed along with a structured survey. A total of 112 questionnaires were delivered to chief information officers (CIOs), 99 of which underwent the analysis. The structural equation and partial least square approaches were adopted for data analysis. GIT driver (G-driver) was found to be an intermediary parameter. Findings revealed the GIT readiness (G-readiness) and GIT context (G-context) result in in GIT adoption whenever there was a G-driver indicator (i.e., ethical driver, economic driver, response driver, or regulatory driver). The present study found the significance of all the variables to be above 1.96 except G-context −> green intention to adoption path and G-readiness −> green intention to adoption. Considering that the determination of coefficients and the analysis of relationships between factors directly depends on the opinion of experts and if the opinion of expert’s changes, the results will also change; this can be mentioned as the most important limitation of the research. Therefore, it should be noted that the largest impact was identified to be posed by the economical driver.
In this research, a novel mathematical model for sustainable supply chain network design is proposed. The main contribution and novelty of this research are considering environmentally friendly facilities and several thresholds for emitted pollution, which bring this research closer to real-world conditions. Since the amount of pollution produced by different supply chain facilities is not the same, it is better to make different decisions regarding each repair and renovation measure, i.e., environmental decisions should be considered step-by-step. In addition, the proposed model was optimized by the whale optimization algorithm (WOA) to find the best solution in large-scale instances in a short and reasonable time. Finally, the performance of the proposed model and the reduction of environmental impacts to improve the stability of logistics systems are reviewed in a case study. The results of the computational analysis show the efficiency of the proposed model.
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