A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach.

Annually, different regions of the world are affected by natural disasters such as floods and earthquakes, resulting in significant loss of lives and financial resources. These events necessitate rescue operations, including the provision and distribution of relief items like food and clothing. One of the most critical challenges in such crises is meeting the blood requirement, as an efficient and reliable blood supply chain is indispensable.

Energy consumption prediction in an office building by examining occupancy rates and weather parameters using the moving average method and artificial neural network.

Occupancy rate refers to the level of usage and presence of individuals within a building or a specific space. This factor can have a significant impact on building energy consumption. When the occupancy rate in a building is high, naturally, energy consumption also increases.