In simulations, a simple matching strategy increases the number of donations by 5-10% a pilot experiment with real donors shows a 5% relative increase in donor action rate (from 3.7% to 3.9%). We provide theoretical guarantees for these policies, both regarding the number of expected donations and the equitable treatment of blood recipients. We develop automated policies for matching patients and donors, based on an online matching model. While measuring actual donation rates remains a challenge, we measure donor action (e.g., making a donation appointment) as a proxy for actual donation. Using the Facebook Blood Donation tool, we conduct the first large-scale algorithmic matching of blood donors with donation opportunities. Global demand for donated blood far exceeds supply, and unmet need is greatest in low- and middle-income countries experts suggest that large-scale coordination is necessary to alleviate demand. Decision-makers can select different combinations of uncertainty levels and demand disturbance ratios and necessary time constraints to obtain the optimal location-allocation solution according to risk preference and actual conditions. Also, given the necessary restrictions on the time window, the cost can be reduced by about 13% with the highest survival probability. Compared with the three MIRP models, the MIRP-ellipsoid set model gained better robustness. Finally, the numerical example experiments demonstrate that the increase in demand uncertainty and survival probability cause the MIRP model to generate more costs. Second, the survival probability function was introduced, and the time window limit was given at the minimum cost to maximize the survival probability of the suffered people. It is also constrained by the uncertain demand for blood in three scenarios. At the same time, the uncertainty of blood demand at each disaster site increased the difficulty of decision-making, and then, a robust location model (MIRP) was constructed with minimum cost with time window constraints. First, the factors of several candidate sites were quantified by the entropy-based TOPSIS method, and 12 candidate blood supply sites with higher priority were selected according to the evaluation indicators. The paper studies the robust location-allocation for emergency temporary blood supply after disaster. Whether the location of emergency temporary blood supply sites is reasonable or not directly affects the rescue efficiency.

Disaster medical rescue in China mainly adopts the вЂњon-site rescueвЂќ model.

Anylogic fifo