目的 减少高架低温库内作业的碳排放，构建出入库效率最大、货架稳定性最高和取送作业能耗最小的货位优化模型。方法 从货品存放顺序出发，采用整数编码设计遗传模拟退火算法进行求解，依据案例实际需求及面积选取了3种尺寸的备选方案，并通过MATLAB仿真对比分析各算法的结果，利用BIM建模可视化3种方案的优化结果。结果 采用整数编码设计的遗传模拟退火算法较模拟退火算法、遗传算法分别约早41代、148代收敛到最优解，计算效率分别高出21.01%、13.24%，GASA适合解决同种货品集中放置的低温库货位优化问题，建议低温库尺寸长宽比为2.25∶1。结论 优化后的货位与尺寸能够有效地降低库内作业的碳排放，保证安全的同时提高出入库效率。

The work aims to reduce the carbon emission of operation in elevated cryogenic storage and establish a cargo optimization model with the largest efficiency, the highest shelf stability and the smallest carbon emissions. Starting from the sequence of goods storage, the overall coding design genetic simulation annealing algorithm was used to solve it. Three schemes of different sizes were selected according to the actual needs and area of cases and the results of the algorithm were analyzed through the Matlab simulation comparison. BIM modeling was conducted to visualize the optimization results of the three schemes. Compared with the simulation anneal algorithm and genetic algorithm, the overall coding design genetic simulation annealing algorithm was 41 and 148 generations earlier. It was converged to optimal solutions, and its computing efficiency was 21.01% and 13.24% higher, indicating that the GASA was suitable for solving the problem of elevated cryogenic storage with concentrated placement of goods in the same species. The recommended size for elevated cryogenic storage was 2.25:1. The optimized location and size can effectively reduce the carbon emission of operation in the storage, ensure the safety and improve the efficiency of storage.