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研究了不确定环境下物流中心的选址优化问题，在随机优化模型的基础上，采用遗憾模型的形式构建了相关问题的鲁棒优化模型。分析了鲁棒优化模型与确定性优化模型、随机优化模型的关系，并在此基础上给出了求解鲁棒优化模型的两种方法——枚举法和遗传算法。以Visual Studio6.0为平台，以Visual C++为开发语言编写了两种算法的代码，代码中通过调用Lingo9.0来求解确定性优化模型和两阶段随机优化模型。利用上述两种算法对若干算例进行了测试，结果表明，本文给出的算法能够满足问题求解需要，与随机优化模型最优解相比，鲁棒优化模型的最优解对各情景下参数扰动的现象敏感程度更低，因此具有更低的风险。

Robust Optimization Model and Algorithm for Logistics Center Location and Allocation under Uncertain Environment

This study focuses on the logistics center location and allocation problem under uncertain environment. Based on the stochastic optimization model, a robust optimization model using the formation of regret model is proposed. Then, the relations among the robust optimization model, stochastic optimization model, and deterministic optimization model are analyzed and two algorithms, enumeration method and genetic algorithm are presented. The codes of the two algorithms are implemented by Visual C++ on Visual Studio 6.0. Optimization software Lingo 9.0 is utilized in the code to solve the deterministic optimization model and two-stage stochastic optimization model. Numerical experiments show that the algorithms are acceptable to solve the problem. Moreover, the optimal solution of robust optimization model is insensitive to the disturbance of parameters under different scenarios and better than the result of stochastic optimization model.