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. On the basis of 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 used 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 the robust optimization model is insensitive to the disturbance of parameters under different scenarios and better than the result of stochastic optimization model.