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Distributionally robust Weber problem with uncertain demand by Yan Gu; Jianlin Jiang; Shun Zhang is a Computer Science article available to read on EtoBox.

What is Distributionally robust Weber problem with uncertain demand about?

Weber problem is an important model in facility location field, and it can be modeled as a stochastic problem when the future demand of customers is uncertain. By minimizing the maximal expectation of the objective on an ambiguity set, the distributionally robust optimization (DRO) can utilize the valuable information from historical data and thus it has become an attractive formulation for stochastic problems. In this paper, an extended moment-based DRO formulation and a polynomial-time algorithm are contributed to solving the Weber problem with uncertain demand. Specifically, by constructing a new ambiguity set, which is proved to contain the true distribution with high probability, an extended DRO formulation allowing positive semidefinite covariance matrix is first built for general stochastic problems. To obtain a more robust solution, the Weber problem is then reformulated into an equivalent stochastic variational inequality (SVI). Following the extended DRO formulation, a distributionally robust Weber problem (DRWP) is further developed with minimizing the expectation of a residual function of the SVI. The DRWP can be transformed to a semidefinite program (SDP) with an undes

Who reads Distributionally robust Weber problem with uncertain demand?

Author

Yan Gu; Jianlin Jiang; Shun Zhang

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)