Distributionally Robust Chance-Constrained p -Hub Center Problem

Yue Zhao (), Zhi Chen () and Zhenzhen Zhang ()
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Yue Zhao: Institute of Operations Research and Analytics, National University of Singapore, Singapore 119077
Zhi Chen: CUHK Business School, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
Zhenzhen Zhang: School of Economics and Management, Tongji University, Shanghai 200092, China

INFORMS Journal on Computing, 2023, vol. 35, issue 6, 1361-1382

Abstract: The p -hub center problem is a fundamental model for the strategic design of hub location. It aims at constructing p fully interconnected hubs and links from nodes to hubs so that the longest path between any two nodes is minimized. Existing literature on the p -hub center problem under uncertainty often assumes a joint distribution of travel times, which is difficult (if not impossible) to elicit precisely. In this paper, we bridge the gap by investigating two distributionally robust chance-constrained models that cover, respectively, an existing stochastic one under independent normal distribution and one that is based on the sample average approximation approach as a special case. We derive deterministic reformulations as a mixed-integer program wherein a large number of constraints can be dynamically added via a constraint-generation approach to accelerate computation. Counterparts of our models in the emerging robust satisficing framework are also discussed. Extensive numerical experiments demonstrate the encouraging out-of-sample performance of our proposed models as well as the effectiveness of the constraint-generation approach.

Keywords: p -hub center problem; ambiguous chance constraints; Wasserstein distance; elliptical distribution; empirical distribution (search for similar items in EconPapers)
Date: 2023
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