A Distributionally Robust Joint Chance Constrained Optimization Model for the Dynamic Network Design Problem under Demand Uncertainty. Sun, H., Gao, Z., Szeto, W. Y., Long, J., & Zhao, F. Networks and Spatial Economics, 14(3):409–433, December, 2014. 37 citations (Semantic Scholar/DOI) [2022-11-29]![A Distributionally Robust Joint Chance Constrained Optimization Model for the Dynamic Network Design Problem under Demand Uncertainty [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper develops a distributionally robust joint chance constrained optimization model for a dynamic network design problem (NDP) under demand uncertainty. The major contribution of this paper is to propose an approach to approximate a joint chance-constrained Cell Transmission Model (CTM) based System Optimal Dynamic Network Design Problem with only partial distributional information of uncertain demand. The proposed approximation is tighter than two popular benchmark approximations, namely the Bonferroni’s inequality and second-order cone programming (SOCP) approximations. The resultant formulation is a semidefinite program which is computationally efficient. A numerical experiment is conducted to demonstrate that the proposed approximation approach is superior to the other two approximation approaches in terms of solution quality. The proposed approximation approach may provide useful insights and have broader applicability in traffic management and traffic planning problems under uncertainty.
@article{sun_distributionally_2014,
title = {A {Distributionally} {Robust} {Joint} {Chance} {Constrained} {Optimization} {Model} for the {Dynamic} {Network} {Design} {Problem} under {Demand} {Uncertainty}},
volume = {14},
issn = {1572-9427},
url = {https://doi.org/10.1007/s11067-014-9236-8},
doi = {10.1007/s11067-014-9236-8},
abstract = {This paper develops a distributionally robust joint chance constrained optimization model for a dynamic network design problem (NDP) under demand uncertainty. The major contribution of this paper is to propose an approach to approximate a joint chance-constrained Cell Transmission Model (CTM) based System Optimal Dynamic Network Design Problem with only partial distributional information of uncertain demand. The proposed approximation is tighter than two popular benchmark approximations, namely the Bonferroni’s inequality and second-order cone programming (SOCP) approximations. The resultant formulation is a semidefinite program which is computationally efficient. A numerical experiment is conducted to demonstrate that the proposed approximation approach is superior to the other two approximation approaches in terms of solution quality. The proposed approximation approach may provide useful insights and have broader applicability in traffic management and traffic planning problems under uncertainty.},
language = {en},
number = {3},
urldate = {2022-11-26},
journal = {Networks and Spatial Economics},
author = {Sun, Hua and Gao, Ziyou and Szeto, W. Y. and Long, Jiancheng and Zhao, Fangxia},
month = dec,
year = {2014},
note = {37 citations (Semantic Scholar/DOI) [2022-11-29]},
keywords = {/unread, Demand uncertainty, Distributionally robust joint chance constraints, Dynamic network design problem, Semidefinite programming, Worst-Case Conditional Value-at-Risk},
pages = {409--433},
}
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