@article{nokey,

title = {Beyond Homogeneous Adversaries: Stackelberg Security Games with Mixed Quantal Response},

author = {Hoang Giang Pham, Tien Mai, Thuy Anh Ta, Minh Hoàng Hà},

year  = {2026},

date = {2026-07-31},

abstract = {The quantal response (QR) model is widely used in Stackelberg security games (SSGs) to capture boundedly rational adversaries. Existing work on SSGs under QR, however, almost exclusively assumes a homogeneous attacker population, ignoring heterogeneity in attacker preferences and rationality. We study SSG with mixed quantal response attackers, where the follower population consists of multiple discrete attacker types, each following a type-specific QR model. The defender allocates limited resources across targets, while an attacker drawn from this heterogeneous population observes the defender’s strategy and attacks a single target. This results in a highly non-convex equilibrium computation problem. We develop a polynomial-time approximation scheme (PTAS) for this setting when the number of attacker types is bounded, based on an exponential cone programming formulation combined with a carefully designed Branch-and-Bound procedure. Experiments demonstrate that our approach outperforms standard gradient-based methods and that explicitly modeling attacker heterogeneity yields significant gains over traditional SSG models with a single QR attacker.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Le2026,

title = {Competitive Facility Location Under Cross-Nested Logit Customer Choice Model: Hardness and Exact Approaches},

author = {Ba Luat Le and Tien Mai and Thuy Anh Ta and Minh Hoàng Hà and Duc-Minh Vu},

doi = {10.1287/ijoc.2025.1150},

issn = {1526-5528},

year  = {2026},

date = {2026-05-06},

urldate = {2026-05-06},

journal = {INFORMS Journal on Computing},

publisher = {Institute for Operations Research and the Management Sciences (INFORMS)},

abstract = {<jats:p>We study the competitive facility location problem, in which a firm aims to establish new facilities in a market already occupied by competitors. In this problem, customer behavior is a crucial factor in making optimal location decisions. We explore a general class of customer choice models, known as the cross-nested logit model, which is recognized for its flexibility and generality in predicting people’s choice behavior. To explore the problem, we first demonstrate that it is NP-hard even when there is only one customer class and the cross-nested structure has only two nests. To tackle the challenging facility location problem, we demonstrate that the objective function under a general cross-nested structure is not concave. Interestingly, we show that, by a change of variables, the objective function can be converted to a mixed-integer exponential cone convex program, enabling it to be solved to optimality via an outer approximation algorithm. Extensive experiments show the efficiency of our approach and provide analyses on the benefits of using the cross-nested model in the facility location context.</jats:p>

                  <jats:p>History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete.</jats:p>

                  <jats:p>Funding: This work was supported by the Vingroup Innovation Foundation, Vietnam [Grant VINIF.2024.DA072].</jats:p>

                  <jats:p>Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2025.1150 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2025.1150 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Le2026CFL_CNL,

title = {Competitive Facility Location under Cross-Nested Logit Customer Choice Model: Hardness and Exact Approaches},

author = {Ba Luat Le and Tien Mai and Thuy Anh Ta and Minh Hoang Ha and Duc Minh Vu},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {INFORMS Journal on Computing},

note = {Accepted March 2026, to appear},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rosati2026_conflicting_jobs_itor,

title = {Exact and Metaheuristic Approaches to Minimizing Makespan in Parallel Machine Scheduling with Conflicting Jobs},

author = {Roberto Maria Rosati and Dinh Quy Ta and Minh Hoang Ha and Andrea Schaerf},

doi = {10.1111/itor.70177},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {International Transactions in Operational Research},

pages = {1–27},

note = {Version of record online: 15 February 2026},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ta2025SBRPRescheduling,

title = {The rescheduling school bus routing problem under a constraint on the number of students with negative impact},

author = {Quy Ta Dinh and Thu Huong Dang and Minh Hoang Ha and Duc Minh Vu and Tien Mai},

url = {https://doi.org/10.1080/01605682.2025.2519991},

doi = {10.1080/01605682.2025.2519991},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Journal of the Operational Research Society},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zhao2025_irp_hgs,

title = {Large Neighborhood and Hybrid Genetic Search for Inventory Routing Problems},

author = {Jingyi Zhao and Claudia Archetti and Tuan Anh Pham and Thibaut Vidal},

doi = {10.1016/j.ejor.2025.11.021},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {European Journal of Operational Research},

note = {In press, corrected proof; available online 25 November 2025},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nguyen2025_stop,

title = {The Set Team Orienteering Problem},

author = {Tat Dat Nguyen and Rafael Martinelli and Quang Anh Pham and Minh Hoang Ha},

doi = {10.1016/j.ejor.2024.09.021},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {European Journal of Operational Research},

volume = {321},

number = {1},

pages = {75–87},

keywords = {},

pubstate = {published},

tppubtype = {article}

}