Portrait de Yossiri Adulyasak

Yossiri Adulyasak

Membre académique associé
yossiri.adulyasak@hec.ca
Professeur titulaire, HEC Montréal
Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle (DIRO)
Conseiller scientifique, IVADO Labs
Sujets de recherche
Apprentissage automatique appliqué
Apprentissage par renforcement
Planification
Recherche opérationnelle
Site web
Google Scholar
LinkedIn

Étudiants actuels

Publications

Fair Policy Optimization in Major-Minor Weakly Coupled Markov Decision Processes
Xiaohui Tu
Yossiri Adulyasak
Erick Delage
We consider fair resource allocation in sequential decision-making environments modeled as major-minor weakly coupled Markov decision proces… (voir plus)ses (M2WCMDP). In this framework, resource constraints couple the action spaces of a major sub-Markov decision process (sub-MDP) and a population of minor sub-MDPs that would otherwise operate independently. Instead of using the traditional utilitarian (total-sum) objective, we optimize a general class of monotone, concave, permutation-invariant, normalized fairness functions. With homogeneous minor sub-MDPs, we prove that the problem under symmetry reduces to optimizing the platform-plus-mean-participant utilitarian objective over the class of \textit{permutation-invariant} policies, which allows us to exploit efficient algorithms that optimize the utilitarian-based objective to solve this fairness-aware problem. For more general settings, we introduce a count-proportion-based deep reinforcement learning approach with a priority-based sampler that generates feasible count actions. The generality of our framework means that the proposed algorithms and theoretical guarantees transfer to any domain with a symmetric M2WCMDP structure. We consider two applications: the machine replacement problem and the joint control of pricing and taxi relocation problem on a New York City-calibrated dataset. We validate our theoretical findings with comprehensive experiments, confirming the effectiveness of our proposed method in achieving strong fairness-aware performance while remaining scalable.
2026-09-27
arXiv (prépublication)
doi.org
arxiv.org
Joint Rolling Stock and Crew Scheduling with Multi-train Composition in Urban Rail Networks
Entai Wang
Lixing Yang
Erick Delage
Jean-François Cordeau
Ziyou Gao
Yossiri Adulyasak
Rolling stock scheduling and crew scheduling are two fundamental problems that arise in the planning of urban rail operations and that are e… (voir plus)specially important in the case of flexible operations in real-world networks. These problems are often solved separately and sequentially in different planning stages, resulting in limited options to adjust crew schedules after rolling stock decisions have been made. To better adjust these two decision-making processes and achieve better solutions, this paper studies a joint rolling stock and crew scheduling problem in urban rail networks. A novel optimization model is formulated with the aim of reducing the operational cost of rolling stock units and crew members. In addition, the multi-train composition mode is considered to adequately match different frequency requirements and rolling stock transport capacities. To solve the model, a customized branch-and-price-and-cut solution algorithm is proposed to find the optimal schedule schemes, in which Benders decomposition is used to solve the linear programming relaxation of the path-based reformulation. Two customized column generation methods with label correcting are embedded to solve the master problem and pricing subproblem for generating paths (columns) corresponding to rolling stock units and crew groups, respectively. Finally, a branch-and-bound procedure with several acceleration techniques is proposed to find integer solutions. To demonstrate the computational performance and the robustness of the proposed approaches, a series of numerical experiments are performed in real-world instances of the Beijing urban rail network under different settings. The computational results confirm the high efficiency of the solution methodology and the benefits of the flexible operation schemes based on the solutions found by the proposed methods. Funding: This work was supported by National Natural Science Foundation of China [Grants 72288101, 72322022, 72371015]. The first author sincerely thanks the China Scholarship Council for supporting his visiting PhD program [Grant 202407090173]. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/trsc.2024.0905 .
2026-02-16
Transportation Science (publié)
doi.org
Fair Resource Allocation in Weakly Coupled Markov Decision Processes
Xiaohui Tu
Yossiri Adulyasak
Nima Akbarzadeh
Erick Delage
We consider fair resource allocation in sequential decision-making environments modeled as weakly coupled Markov decision processes, where r… (voir plus)esource constraints couple the action spaces of
2025-05-02
International Conference on Artificial Intelligence and Statistics (Accept (Poster))
doi.org
proceedings.mlr.press
Planning and Learning in Risk-Aware Restless Multi-Arm Bandits
Nima Akbarzadeh
Yossiri Adulyasak
Erick Delage
2025-04-22
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (publié)
proceedings.mlr.press
proceedings.mlr.press