Yossiri Adulyasak

Associate Academic Member

yossiri.adulyasak@hec.ca

Full professor, HEC Montréal

Université de Montréal

Scientific Advisor, IVADO Labs

Research Topics

Applied Machine Learning

Operations Research

Planning

Reinforcement Learning

Website

Google Scholar

Current Students

Xiaoxu Chen

Postdoctorate - HEC Montréal

Postdoctorate - HEC Montréal

maziyar.khadivi@mila.quebec

Yuanyuan Li

Postdoctorate - HEC Montréal

Co-supervisor :

Emma Frejinger

yuanyuan.li@mila.quebec

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… (see more)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 (preprint)

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… (see more)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 (published)

doi.org

Fair Resource Allocation in Weakly Coupled Markov Decision Processes

We consider fair resource allocation in sequential decision-making environments modeled as weakly coupled Markov decision processes, where r… (see more)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 (published)

proceedings.mlr.press