(last update: Sep 2020)
Submitted articles:
Note: articles submitted under double blinded reviews are not included
  • Mahéo, A., Belieres, S., Adulyasak, Y., Cordeau, J.-F., 2020. Unified Branch-and-Benders-Cut for Two-Stage Stochastic Mixed-Integer Programs[pdf] (Submitted)
  • Zhang, G., Zhu, N., Ma, S., Adulyasak, Y., 2020. Robust Drone Selective Routing Problem in Humanitarian Transportation Network Assessment[pdf] (Submitted)
  • Cheng, C., Adulyasak, Y., Rousseau, L.-M., Sim, M. 2020. Robust Drone Delivery with Weather Information. [pdf] (Submitted)
  • Cheng, C., Adulyasak, Y., Rousseau, L.-M., 2019. Robust Facility Location under Disruptions. [pdf] (Under revision at INFORMS Journal on Optimization)
  • Martinez, K., Adulyasak, Y., Jans, R., 2019. Logic–Based Benders Reformulations for Integrated Process Configuration and Production Planning Problems. [pdf] (Under revision at INFORMS Journal on Computing)
  • Cheng, C., Adulyasak, Y., Rousseau, L.-M., 2019Robust Facility Location Under Demand Uncertainty and Facility Disruptions. [pdf] (Under revision at Omega: The International Journal of Management Science)
  • Thevenin, S., Adulyasak, Y., Cordeau, J.-F., 2020. Material Requirements Planning Under Demand Uncertainty Using Stochastic OptimizationProduction and Operations Management (To appear)[pdf, e-companion:  working paper version] 
  • Sereshti, N., Adulyasak, Y., Jans, R., 2020. The Value of Aggregate Service Levels in Stochastic Lot Sizing ProblemOmega: The International Journal of Management Science (To appear)[pdf:  working paper version] 
  • Yu, Q., Adulyasak, Y., Rousseau, L.-M., Zhu, N., Ma, S., 2020. Team Orienteering with Time-Varying ProfitINFORMS Journal on Computing (To appear). [pdfworking paper version] 
  • Hoogeboom, M., Adulyasak, Y., Dullaert, W., Jaillet, P. 2020. The Robust Vehicle Routing Problem with Time Window AssignmentsTransportation Science (To appear). [pdfworking paper version] 
  • Yarkony, J., Adulyasak, Y., Singh, M., Desaulniers, G. 2020. Data Association via Set Packing for Computer Vision Applications. INFORMS Journal on Optimization 2(3). [Link]
  • Cheng, C., Adulyasak, Y., Rousseau, L.-M., 2020. Drone Routing with Energy Function: Formulation and Exact Algorithm. Transportation Research Part B: Methodological 139. 364-387. [Link]
  • Nguyen, D.-T., Adulyasak, Y., Landry, S., 2019. Bullwhip Effect in Rule-Based Supply Chain Planning Systems: A Case-Based Simulation at a Hard Goods Retailer. Omega: The International Journal of Management Science. (In press). [Link]
  • Martinez, K., Adulyasak, Y., Jans, R., Morabito, R., Toso,  E.A.V., 2019. An Exact Optimization Approach for an Integrated Process Configuration, Lot-Sizing and Scheduling Problem. Computers & Operations Research 103. 310-323. [Link]
  • Deudon, M., Cournut, P., Lacoste, A., Adulyasak, Y., Rousseau, L.-M., 2018. Learning Heuristics for the TSP by Policy GradientIn: van Hoeve WJ. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR). Lecture Notes in Computer Science, vol 10848. [Link]. Here is the excellent [Github] by M. Deudon.
  • Ahmed, A., Varakantham, P., Lowalekar, M. , Adulyasak Y., Jaillet, P., 2017. Sampling based Approaches for Minimizing Regret in Uncertain Markov Decision Problems (MDPs). Journal of Artificial Intelligence Research 59. 229-264. [pdf]
  • Ghosh, S., Varakantham, P., Adulyasak, Y., Jaillet, P., 2017. Dynamic Redeployment to Reduce Lost Demand in Bike Sharing SystemsJournal of Artificial Intelligence Research 58, 387-430. [pdf]
  • Adulyasak, Y., Jaillet, P., 2016. Models and Algorithms for Stochastic and Robust Vehicle Routing with DeadlinesTransportation Science 50 (2). 608-626. [Link] [Supplement]
  • Adulyasak, Y., Cordeau, J.-F., Jans, R., 2015. Benders Decomposition for Production Routing under Demand Uncertainty. Operations Research 63 (4). 851-867. [Link] [Supplement]
  • Ghosh, S., Varakantham, P., Adulyasak, Y., Jaillet, P., 2015. Dynamic Redeployment to Counter Congestion or Starvation in Vehicle Sharing Systems25th International Conference on Automated Planning and Scheduling (ICAPS). [pdf]
  • Adulyasak, Y., Varakantham, P., Jaillet, P., 2015. Solving Uncertain MDPs with Objectives that are Separable over Instantiations of Model Uncertainty29th Conference on Artificial Intelligence (AAAI). [pdf]
  • Adulyasak, Y., Cordeau, J.-F., Jans, R., 2014. The Production Routing Problem: A Review of Formulations and Solution Algorithms. Computers & Operations Research 55. 141-152. [Link]
  • Adulyasak, Y., Cordeau, J.-F., Jans, R., 2014. Formulations and Branch-and-Cut Algorithms for Multivehicle Production and Inventory Routing Problems. INFORMS Journal on Computing 26 (1). 103-120. [Link] [Supplement] [instances and results]
  • Varakantham, P., Adulyasak, Y., Jaillet, P., 2014. Decentralized Stochastic Planning with Anonymity in Interactions28th Conference on Artificial Intelligence (AAAI). [pdf]
  • Adulyasak, Y., Cordeau, J.-F., Jans, R., 2014. Optimization-Based Adaptive Large Neighborhood Search for the Production Routing ProblemTransportation Science 48 (1). 20-45. [Link[instances and results]
  • Ahmed, A., Varakantham, P., Adulyasak, Y., Jaillet, P., 2013. Regret based Robust Solutions for Uncertain Markov Decision ProcessesAdvances in Neural Information Processing Systems (NeurIPS) 26. [pdf]
  • Desrosiers, J., Jans, R. Adulyasak, Y., 2013. Improved Column Generation Algorithms for Clustering Problems. GERAD Tech Rep. G-2013-26. [pdf]
PhD thesis:
  • Adulyasak, Y., 2012. Models and Solution Algorithms for Production Routing ProblemsHEC Montréal. [pdf]

*Please email me if the links do not work
  • Optimization-Based Adaptive Large Neighborhood Search for the Production Routing Problem
  1. Instance set A (Archetti et al. instances) [download] (originally from
  2. Instance set B (Boudia et al. instances)  - [download]
Detailed results -  [download]
  • Formulations and Branch-and-Cut Algorithms for Multi-Vehicle Production and Inventory Routing Problems
  1. MVPRP instances - generated from the instance set A above (can be obtained here [download])
  2. MVIRP instances - generated from the instances from (can also be downloaded here [download]) (note: see computational experiments section of the paper for the details of the instance generation)
Detailed results
  1. Details of the best heuristic solutions generated by Op-ALNS - [download]
  2. Details of the exact solutions  - [download]