Scheduling seminar

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schedulingseminar@rtime.felk.cvut.cz

January 2026

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Petr Vilim (OptalCP) and Vilem Heinz (CTU in Prague) | February 4 | OptalCP: Constraint Programming with Parallel Search and Reinforcement Learning-Based Acceleration
by Zdenek Hanzalek 29 Jan '26

29 Jan '26

Dear scheduling researcher, We are delighted to announce the talk given by Petr Vilim (OptalCP) and Vilem Heinz (CTU in Prague). The title is " OptalCP: Constraint Programming with Parallel Search and Reinforcement Learning-Based Acceleration ". The seminar will take place on Zoom on Wednesday, February 4 at 14:00 UTC. Join Zoom Meeting https://cesnet.zoom.us/j/92404900677?pwd=HV03FRygB2sDF5X7xI7IL68aIU9XX9.1 Meeting ID: 924 0490 0677 Passcode: 249708 You can follow the seminar online or offline on our Youtube channel as well: https://www.youtube.com/channel/UCUoCNnaAfw5NAntItILFn4A The abstract follows. Constraint Programming (CP) is a powerful paradigm for solving hard combinatorial optimization problems, especially in scheduling. In this talk, we introduce OptalCP, a modern CP solver designed for scheduling, and explain why its design is both practical and effective for real-world instances. We begin with an overview of what CP is and how models are defined using variables, domains, and constraints. We then break down the essential components of a solver—propagation and search—and show how OptalCP combines efficient propagation algorithms with parallel search strategies: LNS, failure-directed search, and user heuristics, all exchanging solutions to find better results faster. The main part focuses on OptalCP’s internal solving strategy: Large Neighborhood Search (LNS) for fast improvements, Failure-Directed Search (FDS) for strong reasoning and bounds, and efficient propagation algorithms for aggressive pruning. We explain why combining these approaches is crucial. To demonstrate, we will show the solver on a live example. In the last part, we present research results showing how reinforcement learning - specifically multi-armed bandits (MAB) - can accelerate complete CP search by reducing the explored search tree. This approach achieves state-of-the-art performance on classical JobShop and RCPSP scheduling benchmarks. The next talk in our series will be Christian Blum (IIIA-CSIC) | February 18 | CMSA: A Hybrid Metaheuristic for Combinatorial Optimization. For more details, please visit https://schedulingseminar.com/ With kind regards Zdenek Hanzalek, Michael Pinedo and Guohua Wan -- Zdenek Hanzalek Industrial Informatics Department, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic https://rtime.ciirc.cvut.cz/~hanzalek/

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