Frederic Didier
I joined Google in 2009 and since 2012 I have been part of the operations research team. I focus on the development of our core optimization libraries (open sourced in the or-tools package). I have worked on our graph libraries, LP solver and more recently I am the main developer of our CP-SAT solver.
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OR-Tools' Vehicle Routing Solver: a Generic Constraint-Programming Solver with Heuristic Search for Routing Problems
Steven Agostino Gay
Congrès de la Société française de recherche opérationnelle et d’aide à la décision (ROADEF) (2023) (to appear)
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OR-Tools is the general-purpose optimisation toolbox open-sourced by Google in 2015, being in development since 2008. This toolkit provides a uniform interface to several solvers, both first- and third-party. In particular, it offers a high-level interface for vehicle-routing problems (VRPs). OR-Tools contains several solvers, in particular two CP solvers, CP* (since the first open-source release) and CP-SAT (gold-medal winner at several MiniZinc competitions, developed since 2009), but also two linear solvers: the simplex-based Glop (since 2014), and PDLP, a first-order large-scale linear solver. OR-Tools is being actively developed, with approximately quarterly releases. Outside Google, the solver suite is easily accessible via Google Cloud, either for solving VRPs or mixed-integer linear programs, although the latter API is not yet in general access.
The routing component has historically played a strong role in the development of the overall solver; its major focus is on solving large-scale industrial vehicle-routing problems with complex constraints: vehicle capacities with various starting/ending depots, client time windows considering road traffic and driver breaks, pick-up-and-delivery precedence rules, incompatible shipments within the same vehicle, solution similarity to a previous call to the solver, etc. To this end, a high-level modelling API is proposed to the users in Python, C++, Java, and C#, using only routing concepts, even though the user has access to the underlying constraint-programming model.
From an algorithmic point of view, the routing solver is organised in three parts: (i) first-solution heuristics generate good potential vehicle tours; (ii) local search improves the first solutions, with metaheuristics to guide the search; (iii) a CP engine proves the optimality of the best solution or improves upon it. The main difference with many academic solvers is the focus on generality in the solver, including its heuristics.
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Exploiting the Structure of Unsatisfiable Cores in MaxSAT
Carlos Ansótegui
Joel Gabàs
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI (2015), pp. 283-289
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We propose a new approach that exploits the good properties of core-guided and model-guided MaxSAT solvers. In particular, we show how to effectively exploit the structure of unsatisfiable cores in MaxSAT instances. Experimental results on industrial instances show that the proposed approach outperforms both complete and incomplete state-of-the-art MaxSAT solvers at the last international MaxSAT Evaluation in terms of robustness and total number of solved instances.
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