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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)


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.