Employee Scheduling With SAT-Based Pseudo-Boolean Constraint Solving

The aim of this paper is practical: to show that, for at least one important real-world problem, modern SAT-based technology can beat the extremely mature branch-and-cut solving methods implemented in well-known state-of-the-art commercial solvers such as CPLEX or Gurobi. The problem of employee scheduling consists in assigning a work schedule to each of the employees of an organization, in such a way that demands are met, legal and contractual constraints are respected, and staff preferences are taken into account. This problem is typically handled by first modeling it as a 0–1 integer linear program (ILP). Our experimental setup considers as a case study the 0–1 ILPs obtained from the staff scheduling of a real-world car rental company, and carefully compares the performance of CPLEX and Gurobi with our own simple conflict-driven constraint-learning pseudo-Boolean solver.