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 sc...
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Published in | IEEE access Vol. 9; pp. 142095 - 142104 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 2169-3536 2169-3536 |
DOI: | 10.1109/ACCESS.2021.3120597 |