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Modeling and solving staff scheduling with partial weighted maxSAT.

Emir Demirović1, Nysret Musliu1, Felix Winter1

  • 1Database and Artificial Intelligence Group, Vienna University of Technology, Vienna, Austria.

Annals of Operations Research
|March 19, 2019
PubMed
Summary
This summary is machine-generated.

This study models complex employee scheduling as a weighted partial maximum satisfiability (maxSAT) problem. Researchers compared constraint encodings and solver performance, offering new benchmarks for SAT solver development.

Keywords:
Cardinality constraintsEmployee schedulingSAT encodingsmaxSAT

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Area of Science:

  • Operations Research
  • Computer Science
  • Artificial Intelligence

Background:

  • Employee scheduling is a complex, pervasive problem across industries like healthcare and transportation.
  • Existing scheduling methods often struggle with the complexity of real-world workforce management.

Purpose of the Study:

  • To model challenging staff scheduling instances using a weighted partial maximum satisfiability (maxSAT) framework.
  • To evaluate the effectiveness of different cardinality constraint encodings for this problem.
  • To benchmark the performance of leading maxSAT solvers on these scheduling instances.

Main Methods:

  • Formulation of staff scheduling problems as weighted partial maxSAT instances.
  • Comparative analysis of four distinct cardinality constraint encodings.
  • Performance evaluation of two leading maxSAT solvers using benchmark experiments.

Main Results:

  • Demonstrated the applicability of the maxSAT formulation to complex scheduling scenarios.
  • Identified performance differences among cardinality constraint encodings.
  • Provided a comparative analysis of state-of-the-art maxSAT solver performance.

Conclusions:

  • The maxSAT approach offers a robust method for tackling intricate employee scheduling challenges.
  • The generated benchmark instances facilitate the advancement and validation of SAT solvers.
  • This research contributes valuable insights into optimizing workforce scheduling through advanced computational methods.