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Frohner, N., Teuschl, S., & Raidl, G. R. (2019). Casual Employee Scheduling with Constraint Programming and Metaheuristics. In Computer Aided Systems Theory – EUROCAST 2019 (pp. 279–287). LNCS. https://doi.org/10.1007/978-3-030-45093-9_34
E192-01 - Forschungsbereich Algorithms and Complexity E192 - Institut für Logic and Computation
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Published in:
Computer Aided Systems Theory – EUROCAST 2019
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Date (published):
2019
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Event name:
International Conference on Computer Aided Systems Theory (Eurocast)
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Event date:
15-Feb-2009 - 20-Feb-2009
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Event place:
Gran Canaria, Spain
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Number of Pages:
9
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Publisher:
LNCS, 12013
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Peer reviewed:
Yes
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Abstract:
We consider an employee scheduling problem where many
casual employees have to be assigned to shifts defined by the requirement
of different work locations. For a given planning horizon, locations specify
these requirements by stating the number of employees needed at specific
times. Employees place offers for shifts at locations they are willing to
serve. The goal is to find an assignment of...
We consider an employee scheduling problem where many
casual employees have to be assigned to shifts defined by the requirement
of different work locations. For a given planning horizon, locations specify
these requirements by stating the number of employees needed at specific
times. Employees place offers for shifts at locations they are willing to
serve. The goal is to find an assignment of employees to the locations´
shifts that satisfies certain hard constraints and minimizes an objective
function defined as weighted sum of soft constraint violations. The soft
constraints consider ideal numbers of employees assigned to shifts, distribution
fairness, and preferences of the employees. The specific problem
originates in a real-world application at an Austrian association. In
this paper, we propose a Constraint Programming (CP) model which we
implemented using MiniZinc and tested with different backend solvers.
As the application of this exact approach is feasible only for small to
medium sized instances, we further consider a hybrid CP/metaheuristic
approach where we create an initial feasible solution using a CP solver
and then further optimize by means of an ant colony optimization and
a variable neighborhood descent. This allows us to create high-quality
solutions which are finally tuned by a manual planner.
