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Hybrid Heuristics for Multimodal Homecare Scheduling

  • Andrea Rendl
  • Matthias Prandtstetter
  • Gerhard Hiermann
  • Jakob Puchinger
  • Günther Raidl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7298)

Abstract

We focus on hybrid solution methods for a large-scale real-world multimodal homecare scheduling (MHS) problem, where the objective is to find an optimal roster for nurses who travel in tours from patient to patient, using different modes of transport. In a first step, we generate a valid initial solution using Constraint Programming (CP). In a second step, we improve the solution using one of the following metaheuristic approaches: (1) variable neighborhood descent, (2) variable neighborhood search, (3) an evolutionary algorithm, (4) scatter search and (5) a simulated annealing hyper heuristic. Our evaluation, based on computational experiments, demonstrates how hybrid approaches are particularly strong in finding promising solutions for large real-world MHS problem instances.

Keywords

Local Search Variable Neighborhood Search Scatter Search Vehicle Route Problem With Time Window Construction Heuristic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrea Rendl
    • 1
  • Matthias Prandtstetter
    • 1
  • Gerhard Hiermann
    • 2
  • Jakob Puchinger
    • 1
  • Günther Raidl
    • 2
  1. 1.Mobility Department, Dynamic Transportation SystemsAIT Austrian Institute of TechnologyAustria
  2. 2.Vienna University of TechnologyAustria

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