An indirect Genetic Algorithm for a nurse-scheduling problem

By Uwe Aickelin, K. Dowsland. Computers & Operations Research, 2004

398 citations recorded by the source corpus.

This paper describes a Genetic Algorithms (GAs) approach to a manpower-scheduling problem arising at a major UK hospital. Although GAs have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical GAs paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indirect coding based on permutations of the nurses, and a heuristic decoder that builds schedules from these permutations. Computational experiments based on 52 weeks of live data are used to evaluate three different decoders with varying levels of intelligence, and four well-known crossover operators. Results are further enhanced by introducing a hybrid crossover operator and by making use of simple bounds to reduce the size of the solution space. The results reveal that the proposed algorithm is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.

Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/9ee30a98488dd78b6cb337615b2c72137015de04

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Greet Vanden Berghe
2. Edmund Kieran Burke
3. Hendrik Van Landeghem
4. Abdur Rais
5. Ana Viana
6. Patrick De Causmaecker

In Industrial and Manufacturing Engineering

1. Abdur Rais
2. Ana Viana
3. Greet Vanden Berghe
4. Edmund Kieran Burke
5. Hendrik Van Landeghem
6. Philippe De Bruecker
7. Erik L. Demeulemeester
8. Jorne Van den Bergh

In Management Science and Operations Research

1. Abdur Rais
2. Ana Viana
3. Greet Vanden Berghe
4. Edmund Kieran Burke
5. Hendrik Van Landeghem
6. Philippe De Bruecker
7. Erik L. Demeulemeester
8. Jorne Van den Bergh

Citations in the corpus

• The State of the Art of Nurse Rostering (2004, methodology): “Both the problem and the model are comparable to those presented by Aickelin and Dowsland (2000) and Dowsland (1998) (discussed in Section 3.5). However, Blau and Sear take over- and understaffing into account whereas Aickelin and Dowsland consider solutions with coverage deficiencies as infeasible.”
• Operations Research in Healthcare: a survey (2011, methodology): “Other metaheuristic approaches can be found as follows: Evolutionary Algorithms in Aickelin et al. (2007), Aickelin and Dowsland (2004) and Aickelin and Dowsland (2000); Tabu Search in Bester et al. (2007), Ikegami and Niwa (2003), Dowsland and Thompson (2000) and Dowsland (1998); Scatter Search in…”
• Workforce planning incorporating skills: State of the art (2015, background): “), only a few incorporate decisions about the composition of shifts in their model [1, 2, 3, 7, 10, 11, 12, 24, 27, 28, 33, 48, 72, 75, 103, 104, 108].”