Sub-daily Staff Scheduling for a Logistics Service Provider
| dc.contributor.author | Günther, Maik | |
| dc.contributor.author | Nissen, Volker | |
| dc.date.accessioned | 2018-01-08T09:14:21Z | |
| dc.date.available | 2018-01-08T09:14:21Z | |
| dc.date.issued | 2010 | |
| dc.description.abstract | The current paper uses a scenario from logistics to show that solution approaches based on metaheuristics, and in particular particle swarm optimization (PSO) can significantly add to the improvement of staff scheduling in practice. Sub-daily planning, which is the focus of our research offers considerable productivity reserves for companies but also creates complex challenges for the planning software. Modifications of the traditional PSO method are required for a successful application to scheduling software. Results are compared to different variants of the evolution strategy (ES). Both metaheuristics significantly outperform manual planning, with PSO delivering the best overall results. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11137 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 24, No. 2 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Combinatorial optimization | |
| dc.subject | Evolution strategy | |
| dc.subject | Particle swarm optimization | |
| dc.subject | Staff scheduling | |
| dc.subject | Sub-daily planning | |
| dc.title | Sub-daily Staff Scheduling for a Logistics Service Provider | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 113 | |
| gi.citation.startPage | 105 |