Genetic and tabu search approaches for optimizing the hall call-car allocation problem in elevator group systems
The most common problem in vertical transportation using elevator group appears when a passenger wants to travel from a floor to other different floor in a building. The passenger makes a hall call by pressing a landing call button installed at the floor and located near the cars of the elevator gro...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2011 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/40534 |
| Acceso en línea: | http://hdl.handle.net/11441/40534 https://doi.org/10.1016/j.asoc.2010.05.023 |
| Access Level: | acceso abierto |
| Palabra clave: | Elevator Lift Tabu search Genetic algorithm Elevator group system Vertical transportation Car dispatching |
| Sumario: | The most common problem in vertical transportation using elevator group appears when a passenger wants to travel from a floor to other different floor in a building. The passenger makes a hall call by pressing a landing call button installed at the floor and located near the cars of the elevator group. After that, the elevator controller receives the call and identifies which one of the elevators in the group is most suitable to serve the person having issued the call. In this paper, we have developed different elevator group controllers based on genetic and tabu search algorithms. Even though genetic algorithm has been previously considered in vertical transportation problems, the use of tabu search approaches is a novelty in vertical transportation and has not been considered previously. Tests have been carried out for high-rise buildings considering diverse sizes in the group of cars. Results indicate that the waiting time and journey time of passengers were significantly improved when dealing with such soft computing approaches. Also, a quickly evaluable solution quality function in the algorithms allows suitable computational times for industry implementation. |
|---|