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 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

A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems

High-rise buildings require the installation of complex elevator group control systems (EGCS). In vertical transportation, when a passenger makes a hall call by pressing a landing call button installed at the floor and located near the cars of the elevator group, the EGCS must allocate one of the cars of the group to the hall call. We develop a Particle Swarm Optimization (PSO) algorithm to deal with this car-call allocation problem. The PSO algorithm is compared to other soft computing techniques such as genetic algorithm and tabu search approaches that have been proved as efficient algorithms for this problem. The proposed PSO algorithm was tested in high-rise buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars. Results from trials show that the proposed PSO algorithm results in better average journey times and computational times compared to genetic and tabu search approaches

Optimal car dispatching for elevator groups using genetic algorithms

The car dispatching problem in an elevator group consists of assigning cars to the hall calls at the same time that car call are served. The problem needs to coordinate the movements of individual cars with the objective of operating efficiently the whole group. In this paper, we propose an elevator group control system based on a genetic algorithm which makes use of a novel fitness function to evaluate the individuals. The fitness function allows a quick execution of the algorithm. Tests are provided for various types of high-rise buildings to assess the elevator service performance. Comparative simulations show that our genetic algorithm outperforms traditional conventional algorithms developed in the industry. It is important to note that the algorithm is quickly evaluated allowing a real-life implementatio

AJTEC2011-44028 OPTIMIZATION OF THE RELATIONSHIP BETWEEN THE TWO-PHASE FRICTION FACTOR AND REYNOLDS EQUIVALENT NUMBER MODEL BY MEANS OF GENETIC ALGORITHM

ABSTRACT The two-phase friction factor of R134a in a coppe