Un modelo ACO para una versión no estacionaria del problema del ascensor único

The Ant Colony Optimization (ACO) metaheuristic is a bio-inspired approach for hard combinatorial optimization problems for stationary and non-stationary environments. In the ACO metaheuristic, a colony of artificial ants cooperate for finding high quality solutions in a reasonable time. An interesting example of a non-stationary combinatorial optimization problem is the Multiple Elevators Problem (MEP) which consists in finding a sequence of movements for each elevator to perform in a building so that to minimize, for instance, the waiting average time of the passengers. Events like the arrival of one new passenger to the elevator queue or the fault of one elevator produce dynamically the changes of state in this problem. A subclass of MEP is the non-stationary version of the so called Single Elevator Problem (SEP). In this work, we propose the design of an ACO model for the SEP that can be implemented as an Ant Colony System (ACS).

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