Metaheuristic algorithms for elevator group control system: a holistic review

Optimization plays a crucial role in the elevator group control system (EGCS) since various unpredictable factors, such as future traffic demand of each floor, passengers’ random destinations, and indiscriminate starting-stopping of elevators, are incorporated in scheduling a group of elevators. When solving the optimization problem of EGCS, a number of dynamic performance indices, including average waiting time, average journey time, energy consumption, etc., have to be taken into account. Until now, numerous optimization approaches have been utilized to solve the car-dispatching problem of vertical transportation. Among those methods, in this study, the authors concentrate on various metaheuristic techniques that were implemented to optimize the metrics of EGCS. While establishing a metaheuristic approach, all the previous authors recognized various factors and limitations, which ought to analyze to develop a new metaheuristic-based EGCS. Owing to this, EGCS implemented via metaheuristic techniques is summarized in this review study, together with the underlying contributions, fitness functions, computational time, and limitations. What is more, performance comparisons of different previously implemented metaheuristic approaches are depicted in this study. This research will not only assist to figure out optimal elevator group optimization algorithms, but also will shrink the technological gap by outlining a number of potential future research lines and methodologies.