A multi-objective genetic algorithm designed for energy saving of the elevator system with complete information

In this paper, the energy saving problem is studied for the elevator system with complete information. “Complete information” in elevator system means all the information about passengers, cars and hall calls are available in scheduling. First, the energy consumption data of an elevator is analyzed and the energy consumption model is constructed. Then, a multi-objective genetic algorithm (MOGA) is developed for the elevator control. In this algorithm, the energy conservation and the acceptable levels of waiting time are considered simultaneously. In addition, a simulation platform is developed which can be used to demonstrate the scheduling process and the optimization result and derive the real-time data of energy and time consumption. Using this platform, a four-elevator and ten-floor building is constructed and the effectiveness of the new developed MOGA algorithm is tested. The results illustrate that, compared with the traditional Nearest Car (NC) group control method, the MOGA method can reduce the energy consumption by 23.6% averagely.