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Keywords: Elevator Group Sem, energy consumption, Genetic Network Programming
Received 23 July 2009; Revised 14 February 2010
1. Introduction
Elevator Group Supervisory Control System (EGSCS) is a traffic system, which provides the transportation services for passengers in modern buildings. As the elevator systems include uncertainty due to the future arrival of the passengers, it difficult to model, analyze, and optimize the elevator group supervisory control system. Recently, artificial intelligence technology has been used in such complex systems. Genetic Network Programming(GNP), a graph-based evolutionary method extended from genetic algorithm and genetic programming, has been already applied to EGSCS. On the other hand, since energy consumption is becoming one of the greatest challenges in the society, it should be taken as one of the criteria of the elevator operations. The elevators with maximum energy efficiency are therefore required. In this paper, the GNP is used to solve EGSCS with energy consumption (EC). Moreover, the idle car assignment has been embedded in the proposed method. Finally, the simulations show that some factors should be introduced into GNP in order to deal with the higher EC in the light traffic of the elevator systems. 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Elevators have become one of the most important building services encountered in our daily life. They have been built throughout history, but the first modern passenger elevators were developed no more than about 150 years ago. The control system that manages several elevators systematically in order to make them operate efficiently depending on the traffic patterns is called Elevator Group Supervisory Control Systems (EGSCS) [1]. In elevator systems, the average waiting time (AWT) is the main criterion to obtain an efficient performance of the system, while other criteria are used such as the long waiting percentage (LWP). Meanwhile, the energy crisis is becoming the reality. Energy consumption (EC) of elevators in each office building was generally considered to be relatively small, but the aggregate of it is large. As it is said that elevators typically use 3–5% of the electricity in modern buildings [2], so a control is required not only to satisfy the conventional passenger demands, but also to ensure energy efficiency. Achieving those objectives is difficult for a number of reasons including the need to coordinate the cars, the constraints on the car movements, operation with incomplete state information, handling dynamic passenger traffics, and so on. Therefore, the dispatching control problem of the elevator systems is NP-hard because of the enormous size of the state space in the elevator systems [3]. In general, the control strategy must be flexible enough to meet various conditions.
The modern dispatching algorithms employ fuzzy logic [4], expert systems [5], sophisticated rule-based and search-based strategies [6], artificial intelligence with learning [7], dynamic programming [8], and reinforcement learning [9]. Also, evolutionary systems have shown the successful examples in order to maximize the efficiency of such complex systems. Genetic Algorithm (GA) [10] originally developed by Holland evolves strings and has been applied to many kinds of optimization problems. Genetic Programming (GP) [11] was devised later in order to expand the expression ability of GA by using tree structures. Since the past studies suggested that the expression ability in each evolutionary computation is potentially linked to the complexity of the applicable problems, a new evolutionary computation named Genetic Network Programming (GNP) [12,13,14] with directed graphs as its genes was proposed as an extension of GA and GP 10 years ago. In this paper, GNP is used to solve EGSCS with energy consumption, while the advantages of EGSCS with GNP have been studied [13,14] compared to the above other technologies. In addition, the systematic study of the elevator control problem is done by dividing the passenger traffic in to three different patterns such as regular traffic, up-peak traffic and down-peak traffic, and from light mode to heavy mode. Since there are some idle cars in the light traffic mode, the assignment of them to the optimal floor is also important for the controller. Therefore, the idle car assignment has been embedded in our proposed method. The purpose of this paper is to analyze the performances of the elevator system using the proposed method, especially in energy consumption. In addition, the relationship between the energy consumption and other performances is analyzed. A methods is also proposed to adjust the balance between EC and waiting time. It was found from simulation results that the proposed method can realize the feasible solution required. The paper is organized as follows. Sections 2 and 3 give an overview of EGSCS and GNP. Section 4 explains EGSCS with EC using GNP. Section 5 shows
Received 23 July 2009; Revised 14 February 2010
1. Introduction
Elevator Group Supervisory Control System (EGSCS) is a traffic system, which provides the transportation services for passengers in modern buildings. As the elevator systems include uncertainty due to the future arrival of the passengers, it difficult to model, analyze, and optimize the elevator group supervisory control system. Recently, artificial intelligence technology has been used in such complex systems. Genetic Network Programming(GNP), a graph-based evolutionary method extended from genetic algorithm and genetic programming, has been already applied to EGSCS. On the other hand, since energy consumption is becoming one of the greatest challenges in the society, it should be taken as one of the criteria of the elevator operations. The elevators with maximum energy efficiency are therefore required. In this paper, the GNP is used to solve EGSCS with energy consumption (EC). Moreover, the idle car assignment has been embedded in the proposed method. Finally, the simulations show that some factors should be introduced into GNP in order to deal with the higher EC in the light traffic of the elevator systems. 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Elevators have become one of the most important building services encountered in our daily life. They have been built throughout history, but the first modern passenger elevators were developed no more than about 150 years ago. The control system that manages several elevators systematically in order to make them operate efficiently depending on the traffic patterns is called Elevator Group Supervisory Control Systems (EGSCS) [1]. In elevator systems, the average waiting time (AWT) is the main criterion to obtain an efficient performance of the system, while other criteria are used such as the long waiting percentage (LWP). Meanwhile, the energy crisis is becoming the reality. Energy consumption (EC) of elevators in each office building was generally considered to be relatively small, but the aggregate of it is large. As it is said that elevators typically use 3–5% of the electricity in modern buildings [2], so a control is required not only to satisfy the conventional passenger demands, but also to ensure energy efficiency. Achieving those objectives is difficult for a number of reasons including the need to coordinate the cars, the constraints on the car movements, operation with incomplete state information, handling dynamic passenger traffics, and so on. Therefore, the dispatching control problem of the elevator systems is NP-hard because of the enormous size of the state space in the elevator systems [3]. In general, the control strategy must be flexible enough to meet various conditions.
The modern dispatching algorithms employ fuzzy logic [4], expert systems [5], sophisticated rule-based and search-based strategies [6], artificial intelligence with learning [7], dynamic programming [8], and reinforcement learning [9]. Also, evolutionary systems have shown the successful examples in order to maximize the efficiency of such complex systems. Genetic Algorithm (GA) [10] originally developed by Holland evolves strings and has been applied to many kinds of optimization problems. Genetic Programming (GP) [11] was devised later in order to expand the expression ability of GA by using tree structures. Since the past studies suggested that the expression ability in each evolutionary computation is potentially linked to the complexity of the applicable problems, a new evolutionary computation named Genetic Network Programming (GNP) [12,13,14] with directed graphs as its genes was proposed as an extension of GA and GP 10 years ago. In this paper, GNP is used to solve EGSCS with energy consumption, while the advantages of EGSCS with GNP have been studied [13,14] compared to the above other technologies. In addition, the systematic study of the elevator control problem is done by dividing the passenger traffic in to three different patterns such as regular traffic, up-peak traffic and down-peak traffic, and from light mode to heavy mode. Since there are some idle cars in the light traffic mode, the assignment of them to the optimal floor is also important for the controller. Therefore, the idle car assignment has been embedded in our proposed method. The purpose of this paper is to analyze the performances of the elevator system using the proposed method, especially in energy consumption. In addition, the relationship between the energy consumption and other performances is analyzed. A methods is also proposed to adjust the balance between EC and waiting time. It was found from simulation results that the proposed method can realize the feasible solution required. The paper is organized as follows. Sections 2 and 3 give an overview of EGSCS and GNP. Section 4 explains EGSCS with EC using GNP. Section 5 shows
