Application of the expert system to elevator group-supervisory control

Tsuji, Saburo; Amano, M.; Hikita, Shiro

doi:10.1109/caia.1989.49165

Cited by 17 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For overcoming the above difficulty, a number of applicable optimization approaches to the elevator dispatching problem have been proposed and applied to reduce the average journey time for all passengers. In literature, there are many studies for solving optimal elevator group control problems, such as knowledge-based systems [5][6][7]; fuzzy logic theory [8][9][10]; neural networks [11], hybrid evolutionary algorithm [1,2,12]. However, most of the studies are more focused on the transportation efficiency, i.e., minimum journey time.…”

Section: Introductionmentioning

confidence: 99%

GA Based Hybrid Fuzzy Rule Optimization Approach for Elevator Group Control System

Chen

Hsu

Lee

et al. 2013

AMM

View full text Add to dashboard Cite

Elevators are the essential transportation tools in high buildings so that Elevator Group Control System (EGCS) is developed to dynamically layout the schedule of elevators in a group. In this study, a fuzzy rules based intelligent elevator group control system has been proposed in which the structure of rules including the related parameters are generated optimally based on the traffic data so as to maximize service quality. In literature, the fuzzy related approaches have been applied in EGCS but the fuzzy rules were all pre-defined. However, how to create the most suitable fuzzy rule set in EGCS for dispatching elevators more efficiently and economically are never discussed in literature. The aim of the proposed approach is to minimize the average waiting time at peak hours as well as to minimize the power energy at off-peak hours by using the proposed fuzzy rule based ECGS. Moreover, there are many decision variables are considered in the GCGS to provide the most appropriate elevator assignment whenever any hall call is given. These variables include the number of elevators, traffic flow, direction, passenger preferences (for instance, department stores, hospitals, hotels, and office buildings), congestion and VIP priority floor, etc. In this study, a fuzzy rule based elevator-dispatching approach has been proposed for the EGCS in which the fuzzy rules and related parameters are derived optimally by using genetic algorithm based on the historical elevator transportation data. The experimental results show that the performance of the proposed approach is superior to these of traditional approaches in literatures.

show abstract

Section: Introductionmentioning

confidence: 99%

GA Based Hybrid Fuzzy Rule Optimization Approach for Elevator Group Control System

Chen

Hsu

Lee

et al. 2013

AMM

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

Analysis of energy consumption of elevator group supervisory control system based on genetic network programming

Mabu

Hirasawa

et al. 2011

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

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.

show abstract

“…Since that time, increases in processor speed and memory capacity have allowed for the implementation of increasingly complicated dispatching algorithms. Modern dispatching algorithms employ fuzzy logic [2], [14], expert systems [22], sophisticated rule-based and search-based strategies [3], [16], artificial intelligence with learning [13], dynamic programming [10], [15], and reinforcement learning [7], [17]. In general, different dispatching algorithms are used for the different traffic situations defined earlier.…”

mentioning

confidence: 99%

Optimal dispatching control for elevator systems during uppeak traffic

Pepyne

Cassandras

Proceedings of 35th IEEE Conference on Decision and Control

View full text Add to dashboard Cite

Abstract-In this paper we develop optimal dispatching controllers for elevator systems during uppeak traffic. An uppeak traffic period arises when the bulk of the passenger traffic is moving from the first floor up into the building (e.g., the start of a business day in an office building). The cars deliver the passengers and then return empty to the first floor to pick up more passengers. We show that the structure of the optimal dispatching policy minimizing the discounted or average passenger waiting time is a threshold-based policy. That is, the optimal policy is to dispatch an available car from the first floor when the number of passengers inside the car reaches or exceeds a threshold that depends on several factors including the passenger arrival rate, elevator performance capabilities, and the number of elevators available at the first floor. Since most elevator systems have sensors to determine the car locations and the number of passengers in each car, such a threshold policy is easily implemented. Our analysis is based on a Markov decision problem formulation with a batch service queueing model consisting of a single queue served by multiple finite-capacity bulk servers. We use dynamic programming techniques to obtain the structure of the optimal control policy and to derive some of its important properties. Several numerical examples are included to illustrate our results and to compare the optimal threshold policy to some known ad hoc approaches. Finally, since many transportation systems can be modeled as multiserver batch service queueing systems, we expect our results to be useful in controlling those systems as well.

show abstract

Application of the expert system to elevator group-supervisory control

Cited by 17 publications

References 0 publications

GA Based Hybrid Fuzzy Rule Optimization Approach for Elevator Group Control System

GA Based Hybrid Fuzzy Rule Optimization Approach for Elevator Group Control System

Analysis of energy consumption of elevator group supervisory control system based on genetic network programming

Optimal dispatching control for elevator systems during uppeak traffic

Contact Info

Product

Resources

About