Ant Colony Optimization for single car scheduling of elevator systems with full information

Shen, Zong–Yang; Zhao, Qianchuan

doi:10.1109/iciea.2009.5138455

Cited by 4 publications

(4 citation statements)

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“…Moreover, we use our B&B method to evaluate the Ant Colony Optimization (ACO) method used in [16], in which the single car, full information problem is considered. The service of a passenger is decomposed into two tasks: the loading task and the unloading task.…”

Section: Methodsmentioning

confidence: 99%

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A Branch and Bound Method to the Continuous Time Model Elevator System with Full Information

Shen

Zhao

2008

SICE Journal of Control, Measurement, and System Integration

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A new Branch and Bound method is given for the scheduling of the group elevator system with full information. Full information means that not only the parameters of the elevator systems but also the arrival time, origins and destinations of all the passengers who are to be served are known beforehand. The performance obtained by solving the full information problem is the best performance that the elevator scheduling algorithm can achieve and then can be used to measure how good an elevator scheduling algorithm is. The method can handle the continuous time event and is based on the concept of "trip", which refers to the movement of the car without changing the direction and with at least one passenger being served.

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Section: Methodsmentioning

confidence: 99%

“…The upper bound is obtained by finding a feasible solution and the lower bound is obtained by the Lagrangian relaxation. In [16] ACO is applied to solve the full information single car elevator problem. In this paper we compare the upper bound in [8] and the result of [16] with our result and show how good their results are.…”

Section: Literature Reviewmentioning

confidence: 99%

A Branch and Bound Method to the Continuous Time Model Elevator System with Full Information

Shen

Zhao

2008

SICE Journal of Control, Measurement, and System Integration

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“…At present, the study mainly includes EGCS with destination registration [2], EGCS with advanced/full information [3][4][5]. EGCS with destination registration can get the information of destination floor as soon as the passenger arrives.…”

Section: Introductionmentioning

confidence: 99%

Energy-saving algorithm for elevator group control system with cameras

Wang

Shen

Wang

et al. 2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

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This paper presents an energy-saving algorithm for elevator group control system (EGCS) with cameras. The cameras installed at each floor provide the information of the number of passengers waiting for service. With processing of the camera information, the number of passengers corresponding to each hall call is estimated. Then the total number of passengers assigned to be served by each elevator can be calculated. By analyzing the impact of the number of passengers on the performance of EGCS, new dispatching rules are designed. With fusion of rules and optimization of parameters, an energy-saving algorithm for EGCS with cameras is proposed. In both cases that camera provides accurate and fuzzy information, the performance of the proposed algorithm in down-peak traffic mode is presented. Compared with the existing algorithm, the proposed algorithm can achieve about 20% energy-saving without degradation of service performance.

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“…The dispatching plan topology of the ant colony optimization focuses on the shortest feasible scheduling path that serves the desired request call with a small waiting time [20]. The dispatching mechanism is optimized based on the pheromone update logic or car-choosing probability technique [21]. The Ant colony algorithm searches an optimal solution by trying out different iterations for every possible distinct items in the system and maintains a global scheduling plan which automates the feasible plan every iteration and based on the feedback obtained in the system the optimized shortest path is identified.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Scheduling Algorithm for Real-Time Embedded Control Systems using Arduino Board

Thirunavukkarasu¹,

Krishna²

2017

KEG

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The time taken for the scheduling task in a control system to reduce the traffic within the system is one of significant field of research in modern era. There are different control systems that require time scheduling such as elevator control system, traffic control system and train control system. Currently, there are unique control logic strategies adopting scheduling algorithm that are implemented in real time systems like earliest deadline first and ant colony optimization. At the same time, the disadvantages possessed by them are the exponential dip in the performance ratio due to over loading. Despite of all the available resources there are many issues faced such as congestion in traffic networks due to non-adaptive scheduling algorithms, etc., which led to several misfortunes and danger for human life. Hence an improved algorithm that increases the efficiency of the system is required to validate the processing time and the deadlines. Our research is focused on validating a proposed idea of using Arduino microcontroller to implement the different scheduling tasks and validate the efficiency of the algorithm to optimize the results of the system. This take cares of assigning the critical paths which priorities the tasks and focuses on reducing the scheduling time. This rapidly increases the processing speed and efficiency of the algorithm. We plan to use the Arduino board which has an inbuilt error detection algorithm that helps in checking whether the time scheduling is done effectively. In the initial phase of the project we develop and fabricate the hardware design using CAD design software packages like Solid Works. This is later employed with suitable environmental interfaces like, sensors and microcontrollers that can work in an adaptable environment as per requirements to validate the scheduling algorithm. The scheduling algorithm can also be used for controlling the current flow and power storage which will contribute a lot in the power consumption aspect. Graphical data interpretation of various algorithms from the past literature is observed and few selected ones are to be implemented in the experimental set up that is built as an initial proof of concept. By analyzing the results from the simulations carried out using the Altera FPGA board with VHDL and Arduino it is clear that we obtain better results using the Arduino board. Finally, to have an extensive study on different intelligent control logics that are used in the above mentioned control systems, we use the prototyped miniature model of an elevator system and a train control system to validate the different disk scheduling approaches like First Come-First Serve (FCFS), Elevator (SCAN) and ant colonization to solve the discrete combinational optimization of the scheduling logic. Initial validation of the system focuses on the effectiveness of using the ant How to cite this article:

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Ant Colony Optimization for single car scheduling of elevator systems with full information

Cited by 4 publications

References 20 publications

A Branch and Bound Method to the Continuous Time Model Elevator System with Full Information

A Branch and Bound Method to the Continuous Time Model Elevator System with Full Information

Energy-saving algorithm for elevator group control system with cameras

Scheduling Algorithm for Real-Time Embedded Control Systems using Arduino Board

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