Optimal scheduling model for hub airport taxi based on improved ant colony collaborative algorithm

2010 ◽  
Vol 30 (4) ◽  
pp. 1000-1003 ◽  
Author(s):  
Jian-li DING ◽  
Xiao-li LI ◽  
Quan-fu LI
Keyword(s):  
Optimal Scheduling ◽  
Ant Colony ◽  
Scheduling Model ◽  
Hub Airport

scholarly journals Multi-Objective Optimal Scheduling Model of Dynamic Control of Flood Limit Water Level for Cascade Reservoirs

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1836 ◽  
Author(s):  
Guanjun Liu ◽  
Hui Qin ◽  
Qin Shen ◽  
Rui Tian ◽  
Yongqi Liu
Keyword(s):  
Power Generation ◽  
Water Level ◽  
Flood Control ◽  
Dynamic Control ◽  
Optimal Scheduling ◽  
Cascade Reservoirs ◽  
Multi Objective ◽  
Scheduling Model ◽  
Extreme Risk ◽  
Risk Rate

Reservoirs play a significant role in water resources management and water resource allocation. Traditional flood limited water level (FLWL) of reservoirs is set as a fixed value which over-considers the reservoir flood control and limits the benefits of reservoirs to a certain extent. However, the dynamic control of the reservoir FLWL is an effective solution. It is a method to temporarily increase the water level of the reservoir during the flood season by using forecast information and discharge capacity, and it can both consider flood control and power generation during the flood season. Therefore, this paper focuses on multi-objective optimal scheduling of dynamic control of FLWL for cascade reservoirs based on multi-objective evolutionary algorithm to get the trade-off between flood control and power generation. A multi-objective optimal scheduling model of dynamic control of FLWL for cascade reservoirs which contains a new dynamic control method is developed, and the proposed model consists of an initialization module, a dynamic control programming module and an optimal scheduling module. In order to verify the effectiveness of the model, a cascade reservoir consisting of seven reservoirs in the Hanjiang Basin of China were selected as a case study. Twenty-four-hour runoff data series for three typical hydrological years were used in this model. At the same time, two extreme schemes were chosen for comparison from optimized scheduling schemes. The comparison result showed that the power generation can be increased by 9.17 × 108 kW·h (6.39%) at most, compared to the original design scheduling scheme, while the extreme risk rate also increased from 0.1% to 0.268%. In summary, experimental results show that the multi-objective optimal scheduling model established in this study can provide decision makers with a set of alternative feasible optimized scheduling schemes by considering the two objectives of maximizing power generation and minimizing extreme risk rate.

A Guided Ant Colony Optimization Algorithm for Conflict-free Routing Scheduling of AGVs Considering Waiting Time

2017 ◽  
Vol 6 (2) ◽  
pp. 69
Author(s):  
Li Junjun ◽  
Xu Bowei ◽  
YANG Yongsheng ◽  
Wu Huafeng
Keyword(s):  
Ant Colony Optimization ◽  
Waiting Time ◽  
Solution Method ◽  
Ant Colony ◽  
Automated Guided Vehicles ◽  
Transition Rule ◽  
Delivery Time ◽  
Ant Colony Optimization Algorithm ◽  
Scheduling Model ◽  
Handling Cost

<p>Efficient conflict-free routing scheduling of automated guided vehicles (AGVs) in automated logistic systems can improve delivery time, prevent delays, and decrease handling cost. Once potential conflicts present themselves on their road ahead, AGVs may wait for a while until the potential conflicts disappear besides altering their routes. Therefore, AGV conflict-free routing scheduling involves making routing and waiting time decisions simultaneously. This work constructs a conflict-free routing scheduling model for AGVs with consideration of waiting time. The process of the model is based on calculation of the travel time and conflict analysis at the links and nodes. A guided ant colony optimization (GACO) algorithm, in which ants are guided to avoid conflicts by adding a guidance factor to the state transition rule, is developed to solve the model. Simulations are conducted to validate the effectiveness of the model and the solution method.</p>

Cascade Reservoirs Optimal Scheduling Method Based on Chaotic Ant Colony Algorithm

2014 ◽  
Vol 919-921 ◽  
pp. 1230-1233
Author(s):  
Zhong Hao Zhu ◽  
Hong Min Gao
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Scheduling ◽  
Ant Colony ◽  
Effective Solution ◽  
Cascade Reservoirs ◽  
Cascade Hydropower Stations ◽  
Scheduling Method ◽  
Generation Capacity ◽  
Hydropower Stations

On the basis of the analysis of the basic information of the river basin reservoirs and application of chaotic ant colony algorithm, the medium-and long-term optimization operation model is established, which regards the maximum annual generation capacity of the cascade hydropower stations as the main purpose. The simulation result shows the algorithm improves the total annual power generation of the cascade reservoirs, and finally provides an effective solution to solve the cascade reservoirs optimization operation problem.

scholarly journals Research on joint optimal scheduling of air conditioning load and electric vehicle under demand response

2021 ◽  
Vol 257 ◽  
pp. 01058
Author(s):  
Haiyu Huang ◽  
Chunming Wang ◽  
Shaolian Xia ◽  
Huaqiang Xiong ◽  
Baofeng Jiang ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Demand Response ◽  
Air Conditioning ◽  
Power Grid ◽  
Peak Load ◽  
Optimal Scheduling ◽  
Peak Shaving ◽  
Energy Internet ◽  
Direct Load Control ◽  
Scheduling Model

As an important part of energy Internet carrier, demand side resources can participate in many interactions with power grid. In order to reduce the peak to valley load difference of power grid, from the perspective of tapping the combined peak shaving potential of air conditioning load and electric vehicles, guided by TOU price and direct load control, this paper proposes an optimal scheduling model with the minimum load difference and the maximum total revenue of users as the objective function. The results show that the joint optimal scheduling strategy can reduce the peak load and eliminate the “secondary peak load” caused by disorderly charging of electric vehicles.

scholarly journals Research on Double-Layer Optimal Scheduling Model of Integrated Energy Park Based on Non-Cooperative Game

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3164 ◽  
Author(s):  
Feifan Chen ◽  
Haifeng Liang ◽  
Yajing Gao ◽  
Yongchun Yang ◽  
Yuxuan Chen
Keyword(s):  
Energy Efficiency ◽  
Double Layer ◽  
Cooperative Game ◽  
Lower Layer ◽  
Cost Model ◽  
Energy System ◽  
Optimal Operation ◽  
Optimal Scheduling ◽  
Improve Energy Efficiency ◽  
Scheduling Model

As the realization form of integrated energy system, integrated energy park is the key research object in the field of energy. Actual integrated energy parks are often partitioned internally. In order to take into account the interests of each zone in the optimal scheduling of integrated energy parks, a double-layer optimal scheduling model of integrated energy parks based on non-cooperative game theory is proposed. First, according to the operation of the integrated energy park, the output and cost model of the park is established. Second, with the minimum daily cost as the upper layer objective and the highest energy efficiency of the cogeneration system as the lower layer objective, a double-layer optimal scheduling model is established. Then based on non-cooperative game, the optimal operation strategy of each zone is obtained through the game among all the zones. An integrated energy park is taken as an example, the results show that the proposed model can make zones adjust their operation strategies more reasonably, thus helping to reduce the cost of the park and improve energy efficiency.

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