Modeling and Analysis for Optimization Mode of City Bus Operator Scheduling Management

2014 ◽  
Vol 513-517 ◽  
pp. 3220-3223
Author(s):  
Yu Jie Liu

This paper proposes a bus route scheduling management model based on Ant Colony Optimization and traditional scheduling model. It optimizes the existing bus route scheduling management model according to Ant Colony algorithm, enhances the performance of both Ant Colony algorithm and the traditional scheduling model, and improves the optimal performance of the combining algorithm. The experiment results show that, the proposed algorithm can effectively deal with the bus route scheduling management, and the optimization result obtained is obviously better than the traditional algorithms. Furthermore, it solve the problems exist in the traditional algorithms, therefore has great application value.

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Weizhe Zhang ◽  
Boyu Song ◽  
Enci Bai

Heterogeneous multicore and multiprocessor systems have been widely used for wireless sensor information processing, but system energy consumption has become an increasingly important issue. To ensure the reliable and safe operation of sensor systems, the task scheduling success rate of heterogeneous platforms should be improved, and energy consumption should be reduced. This work establishes a trusted task scheduling model for wireless sensor networks, proposes an energy consumption model, and adopts the ant colony algorithm and bee colony algorithm for the task scheduling of a real-time sensor node. Experimental result shows that the genetic algorithm and ant colony algorithm can efficiently solve the energy consumption problem in the trusted task scheduling of a wireless sensor and that the performance of the bee colony algorithm is slightly inferior to that of the first two methods.


2020 ◽  
Vol 57 (1) ◽  
pp. 010603
Author(s):  
聂清彬 Nie Qingbin ◽  
潘峰 Pan Feng ◽  
吴嘉诚 Wu Jiacheng ◽  
曹耀钦 Cao Yaoqin

2010 ◽  
Vol 428-429 ◽  
pp. 394-397
Author(s):  
Xin Wu Li

Color management for liquid crystal display is one of the key techniques in the color image reproduction. A new color management model is presented based on overcoming flaws and limitations of current ways of liquid crystal display color management . First, the paper takes standard color target for experimental sample, and substitutes color blocks in color shade district for complete color space. Second, data collecting method is introduced and some data bases for deducing the model are created. Then, ant colony algorithm is corrected to speed up model’s convergence and a new model for liquid crystal display color management based on improved ant colony algorithm is deduced and analyzed. Finally the experimental results show that the model can improve color management accuracy of liquid crystal display and can be used in its color management practically.


2011 ◽  
Vol 121-126 ◽  
pp. 2021-2025 ◽  
Author(s):  
Jing Hua Zhao ◽  
Jie Lin

In the dynamic production environment of supply chain, based on information sharing among enterprises of supply chain, this paper designs an expert system aided multi-agent intelligent ant colony algorithm system to solve the production scheduling optimization model. Where ant colony is constructed with multi-agent and the order decomposition structure and constraint are expressed by expert system. And then it builds a system using JESS and JADE to confirm this algorithm applied in a mass customization supply chain scheduling model


2014 ◽  
Vol 644-650 ◽  
pp. 2338-2341
Author(s):  
Fang Geng Zhao ◽  
Xiao Yan Shi

In this paper, the vehicle scheduling model in pooling pallet distribution was established, and the genetic algorithm for the problem was researched. In the algorithm, the genetic algorithm and the idea of ant colony algorithm are integrated in the proposed two crossover operators, and the appropriate values of parameters were determined by experiments.


2011 ◽  
Vol 211-212 ◽  
pp. 918-924
Author(s):  
Xian Chun Zou ◽  
Yan Ma ◽  
Ning Song

This paper analyzes grid resources organization, elaborates on the fundamental principles of the Ant Colony Algorithm, and proposes a grid resource discovery method based on the Ant Colony Algorithm. We consider users request ontology as ants, take search resource as food, and food source is the node of search target. The process of ants to find food is similar to the process of discovery grid resources.


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