Improved Ant Colony-Genetic Algorithm for Information Transmission Path Optimization in Remanufacturing Service System
Abstract The information transmission path optimization (ITPO) can often affect the efficiency and accuracy of remanufacturing service. However, there is a greater degree of uncertainty and complexity in information transmission of remanufacturing service system, which leads to a critical need for d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2018-12-01
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| Series: | Chinese Journal of Mechanical Engineering |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s10033-018-0311-9 |
| Summary: | Abstract The information transmission path optimization (ITPO) can often affect the efficiency and accuracy of remanufacturing service. However, there is a greater degree of uncertainty and complexity in information transmission of remanufacturing service system, which leads to a critical need for designing planning models to deal with this added uncertainty and complexity. In this paper, a three-dimensional (3D) model of remanufacturing service information network for information transmission is developed, which combines the physic coordinate and the transmitted properties of all the devices in the remanufacturing service system. In order to solve the basic ITPO in the 3D model, an improved 3D ant colony algorithm (Improved AC) was put forward. Moreover, to further improve the operation efficiency of the algorithm, an improved ant colony-genetic algorithm (AC-GA) that combines the improved AC and genetic algorithm was developed. In addition, by taking the transmission of remanufacturing service demand information of certain roller as example, the effectiveness of AC-GA algorithm was analyzed and compared with that of improved AC, and the results demonstrated that AC-GA algorithm was superior to AC algorithm in aspects of information transmission delay, information transmission cost, and rate of information loss. |
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| ISSN: | 1000-9345 2192-8258 |