scholarly journals Quantitative Analysis and Performance Study of Ant Colony Optimization Models Applied to Multi-mode Resource Constraint Project Scheduling Problems

2017 ◽  
pp. 145-154
Author(s):  
Antonio Gonzalez-Pardo ◽  
Javier Del Ser ◽  
David Camacho
Keyword(s):  
Quantitative Analysis ◽  
Ant Colony Optimization ◽  
Project Scheduling ◽  
Resource Constraint ◽  
Ant Colony ◽  
Optimization Models ◽  
Scheduling Problems ◽  
Performance Study ◽  
And Performance ◽  
Multi Mode

scholarly journals Reengineering MANET Routing using Ant Colony Optimization: Modelling and Performance Study

2020 ◽  
Vol 176 (32) ◽  
pp. 38-45
Author(s):  
Bright Selorm ◽  
Michael Asante ◽  
Benjamin Hayfron-Acquah ◽  
Ebenezer K.
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Performance Study ◽  
Manet Routing ◽  
And Performance

Penerapan Algoritma ACO untuk Penjadwalan Kuliah Pengganti pada Perguruan Tinggi (Studi Kasus: Program Studi Informatika, Universitas Multimedia Nusantara)

2019 ◽  
Vol 9 (2) ◽  
pp. 79-85
Author(s):  
Indah Noviasari ◽  
Andre Rusli ◽  
Seng Hansun
Keyword(s):  
Ant Colony Optimization ◽  
Educational Institution ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Perceived Ease Of Use ◽  
Ant Colony Optimization Algorithm ◽  
Scheduling System ◽  
Course Scheduling

Students and scheduling are both essential parts in a higher educational institution. However, after schedules are arranged and students has agreed to them, there are some occasions that can occur beyond the control of the university or lecturer which require the courses to be cancelled and arranged for replacement course schedules. At Universitas Multimedia Nusantara, an agreement between lecturers and students manually every time to establish a replacement course. The agreement consists of a replacement date and time that will be registered to the division of BAAK UMN which then enter the new schedule to the system. In this study, Ant Colony Optimization algorithm is implemented for scheduling replacement courses to make it easier and less time consuming. The Ant Colony Optimization (ACO) algorithm is chosen because it is proven to be effective when implemented to many scheduling problems. Result shows that ACO could enhance the scheduling system in Universitas Multimedia Nusantara, which specifically tested on the Department of Informatics replacement course scheduling system. Furthermore, the newly built system has also been tested by several lecturers of Informatics UMN with a good level of perceived usefulness and perceived ease of use. Keywords—scheduling system, replacement course, Universitas Multimedia Nusantara, Ant Colony Optimization

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