scholarly journals An iterative algorithm for regret minimization in flexible demand scheduling problems

2021 ◽  
Author(s):  
Zihang Dong ◽  
David Angeli ◽  
Antonio Paola ◽  
Goran Strbac
Keyword(s):  
Iterative Algorithm ◽  
Scheduling Problems ◽  
Regret Minimization

Two-Operation Crane Scheduling Problems

1982 ◽  
Vol 14 (3) ◽  
pp. 147-155 ◽  
Author(s):  
R. W. Lieberman ◽  
I. B. Turksen
Keyword(s):  
Scheduling Problems ◽  
Crane Scheduling

Application of Column Generation for Train-set Scheduling Problems with Regular Maintenance Constraints

2012 ◽  
Vol 132 (1) ◽  
pp. 151-159
Author(s):  
Akiyoshi Ohno ◽  
Tatsushi Nishi ◽  
Masahiro Inuiguchi ◽  
Satoru Takahashi ◽  
Kenji Ueda
Keyword(s):  
Column Generation ◽  
Scheduling Problems

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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