A DEMATEL and consensus based MCGDM approach for with multi-granularity hesitant fuzzy linguistic term set

Zhao, Mengke; Wu, Jian; Cao, Mingshuo; Yu, Zhaoyan

doi:10.3233/jifs-191805

Cited by 5 publications

(2 citation statements)

References 49 publications

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“…[1] One such proposed work is "Time Table Scheduling using Genetic Artificial Immune Network," which highlights the importance of scheduling in real-life situations such as personnel scheduling, production scheduling, and educational schedule scheduling. [2] Educational schedule scheduling can be particularly challenging due to the various constraints that must be met to achieve a feasible solution. [6] Although Genetic Algorithms (GAs) have been used with mixed success, this work proposes a solution using N Queen algorithmbased approach to solve the heavily constrained Education timetable problem.…”

Section: IImentioning

confidence: 99%

Automatic Timetable Generation

Uma¹,

Sharvesh²,

Pradeep³

et al. 2023

IJRASET

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Generating a timetable manually for educational institutions can be a tedious task for educators, as each grade has a predefined education scheme and faculty workload that needs to be taken into account. The challenge lies in preparing a timetable that does not overlap with the schedules of the faculty members and can efficiently utilize all available resources. To overcome these difficulties, our project proposes the use of algorithms such as Evolutionary Algorithm, Tabu Search, Simulated Annealing, and scatter search. The proposed system will take inputs such as grade-wise subjects, teachers, and workload, and generate a possible timetable for the working days of the week. The system will integrate these inputs to make optimal use of all resources while adhering to the predefined constraints. This will help overcome the constraints that appear in the manual timetable generation process, resulting in timetables for any number of courses and grades. The implementation of this proposed system will provide a dynamic solution with the best approach, making it easier for educationalists to manage and set timetables. It will eliminate the complexity of manually generating and managing timetables, providing an efficient and effective solution to this challenging task

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Section: IImentioning

confidence: 99%

Automatic Timetable Generation

Uma¹,

Sharvesh²,

Pradeep³

et al. 2023

IJRASET

View full text Add to dashboard Cite

show abstract

“…Not only are there ambiguities in natural semantic expression, but also many uncertainties and complexities in the decision-making problems [ 25 ]. To this end, many useful tools have been developed to model vague and uncertain information, including: fuzzy sets, FS [ 19 , 20 ], intuitionistic fuzzy sets, IFS [ 20 ], interval type-2 FN [ 26 ], IFSs [ 27 – 29 ], hesitant fuzzy linguistic term sets [ 30 , 31 ] etc.…”

Section: Introductionmentioning

confidence: 99%