Optimizing CSPC event scheduling using a genetic algorithm / (Record no. 29375)

MARC details
000 -LEADER
fixed length control field 02380nam a2200301 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field CSPC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250404083116.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250331s2023 ph a b 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency CSPC
Language of cataloging eng
Transcribing agency CSPC
Description conventions rda
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) UTH BSCS B328o 2023
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Bautista, Shaira F.
Relator term author
245 10 - TITLE STATEMENT
Title Optimizing CSPC event scheduling using a genetic algorithm /
Statement of responsibility, etc. Shaira F. Bautista, Kelsey Eunice T. Bega, and Jireh Arjie C. Polvoreza
300 ## - PHYSICAL DESCRIPTION
Extent 146 leaves
336 ## - CONTENT TYPE
Source rdacontent
Content type term text
337 ## - MEDIA TYPE
Source rdamedia
Media type term unmediated
338 ## - CARRIER TYPE
Source rdacarrier
Carrier type term volume
502 ## - DISSERTATION NOTE
Dissertation note Undergraduate Thesis (BSIT) - Information Technology
Name of granting institution Camarines Sur Polytechnic Colleges, 2023
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Include bibliographic references
520 3# - SUMMARY, ETC.
Summary, etc. College event scheduling poses a significant challenge due to the diverse nature of events in terms of size, style, and context. These differences often lead to disagreements and inefficiencies, creating disruptions for students and staff alike. This research aims to address this challenge by exploring the feasibility of using a Genetic Algorithm to optimize event scheduling at Camarines Sur Polytechnic Colleges. Data on various event details, such as the number of events, their durations, and the number of attendees, was collected and used to train the algorithm, which was implemented in Python. The performance of the Genetic Algorithm was evaluated by analyzing its space and time complexity under different population and generation sizes. The results indicated that a larger generation size improved scheduling optimization, resulting in reduced CPU utilization and conflicts. The prototype developed as part of this research automates the scheduling process, significantly reducing the need for manual work. Further research is recommended to adapt the algorithm for other types of events, ensuring it meets their distinct requirements. Overall, this study contributes to the efficient and effective scheduling of events in educational institutions.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Besa, Kelsey Eunice T.
Relator term author
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Polvoreza, Jireh Arjie C.
Relator term author
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Relator term adviser
Name Fortuno, Kaela Marie N.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Relator term chair, panelist
Name Oñate, Joseph Jessie S.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Relator term panelist
Name Onesa, Rosel O.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Relator term panelist
Name Ibo, Allan Jr., O.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Suppress in OPAC No
Koha item type Unpublished Materials
Classification part B328o
Item part BSCS
Call number prefix UTH
Call number suffix 2023
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Dewey Decimal Classification     Main Library Main Library Undergraduate Thesis Section 03/31/2025   UTH BSIT B328o 2023 006530UM 03/31/2025 1-1 03/31/2025 Unpublished Materials