Rule Prioritisation Heuristic in Shift Design of Airline Ground Crew
In various industries, the exigency of workforce shift scheduling and rostering demands immediate, innovative solutions. Many off-the-shelf scheduling products lack the flexibility to adapt to varied organizational requirements, resulting in widespread reliance on manual interventions. This re...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/44688/3/DSVA_Lee%20Kong%20Weng.pdf http://ir.unimas.my/id/eprint/44688/4/Thesis%20PhD_LeeKongWeng%20-%2024%20pages.pdf http://ir.unimas.my/id/eprint/44688/5/Thesis%20PhD_LeeKongWeng.ftext.pdf |
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| Summary: | In various industries, the exigency of workforce shift scheduling and rostering demands
immediate, innovative solutions. Many off-the-shelf scheduling products lack the flexibility
to adapt to varied organizational requirements, resulting in widespread reliance on manual
interventions. This research, rooted in a case study of a local airline at Kuala Lumpur
International Airport (KLIA), a major regional hub, endeavours to formulate an algorithm
addressing workforce shift scheduling and rostering complexities. The proposed algorithm
aims to ensure judicious allocation of operational tasks, emphasizing minimal workforce
utilization, reduced idleness, and heightened adaptability in employee schedules. Utilizing
an existing workforce prediction method and shift design criteria, the study determines the
minimum workforce required for task completion. Shifts are assigned based on this
minimum, employing heuristic and genetic algorithms. Subsequent optimization of potential
schedules culminates in the identification of the most efficacious ones. Empirical tests reveal
the proposed heuristic method's superiority over manual practices and genetic algorithms. It
not only minimizes shift idle time and aligns with optimal shift starting times but also
significantly reduces workforce requirements, streamlining the shift scheduling process,
irrespective of data size. This research contributes valuable insights to the advancement of
workforce management strategies, offering practical and innovative solutions within the
specified context. |
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