title: Commercial diver selection using multiple-criteria decision-making methods.
abstract: BACKGROUND
  Personnel selection for different commercial diving jobs is time-consuming and subjective, This paper proposes a combination of two multiple-criteria decision-making (MCDM) methods to provide an objective tool for evaluation according to two main selection criteria: work experience and physical fitness.
  
  
  METHODS
  Subcriteria were computed using the analytic hierarchy process (AHP). By consulting two field professionals, subcriteria for work experience were determined as: working hours on the project type, hand tools, hydraulic tools, pneumatic tools, LP air jet and water lift/dredge, wet bell diving and paramedic training level. Determined by three medical experts, the subcriteria for physical fitness were: age, VO2Max, critical flicker fusion frequency (CFFF), psychomotor performance, and visual and hearing acuities. The pair-wise comparison matrices used to calculate subcriteria weights are filled by the same experts. Eight divers were included in the analysis. The AHP yielded scores of work experience for seven different project types and a physical fitness score for each diver. These scores were used in data envelopment analysis (DEA), to obtain an aggregate ranking of the divers.
  
  
  RESULTS
  The methodology was able to differentiate between qualified and unqualified divers. Divers were scored between 0 and 1 for each project type. The overall ranking of divers according to the average of the seven project types' scores was: 1. Diver 7 (1.0000); 2. Diver 5 (0.9486); 3, Diver 8 (0.9453); 4. Diver 2 (0.9421); 5. Diver 3 (0.8441); 6. Diver 4 (0.7804); 7. Diver 6 (0.6554); 8. Diver 1 (0.3931).
  
  
  CONCLUSIONS
  The proposed methodology allows decision-makers to perform evaluations objectively and systematically, reducing personal conflicts and confusions resulting from subjective immethodical judgments. This methodology is to be applied in real projects to validate the selection criteria and confirm the results.
authors.affiliation: Galatasaray University, Computer Engineering  Department, Istanbul, Turkey.
authors.forename: Tamer
authors.initials: T
authors.lastname: Ozyigit
authors.forename: S Murat
authors.initials: SM
authors.lastname: Egi
mesh_terms: D000328:Adult; D000465:Algorithms; D003657:Decision Making; D003661:Decision Support Techniques; D004242:Diving / Q000502:physiology / Q000592:standards*; D005425:Flicker Fusion; D006309:Hearing; D006801:Humans; D009790:Occupations; D010101:Oxygen Consumption; D010560:Personnel Selection / Q000379:methods*; D010809:Physical Fitness; D011597:Psychomotor Performance; D014792:Visual Acuity