Bolong He, Snezana Mitrovic-Minic, Len Garis, Pierre Robinson and Tamon Stephen
The Surrey (British Columbia, Canada) fire department has an annual cycle for hiring full-time firefighters. This paper optimizes the timing of the annual hiring period. A key…
Abstract
Purpose
The Surrey (British Columbia, Canada) fire department has an annual cycle for hiring full-time firefighters. This paper optimizes the timing of the annual hiring period. A key issue is handling workplace absences, which can be covered by overtime cost or full-time hires.
Design/methodology/approach
Short-term and long-term absences patterns are analyzed according to season and age cohorts of the firefighters. These are then used in both an explanatory and time series model to predict future absences. The hiring schedule is optimized based on these predictions and additional constraints.
Findings
The current practice fares well in the analysis. For the time period studied, moving to earlier hiring dates appears beneficial. This analysis is robust with respect to various assumptions.
Originality/value
This is a case study where analytic techniques and machine learning are applied to an organizational practice that is not commonly analyzed. In this case, the previous method was not much worse than the optimized solution. The techniques used are quite general and can be applied to various organizational decision problems.