Firefighter Staffing Including Temporary Absences and Wastage

Authors:
Fry Michael J.;Magazine Michael J.;Rao Uday S.
Affiliations:
College of Business, University of Cincinnati, Cincinnati, Ohio 45221;College of Business, University of Cincinnati, Cincinnati, Ohio 45221;College of Business, University of Cincinnati, Cincinnati, Ohio 45221
Venue:
Operations Research
Year:
2006

Citing 6
Cited 2

The army manpower long-range planning system

Operations Research
Production control in multistage systems with variable yield losses

Operations Research
Covering-location models for emergency situations that require multiple response units

Management Science
Reinventing Army Recruiting

Interfaces
Managing Learning and Turnover in Employee Staffing

Operations Research
ANNIVERSARY ARTICLE: Improving Emergency Responsiveness with Management Science

Management Science

Determining the number of new employees with learning, forgetting and variable wage with a Newsvendor model in pull systems

Journal of Intelligent Manufacturing
A decision support system for mean-variance analysis in multi-period inventory control

Decision Support Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We examine the problem of determining the annual staffing level that minimizes total expected costs for a fire department, subject to minimum service-level-based staffing requirements. We develop a quantitative model that allows for stochastic temporary absences, permanent wastage, and limited hiring opportunities, and takes into account the unique firefighter work schedule. Our model is reminiscent of traditional newsvendor-type inventory models, but where the uncertainty is dependent on the decision variable. We derive easily implementable optimal staffing policies for both the continuous and discrete cases. We provide numerical results comparing our optimal solution to several heuristic staffing policies commonly used in practice, which show that our optimal policy can provide significant savings. We perform sensitivity analysis to generate managerial insights. We discuss an application of our model and analysis to the Cincinnati Fire Department.