Rostering staff at a mathematics support service using a finite-source queueing model

We study the problem of staffing university mathematics support services (MSSs) in which students drop in to the service (without appointment) for tutoring support. Our approach seeks to find the minimum sufficient number of tutors (with appropriate specialities) to present by hour and day to cover student demand with tolerable delays. We employ traditional operational research techniques to aid managers and administrators of MSSs to roster their services. The machine interference type queue is adopted to model the number of student queries within a mathematics support session. We define and solve an appropriate integer program to roster the number of tutors needed to run the service efficiently

Time-dependent stochastic methods for managing and scheduling Emergency Medical Services

Emergency Medical Services (EMS) are facing increasing pressures in many nations given that demands on the service are rising. This article focuses in particular on the operations of the Welsh Ambulance Service Trust (WAST), which is the only organisation that provides urgent paramedical care services on a day-to-day basis across the whole of Wales. In response to WAST’s aspiration to improve the quality of care it provides, this research investigates several interrelated advanced statistical and operational research (OR) methods, culminating in a suite of decision support tools to aid WAST with capacity planning issues. The developed techniques are integrated in a master workforce capacity planning tool that may be independently operated by WAST planners. By means of incorporating methods that seek to simultaneously better predict future demands, recommend minimum staffing requirements and generate low-cost rosters, the tool ultimately provides planners with an analytical base to effectively deploy resources. Whilst the tool is primarily developed for WAST, the generic nature of the methods considered means they could equally be applied to any service subject to demand that is of an urgent nature, cannot be backlogged, is heavily time-dependent and highly variabl

Predicting ambulance demand using singular spectrum analysis

This paper demonstrates techniques to generate accurate predictions of demand exerted upon the Emergency Medical Services (EMS) using data provided by the Welsh Ambulance Service Trust (WAST). The aim is to explore new methods to produce accurate forecasts that can be subsequently embedded into current OR methodologies to optimise resource allocation of vehicles and staff, and allow rapid response to potentially life-threatening emergencies. Our analysis explores a relatively new non-parametric technique for time series analysis known as Singular Spectrum Analysis (SSA). We explain the theory of SSA and evaluate the performance of this approach by comparing the results with those produced by conventional time series methods. We show that in addition to being more flexible in approach, SSA produces superior longer-term forecasts (which are especially helpful for EMS planning), and comparable shorter-term forecasts to well established methods

Staffing a Mathematics Support Service

Abstract We study the problem of staffing university mathematics support services in which students drop in to the service (without appointment) for tutoring support. Our approach seeks to find the minimum sufficient number of tutors (with appropriate specialities) to present by hour and day to cover student demand with tolerable delays. We employ traditional operational research techniques to aid managers and administrators of mathematics support services to roster their services. The machine interference type queueing is adopted to model the number of student queries within a mathematics support session. We define and solve an appropriate integer program to roster the number of tutors needed to run the service efficiently