Agent-based simulation of a multi-queue emergency services call centre to evaluate resource allocation

Emergency services call centres operate to stringent parameters based on providing rapid support to their communities. It is this that differentiates the emergency service call centre from other call centre categories. The focus of this paper is to model the resource allocation within the NSW Police Assistance Line (NSWPAL), which is a multi-site, multi-queue, multiserver call centre that takes urgent and non-urgent calls from the community of New South Wales, Australia. The major resource for NSWPAL is its Customer Service Representatives (CSRs) who answer the calls and, although multi-skilled, may not be equally skilled across the queues. The business case for the model is simple. Staff costs are a major consideration for the call centre with unscheduled incoming calls on the different queues leading to additional staff costs. Although rostering of staff for the queues is based on historical forecasts, one of the challenges in running a multi-queue centre is to staff the queues to meet the unexpected demands, especially during emergencies. This leads to the shuffling of staff to meet the load and invariably affects the service level of one of the queues. One such event occurred in July 2007 during a normally busy period. The inbound call rate was such that, although all calls were answered, service level fluctuated and could not be maintained at the target level for a period of time. It is these situations that we aim to model to examine staffing policies and realtime management regimes. The literature suggests that traditional modelling techniques will not provide the flexibility required to model the call centre. In this paper we propose an agent-based model of the call centre, with the main agents being callers, call distribution manager, Team Leader, CSRs and the queues. The objective of the model is to improve the business through generating practices that lead to better customer service level metrics at reduced costs through more efficient resource allocation. The agent based model provides the flexibility to model the resource allocation without having to commit considerable computing resources and undertaking a rigorous mathematical approach. The model uses the extensive data base of the call centre to provide information for modelling the agents. In this paper we describe the model at the systems level and present the agent-based model we