Call Centre Shift Schedule Optimisation using Local Search Heuristics

Abstract

Many call centre shift scheduling approaches focus on one call centre day when determining the number of agents to be assigned to each shift. However, this approach makes the assumption that shifts will be filled with the same agents everyday, and ignores the practicalities of an actual call centre like day-offs, which would require shift assignments over longer time horizons. Moreover, many of these shift scheduling approaches use the arrival rate and service rate as inputs. This presents an issue because it might be difficult to estimate these rates with confidence from the data, especially the arrival rate which fluctuates during the day. We present a local search heuristic approach of assigning shifts and day-offs to existing call centre agents using hill climbing, tabu search, and simulated annealing. This is achieved without increasing the staffing costs. Our methods use individual calls data directly, therefore removing the need to estimate the arrival rate, and minimising the need to estimate the service rate. The methods are applied to real-life data from a call centre and the results show improvements in the achieved service level and a significant drop in the number of abandoned calls.