Simulation-Based Optimization: A Case Study for Airline's Cargo Service Call Center

Abstract

In this chapter, we introduce the basic concept of the simulation-based optimization and illustrate its usefulness and applicability for generating the manpower planning of airline's cargo service call center. Because of the continuous increase in oil prices, and combined with many other factors, the airline industry is currently facing new challenges to keep its customers satisfied. One of the most important drivers of the customer satisfaction is the customer service. The excellent customer service can give an airline company the edge over its competitors. Airline companies need to insure the appropriate level of staffing at their service call centers in order to maintain a high level of customer satisfaction with the appropriate level of the overall cost. With the high level of uncertainty in the customer demand and a number of complicated factors in the problem, it becomes necessary to apply the simulation-based optimization technique to help managers generate the efficient staffing policy for the airline's cargo service call center. In this work, the technique called reinforcement learning and Markov decision process are used to build and solve the mathematical model to determine the appropriate staffing policy at the airline's cargo service call center on the monthly basis. Simulation and optimization models are incorporated together so as to solve the overall problem. The results of the case study are thoroughly analyzed, discussed, and compared with the current staffing policies. All results illustrate the impressive performance of the recommended staffing policies over the current staffing policies.