Designing a model for selecting, ranking and optimising service quality indicators using meta-heuristic algorithms

The purpose of this study is to select and rank the indicators affecting service quality and minimise the service quality gap. In this regards, two famous methods of meta-heuristic algorithms, one genetic algorithm and the other particle swarm optimisation, and their combination with support vector machine, namely 'GA-SVM and PSO-SVM' are used. Also, two macro quality indicators, including five performance indicators and five service quality gap indicators from the SERVQUAL model are considered. GA-SVM algorithm has been used to select the effective indicators in service quality and PSO-SVM has been implemented to rank these indicators. The efficiency and accuracy of the presented approach were confirmed through implementation on a manufacturing company. According to the obtained data, the two performance indicators of the final time of service level and the level of response do not play an important role in measuring and improving the quality of services provided in the company.