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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 …