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This study aims to investigate the perceptions of Iranian English teachers about their teaching efficiency through a specific practicum course, namely, language teaching methodology. Drawing on a marketing education perspective, the researchers sought to measure the service quality offered in universities by examining teachers’ satisfaction with the curriculum.

Using quantitative gap analysis, the study evaluated the knowledge and skills of teachers in their preservice education regarding the perceived importance of knowledge and skills required in practice. The study involved 120 English as a foreign language teachers holding a BA degree from two universities in Iran, each with two to four years of teaching experience in the private sector. Data were collected using a 40-item semantic differential Likert scale developed by the researchers. The scale incorporated two components that assessed teachers’ perceived importance of knowledge and skills and their preservice educational preparation.

The findings revealed that most participants were overprepared in various items. However, there were some gaps in content knowledge and skills. Reasons for these gaps mainly included having adequate experience of and/or familiarity with course content, having completed relevant courses beforehand and imitating role model teachers. Teachers’ underpreparation was attributed to their lack of preparation in some specific subjects due to limited skill, practice, motivation, experience and familiarity with that content area.

This study explores the perceived knowledge and skills of Iranian English teachers and provides empirical insights into higher education service quality and customer satisfaction.

The purpose of this paper is to solve large-scale many-to-many hub location-routing problem (MMHLRP) using variable neighborhood search (VNS). The MMHLRP is a combination of a single allocation hub location and traveling salesman problems that are known as one of the new fields in routing problems. MMHLRP is considered NP-hard since the two sub-problems are NP-hard. To date, only the Benders decomposition (BD) algorithm and the variable neighborhood particle swarm optimization (VNPSO) algorithm have been applied to solve the MMHLRP model with ten nodes and more (up to 300 nodes), respectively. In this research, the VNS method is suggested to solve large-scale MMHLRP (up to 1,000 nodes).

Generated MMHLRP sample tests in the previous work were considered and were added to them. In total, 35 sample tests of MMHLRP models between 10 and 1,000 nodes were applied. Three methods (BD, VNPSO and VNS algorithms) were run by a computer to solve the generated sample tests of MMHLRP. The maximum available time for solving the sample tests was 6 h. Accuracy (value of objective function solution) and speed (CPU time consumption) were considered as two major criteria for comparing the mentioned methods.

Based on the results, the VNS algorithm was more efficient than VNPSO for solving the MMHLRP sample tests with 10–440 nodes. It had many similarities with the exact BD algorithm with ten nodes. In large-scale MMHLRP (sample tests with more than 440 nodes (up to 1,000 nodes)), the previously suggested methods were disabled to solve the problem and the VNS was the only method for solving samples after 6 h.

The computational results indicated that the VNS algorithm has a notable efficiency in comparison to the rival algorithm (VNPSO) in order to solve large-scale MMHLRP. According to the computational results, in the situation that the problems were solved for 6 h using both VNS and VNPSO, VNS solved the problems with more accuracy and speed. Additionally, VNS can only solve large-scale MMHLRPs with more than 440 nodes (up to 1,000 nodes) during 6 h.