Performance evaluation of services quality in higher education institutions using modified SERVQUAL approach with grey analytic hierarchy process (G-AHP) and multilevel grey evaluation

In today’s climate of fierce competition, there is a necessity to pay especial attention on customer demands either in manufacturing or service sector. Managers in service sector are under pressure in terms of environmental factors, they focus on customers’ satisfaction and this has led to the continuous improvement in the performance of service organizations. Meanwhile, customers’ expectations should be properly understood and measured. There have been various efforts to measure the quality of services using the SERVQUAL model. In this study, we try to investigate the concepts and factors influencing the quality of services according to modified SERVQUAL model and then utilize the proposed model of Grey Analytic Hierarchy Process (G-AHP) and Multilevel Grey Evaluation in order to evaluate the quality of services in the framework of Grey Systems Theory (GST). In order to propose our method, we will conduct a case study of the performance of service quality in higher education institutions of Isfahan-Iran

An intuitionistic fuzzy-grey superiority and inferiority ranking method for third-party reverse logistics provider selection

Organisations often outsource reverse logistics (RL) to third-party RL providers (3PRLPs) to focus on their primary business and reduce costs. The existence of multiple criteria available for choosing a 3PRLP, which are sometimes contradictory and yet related to each other, has led decision-makers to consider the development of multi-criteria decision-making models. The purpose of this study is to develop a hybrid model integrating the analytic network process (ANP) and the intuitionistic fuzzy-grey superiority and inferiority ranking (IFG-SIR) process to help an industrial production group select a 3PRLP. The ANP method is used to analyse the relationships among the different selection criteria and to obtain a weight indicating the relative importance of each criterion. The best 3PRLP is chosen using the IFG-SIR process. The classical SIR technique requires a sufficient amount of data while relying on the technique for order preference by similarity to ideal solutions and simple additive weighted methods. We use intuitionistic fuzzy sets to account for the subjectivity inherent to the potentially strategic opinions of the experts and of grey relation analysis to simplify the ranking process. We present a real-world case study to exhibit the applicability and demonstrate the efficacy of the proposed model

An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics

We consider the problem faced by a company that must outsource reverse logistics (RL) activities to third-party providers. Addressing RL outsourcing problems has become increasingly relevant issue in the management science and decision making literatures. The correct evaluation and ranking of the decision criteria/priorities determining the selection of the best third-party RL providers (3PRLPs) is essential for the competitive performance of the outsourcing company. The method proposed in this study allows to identify and classify these decision criteria. First, the relevant criteria and sub-criteria are identified using a SWOT analysis. Then, Intuitionistic Fuzzy AHP is used to evaluate the relative importance weights among the criteria and the corresponding sub-criteria. These relative weights are implemented in a novel extension of Mikhailov\u27s fuzzy preference programming method to produce local weights for all criteria and sub-criteria. Finally, these local weights are used to assign a global weight to each sub-criterion and create a ranking. We discuss the results obtained by applying the proposed model to a case study of a real company. In particular, these results show that the most important priority for the company when delegating RL activities to 3PRLPs is to focus on the core business, while reducing costs constitutes one of its least important priorities

A conceptual analytic network model for evaluating and selecting third-party reverse logistics providers

Although the success of forward logistics depends on the performance of reverse logistics, some manufacturing companies are not able to manage their reverse logistics effectively and thus delegate this important process to third-party reverse logistics providers (3PRLPs). In such cases, the decision to evaluate and select an appropriate 3PRLP becomes highly significant. In this paper, we use the analytic network process (ANP) and propose an analytical framework to systematically model the complex nature of interactions among the selection factors. In this model, the factors determining the evaluation of 3PRLPs are initially valued using Likert scale questionnaires. Then, a screening process is implemented using the average alternative method. Finally, the factors selected are structured in a network framework following the ANP. We present a case study to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms. The results have important managerial implications for production managers and illustrate that, in our case study, quality is the most important factor when selecting a 3PRLP

Fuzzy modeling of a piezoelectric actuator

In this research, a piezoelectric actuator was modeled using fuzzy subtractive clustering and neuro-fuzzy networks. In the literature, the use of various modeling techniques (excluding techniques used in this article) and different arrangements of inputs in black box modeling of piezoelectric actuators for the purpose of displacement prediction has been reported. Nowadays, universal approximators are available with proven ability in system modeling; hence, the modeling technique is no longer such a critical issue. Appropriate selection of the inputs to the model is, however, still an unsolved problem, with an absence of comparative studies. While the extremum values of input voltage and/or displacement in each cycle of operation have been used in black box modeling inspired by classical phenomenological methods, some researchers have ignored them. This article focuses on addressing this matter. Despite the fact that classical artificial neural networks, the most popular black box modeling tools, provide no visibility of the internal operation, neuro-fuzzy networks can be converted to fuzzy models. Fuzzy models comprise of fuzzy rules which are formed by a number of fuzzy or linguistic values, and this lets the researcher understand the role of each input in the model in comparison with other inputs, particularly, if fuzzy values (sets) have been selected through subtractive clustering. This unique advantage was employed in this research together with consideration of a few critical but subtle points in model verification which are usually overlooked in black box modeling of piezoelectric actuators.Morteza Mohammadzaheri, Steven Grainger and Mohsen Bazghale