This paper aims to propose a quality assessment model for higher education institutions in the technical–technological field and a system for decision support and optimal management strategies for quality improvement. Obtaining research results is based on surveying stakeholders in higher education and obtaining quantitative data regarding key performance indices. Quantitative data and the genetic algorithm method are applied to determine optimal management strategies for quality improvement. Quality in the higher education sector is among the current issues in the academic community. By monitoring and researching the higher education field and analysing the literature and the current situation in the system of higher education in developing countries, it can be concluded that there is no single way to assess the quality of higher education institutions. This knowledge was a good starting point for the research presented in this paper. Accordingly, the findings include developing a system for quality assessment and the ranking of higher education institutions. Additionally, evaluating the relevance of key performance indicators of higher education institutions differs from different stakeholder perspectives. However, it is possible to develop a system for decision support and the selection of the optimal strategy for improving the performance of study programs and higher education institutions with regard to quality. The practical implications include defining a decision support system that enables the adoption of optimal decisions by the management teams of higher education institutions to improve study programs and the performance of the higher education institutions. The presented system may enable the benchmarking, simulation, and verification of different scenarios for improving the quality and performance of higher education institutions. In this paper, the authors analysed the characteristics, benefits, and drawbacks of different ranking systems to develop and introduce a novel ranking system that suggests weights for the ranking criteria and different perspectives regarding new digital age requirements. The model was tested, and the results are presented to demonstrate the advantages of the developed model. The originality of the research lies in the presented novel model that can be made available to government institutions and serve as a basis for the overall ranking and evaluation of higher education institutions, with the possibility of developing a performance-based funding system. Additionally, other stakeholders can gain an insight into the performance of an institution in relation to their needs and goals.

End-of-life vehicle (ELV) recycling is a process that spends energy and could be an energy source as well. This part of energy recovering depends on many different factors related to the broad and local aspects of ELV recycling. The ELV recycling process is consuming energy from different energy sources (electrical, fossil), however, this consumption is lower in relation to energy consumption during the production of new vehicle parts from the very beginning. This article attempts to promote an integrated approach in the analysis of the problem of energy recovery through ELV recycling. Authors aim to analyze the ELV recycling process as an energy generator and to present possibilities for its energy recovery. The research analyses are based on the empirical investigation of ELV recycling in the Republic of Serbia, as a developing country, and on defined statistical model presenting the impact of ELV recycling on energy generation, spending, and conservation during one-year intervals. Research results showed that the higher ELV generation rates may led to a higher energy recovery, and environmental and socio-economic sustainability.

The problem of sustainable business excellence has been investigated according to different aspects in different fields. For a more detailed analysis, it is necessary to develop an integrative sustainable business excellence model, respecting the baseline model of business excellence according to the European Foundation of Quality Management, with the possibility of defining the relationships and levels of significance of different variables. The primary goal of this paper is to present a developed integrative model to simulate the effects of information and communication technologies, and quality and effects of leadership improvement, as independent variables, on sustainable business excellence in higher education institutions, as a dependent variable. The model was developed by applying dynamic system techniques and evaluated by using statistical methods. In the sample of 17 Serbian and 21 Russian universities, the authors analyzed the information and communication support, leadership and quality, and their impact on sustainable business excellence. According to the simulation analysis, it was concluded that the EFQM model of self-assessment could help produce a more customer-oriented culture in HEIs, through ICT support, leadership and quality criteria enhancements.

The business environment is rapidly changing and puts pressure on enterprises to find effective ways to survive and develop. Since it is almost impossible to identify the multitude of complex conditions and business risks, an organization has to build its resilience in order to be able to overcome issues and achieve long term sustainability. This paper contributes by establishing a two-step model for assessment and enhancement of organizational resilience potential oriented towards Small and Medium Enterprises (SMEs) in the process industry. Using a dynamic modelling technique and statistical tools, a sample of 120 SMEs in Serbia has been developed as a testing base, and one randomly selected enterprise was used for model testing and verification. Uncertainties regarding the relative importance of organizational resilience potential factors (ORPFs) and their value at each level of business are described by pre-defined linguistic expressions. The calculation of the relative importance of ORPFs for each business level is stated as a fuzzy group decision making problem. First, the weighted ORPFs’ values and resilience potential at each business level are determined. In the second step, near optimal enhancement of ORPFs’ values is achieved by applying a genetic algorithm (GA).

In order to adapt to ever-changing customer needs and satisfy them, good Business Process Management (BPM) in Small and Medium-sized Enterprises (SMEs) is crucial. The target group of this research is production SMEs whose BPM can be monitored respecting the values of key performance indicators (KPIs). This paper shows how improving the performance of the observed business processes can improve the level of customer satisfaction. This improvement should lead to the sustainability of SMEs in the market. In this paper, evaluation of business processes performance is defined as a multi-criteria decision problem. The relative importance of considered KPIs and their imprecise values are described by linguistic expressions, which are then modeled by triangular intuitionistic fuzzy numbers (TIFNs). Calculation of KPI weights is done by using the fuzzy analytic hierarchy process (FAHP). Evaluation of BPM success is conducted respecting the obtained KPI weights and KPI values. An optimal solution for BPM success improvement, respecting customer satisfaction indicators, is calculated using the Artificial Neural Network (ANN) and Genetic Algorithm (GA) approaches. By applying the proposed model, managers of production SMEs can determine the management initiatives that will improve their business and the sustainability of their companies.