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 this paper we solve the problem of static portfolio allocation based on historical Value at Risk (VaR) by using genetic algorithm (GA). VaR is a predominantly used measure of risk of extreme quantiles in modern finance. For estimation of historical static portfolio VaR, calculation of time series of portfolio returns is required. To avoid daily recalculations of proportion of capital invested in portfolio assets, we introduce a novel set of weight parameters based on proportion of shares. Optimal portfolio allocation in the VaR context is computationally very complex since VaR is not a coherent risk metric while number of local optima increases exponentially with the number of securities. We presented two different single-objective and a multiobjective technique for generating mean-VaR efficient frontiers. Results document good risk/reward characteristics of solution portfolios while there is a trade-off between the ability to control diversity of solutions and computation time.

The problem of evaluation and selection of parking lots is a part of significant issues of public transport management in cities. As population expands as well as urban areas, solving the mentioned issues affects employees, security and safety of citizens, and quality of life in long‐time period. The aim of this paper is to propose a multicriteria decision model which includes both quantitative and qualitative criteria, which may be of either benefit or cost type, to evaluate locations. The criteria values and the importance of criteria are either precise or linguistic expressions defined by trapezoidal fuzzy numbers. The human judgments of the relative importance of evaluation criteria and uncertain criteria values are often vague and cannot be expressed by exact precise values. The ranking of locations with respect to all criteria and their weights is performed for various degrees of pessimistic‐optimistic index. The proposed model is tested through an illustrative example with real life data, where it shows the practical implications in public communal enterprises.

As the whole world is going mobile, application of mobile devices in education, also known as m-learning, is becoming one of the most popular areas of educational research. This paper presents the implementation and evaluation of the effectiveness and students’ attitudes toward an adaptive mobile learning system based on personalized content and mobile web. Personalization of learning materials is based on the Felder-Silverman learning style model and the features of the accessing mobile device were identified using the device library. The results of the study confirm students’ positive attitudes toward mobile learning and the developed adaptive m-learning system. They also prove the effectiveness of the system and m-learning as an additional educational tool in terms of increasing students’ knowledge and scores.

In modern manufacturing industries, supplier selection is increasingly recognized as a critical decision in supply chain management. Supplier selection problem is a typical multiple criteria decision making problem involving a number of different and usually conflicting objectives. Because of that, modern supplier selection techniques imply solving of multi-objective optimization problems. In this paper supplier selection using evolutionary algorithm (NSGA-II method) is presented. Recent investigations show that quality and costs are among the most important selection factors. In this paper the authors used variance of quality and total costs as criteria for selection optimization. Results show that described methodology can be applicable for the practical purposes.

The implementation of Information and Communication Technologies (ICT) in enterprises organized as part of a cluster has its own specifications according to the level and type of relations between enterprises in the cluster. The purpose of this paper is to define determinants for the quality of the ICT implementation in clusters, the research goal being to define and validate the quality models of the implementation of ICT in clusters. Based on the theoretical research of clusters, the quality model for the ICT implementation has been developed and tested to the influences of the determinants of the implementation of ICT, which refer to the level of investments in ICT, quality management, the quality of processes and the level of the implementation of the ICT strategy at the level of the quality of the ICT implementation, with the confirmation of the significance of the stated hypothesis. The results of the research indicate the fact that the level of the quality of the ICT implementation could be predicted with a high accuracy, as well as the influence of the performances of enterprises and the cluster as a whole.