Advances in Operational Research in the Balkans

Pattern recognition problem in geometric state with solutions in the class of decision trees is discussed. In each node, the partition of the corresponding subsample of objects is performed using a linear function (hyperplane). In this paper, for the node of the decision tree we state the problem of the dichotomy of a set of classes into 2 subsets of classes for 2 different definitions of the distance function between such subsets. This problem is considered in relation to the projection of the initial sample on the direction connecting 2 most remote points. For any 2 variants of the partition of a set of classes, the concept of closeness is introduced on the basis of the distance between the corresponding binary tuples. For 4 different algorithms for partition of a set of classes, computational experiments are conducted for a series of 100 random sets. The results of computational experiments and the complexity of these algorithms are presented.

The aim of this paper is to propose a new approach for determining the set of the most critical system components. Importance measures, commonly used for this purpose, first rank each individual component and afterward form a set of the most critical components due to their ranking. In this paper, we propose a new approach based on optimization so the whole set of the most critical components could be determined simultaneously. By taking into account interdependence of components, sets of the most critical system components with different cardinalities does not have to share any of the components. The proposed approach uses optimization over minimal cut sets of the observed system. The greater the number of minimal cut sets in which a component appears, the greater is its importance. The problem of determination of the minimal number of components which appear in all minimal cut sets is considered and formulated as set covering problem. The optimization problem is solved using available optimization software and original heuristic algorithm. Experiments were performed on a group of benchmark fault trees, and the results are compared with the results obtained by commonly used importance measures.

Dynamic discrete inventory control models implemented in a spreadsheet can be used as a quite reliable and relatively simple tool for presenting static inventory models with a complex mathematical apparatus. These models can be easily implemented in real systems, e.g. companies. The discretization of the continuous infinite time horizon to more than one finite time period is a more natural manner of describing and analyzing inventory dynamics of real systems. In this manner the continuous time inventory model is interpreted as the discrete time inventory model. The objective of this research is to present a static time continuous multiproduct economic order quantity (EOQ) model with storage space constraints, as a combinatorial optimization problem in the corresponding dynamic discrete time system control process. The heuristics approach used for problem solving is based on examination and comparison of several search algorithms and presented throughout several numerical experiments. Furthermore, this paper describes spreadsheet error detection and debugging approach for the presented model. The approach is based on common and specific constrains of the dynamic discrete inventory control model developed in a spreadsheet environment. Preliminary experiments show the general applicability of the approach.

The class of fuzzy multi-objective linear optimization problems with fuzzy coefficients in the objective functions is addressed in this paper. We introduce a parametric approach that helps to compute the membership values of the extreme points in the fuzzy set solution to such problems. We analyze the efficiency of the feasible basic solutions to a parametric multi-objective linear programming problem through the optimality test in a related linear programming problem. The particular case of triangular fuzzy numbers is presented in detail, and the possible degeneracy of the basic feasible solutions is handled. This paper is a continuation of our work on special classes of fuzzy optimization problems. Previously single-objective (linear and linear fractional) optimization problems with fuzzy coefficients in the objective functions were successfully solved.

Water as a natural resource refers to the groundwater and surface water (lakes, rivers, etc.) in the environment. Lake resources are considered to be a renewable resource of the freshwater which is essential for life of humans, animals, and plants. The pollution of lake resources, caused not only by humans, restricts its function in the ecosystem, including use for human need. Human negative influence and lack of protection measures for lake resources as freshwater reservoirs can be overcome and solved using multiattribute methods. As pollution problems are very complex due to the many environment variables and many solution alternatives, researchers use more than one method as a support to undertake scientific sustained decisions which are based on economic, ecological, social, technological,… goals in order to obtain the best solutions. Short literature review presented in this paper points out two most commonly used methods for this type of problems—Promethee II and AHP method. Promethee II is used for comparing the alternatives pair-wise for each criterion, finding the strength of preferring one over the other and the main objective of using AHP is to identify the preferred alternative and also determine a ranking of the alternative when all the decision criteria are considered simultaneously. Methodological framework proposed in this paper is designed in order to rank alternatives for each criterion by combining these two methods. Qualitative character of the AHP is exceeded using Promethee II in the first stage of the analysis by cutting off the alternatives with negative net outranking flow. The benefit is direct consistency increase of the AHP. Numerical example for the lake Vrutci, the most important freshwater supplier for the Užice area in western Serbia, is given in the paper. Long-term impact of human factors caused eutrophication of the lake Vrutci and led to the exclusion of the lake from the water supply system. The solution for pollution reduction of the lake Vrutci is obtained by implementing the methodological framework and combined application of the two methods and the results are discussed.

The aim of the paper is to evaluate Forest Policy performance in the countries of Europe, using a multiple criteria analysis based on forestry sector data retrieved by Eurostat. Multiple Criteria Decision Analysis is strongly connected with Policy and decision-making, and it can mitigate the uncertainty of planning providing robust solutions, while it is broadly used for agri-environmental issues. The PROMETHEE method was used to provide a ranking of the European countries in their Forest Policy performance drawing on data covering all three sustainability pillars: economy, environment, and society. According to the findings, the Czech Republic has the best performance among the examined countries regarding Forest Policy implementation. Other countries that can be singled out for best practices are Germany, Slovakia, and Hungary. Greece, on the other hand, is ranked last and the evidence verifies that the economic crisis has seriously affected the country in multiple areas. The country rankings were more volatile in the middle places, while countries with high or low performance on Forest Policy implementation maintained a more stable position across the three scenarios. The forest area as an input significantly affects the final ranking of the European countries. In general, large areas available for wood supply lead to a lower country ranking in Forest Policy performance as in Sweden, Finland, and Spain; only Germany achieves a high ranking among countries with large areas available for wood supply. The findings provide an overview of the current situation relating to Forest Policy implementation among European countries and could be used by the European Union in a future framework of Common Forest Policy in the EU.

The Information and Communication Technology (ICT) sector provides a wide range of services for different actors according to their type of actions. In fact, adoption of ICT-enhanced policies is one of the top agendas for governments today in most developing countries. The European Union (EU) and its Member States have a long tradition in development cooperation. The objective of this research is to study the contribution of ICT in the development policy in EU and present the current status by ranking these policies. EU development policies are evaluated qualitatively according to their ICT dependence and complexity levels. The questions, used as criteria, have been retrieved from the official “ICT Implication Assessment method of EU Legislation.” Following the multicriteria method of PROMETHEE II is applied for the total ranking in order to identify the “superior” ones, which present high dependence on and complexity of the ICT implications.

This paper presents a new fuzzy linear programming approach to solve bi-level multi-objective linear programming problems. First, we solve all the linear programming models on a given set of constraints. After that, we determine membership functions of the objective functions and of the decision variables at the first level. Later, we determine weights for all the membership functions, and form a fuzzy linear programming model. The solution of the model should be the best one for all decision-makers on both levels. To demonstrate the efficiency of the proposed approach, we solve a business planning problem and compare the obtained results with the ones obtained using fuzzy goal programming methodology.

The purpose of the paper is to determine the most influential factors related to cities/towns attractiveness and to compare respondents’ perception and real efficiency of certain cities. Empirical study is designed to evaluate residential attractiveness of the cities in Republic of Serbia as cities’ relative efficiency from one hand and citizens’ perception and preferences regarding living conditions from the other hand. For the purpose of attractiveness assessment, two input–output scenarios are created: financial and mixed financial health efficiency scenario. Both scenarios are evaluated using suitable Data Envelopment Analysis models which resulted in comparison of cities/towns attractiveness and determination of the most influential factors. This analysis was extended by a survey of the residents’ perceptions as well as their preference through conjoint analysis. The hypothesis was if the city was assessed as efficient, it does not automatically mean that it is perceived as attractive. Namely, sometimes ‘image’ of the particular city is more important than its quantified efficiency. Comparative analysis of results of both methods proves the aforementioned hypothesis in the case of the 15 regional centres in Serbia. The findings of this study could be used as directions for the public demography policymakers.

As one of the world’s largest and growing economic sectors, travel and tourism significantly contributes to GDP, creates jobs, drives exports, and generates prosperity across the world. Therefore, it is essential to know which countries successfully manage their travel and tourism, and can serve as an example for the others. The aim of the paper is to analyze the efficiency of travel and tourism impact on the GDP and employment in the European Union at the macro level, by using the nonparametric approach data envelopment analysis. All 28 member states of the European Union were included in the research. The observation period was one year (2017). Two inputs and two outputs were selected. Internal travel and tourism consumption and capital investment were the inputs, while travel and tourism’s total contribution to GDP and employment were the outputs. The obtained results are presented, interpreted and there are recommendations given for the tourism policy-makers regarding making better decisions.

This paper investigates the problem of sending packets through network on the interdomain level under condition that required Quality of Service (QoS) is achieved on the end-to-end (E2E) path. Process of sending and routing packets by one of the packet dispersion strategies is modelled using Coloured Petri Nets (CPN). The PN model was simulated to find and capture disjoint routes which ensure that realized values of network performance metrics meet the required ones on the E2E level. Using Data Envelopment Analysis (DEA), network performance is evaluated (packet delay, jitter, packet loss rate) to find which of the previously mentioned disjoint paths are more efficient than others. Based on DEA analysis results, Petri Net (PN) is expanded into stochastic PN in order to implement adaptive packet dispersion strategy. This strategy implies that paths with less probability of losing packets are more probable to be used in packet routing. Thanks to efficiency analysis, probability of selecting service classes by domain has been determined so that greater quality of VoIP service is achieved, which highly depends on offered network performance.

The amount of public procurement spending is growing globally and about one-third of public-sector spending is related to hospitals and healthcare institutions. Knowing that facts, it is a wise decision to strengthen the capacity and capabilities of purchasing public organizations to manage public procurements. Translating needs of public authorities, as well as final users of goods and services, into tender documentation is a complex task which needs multidisciplinary teams. One of the key problems in a public procurement is related to defining specific requirements for companies (bidders), award criteria and technical specification of quality of goods and services. Many purchasing public organizations consider public procurement processes as finished immediately after purchasing and forgot processes of quality surveillance. Dominant practice-public procurement with only one or dominant award criterion based on the lowest price might have as a result purchasing of low-quality goods and services. To prevent that, the purchasing organizations need to apply criteria in addition to or other than price and to describe the functions of the product or the desired outcomes rather than technical specifications. This paper has the aim to present usage of Quadratic Quality Loss Function (QQLF) for the analysis of quality of medical devices for the purpose of public procurement. The application of the QQLF concept can help public purchasing organizations to develop their ability to adequately address problems of quality in use and achieve values for the price. The average or expected quality losses which can be calculated by QQLF are a valuable argument in preventing lower quality products to be repurchased and can be added to other award criteria.

Maturity models define sets of levels or phases describing the development of observed object in a unique and hierarchically structured way. The existence of CMMI model leads to the development of other models and, consequently, to the development of maturity assessment systems. Maturity is attributed with a certain “maturity level” and relates to progressive improvement in performances. This paper discusses maturity assessment using the basic principles of PERT method, i.e., optimistic, pessimistic and most likely maturity assessment of an organization, directly implying that IT, project, and organizational maturity components, as well as demographic characteristics, significantly influence performance management in IT projects on the territory of the Republic of Serbia. IT, project, and organizational performances’ assessment enables implementation of the maturity measurement system in order to create an appropriate project management knowledge framework. This paper presents theoretical and practical implications.

In case cardholders have more than one smart card for electronic payments, there is a problem of correlating smart cards and PINs. When a cardholder has more than two smart cards, it introduces additional confusion due to the storage of multiple smart cards and due to the need for memorizing PINs. The idea presented in this paper describes the process of electronical payment using the concept of multi-card smart card by authentication of PIN coded from biometric fingerprint. The software of biometric scanner integrated into the PIN PAD device checks a cardholder authentication, by using presented algorithm that codes the biometric fingerprint into the PIN. Thus, that encoded PIN is compared with the PIN embedded as an encrypted form in the chip of multi-card smart card or with the control value used in authorization center. Multi-card smart card has the integrated functional button that is controlled by user. If a user wants to make the electronic payment he/she needs to press this button once or a few times. This will activate the virtual card used for electronic payment. The implementation of this idea allows the user to manage the electronic payment by using only one multi-card smart card without entering the PIN. By using this model of electronic payment, a user gets the improved user experience. The idea of this paper is the continuation of previous author’s investigations in order to get the algorithm that will simplify the process of electronic payment as much as possible. The aim of this paper is to introduce the conceptual model of a simplified electronic payment system that meets Strong Customer Authentification, which is required by PSD2 regulations.

Most of the marketing expenditures in mobile advertising are conducted through real-time bidding (RTB) marketplaces, in which ad spaces of the sellers (publishers) are auctioned for the impression of the buyers’ (advertisers) mobile apps. One of the most popular pricing models in RTB marketplaces is cost-per-install (CPI). In a CPI campaign, publishers place mobile ads of the highest bidders in their mobile apps and are paid by advertisers only if the advertised app is installed by a user. CPI pricing model causes some publishers to conduct an infamous fraudulent activity, known as click spamming. A click spamming publisher executes clicks for lots of users who have not authentically made them. If one of these users hears about the advertised app organically (say, via TV commercial) afterwards and installs it, this install will be attributed to the click spamming publisher. In this study, we propose a novel multiple testing procedure which can identify click spamming activities using the data of click-to-install time (CTIT), the time difference between the click of a mobile app’s ad and the first launch of the app after the install. We statistically show that our procedure has a false-positive error rate of 5% in the worst case. Finally, we run an experiment with 30 publishers, half of which are fraudulent. According to the results of the experiment, all non-fraudulent publishers are correctly identified and 73% of the fraudulent publishers are successfully detected.

This paper provides an overview of research on ski lift transportation data, a still heavily underused resource in ski resorts. To the best of our knowledge, this is the first paper that provides an overview of the efforts done in analyzing ski lift transportation data with the goal to advance the decision-making process in ski resorts. The paper is separated into three major research directions, the first being the clustering of ski lift transportation data. The second research direction is concerned with the exploitation of ski lift transportation data for ski injury research and prevention. The third research direction is concerned with congestion analysis in ski resorts. We provide directions for future research in the conclusion.

Recommender systems help users to reduce the amount of time they spend to find the items they are interested in. One of the most successful approaches is collaborative filtering. The main feature of a recommender system is its ability to predict user’s interests by analyzing the behavior of this particular user and/or the behavior of other similar users to generate personalized recommendations. Identification of neighbor users who have had similar taste to the target user in the past is a crucial process for successful application of collaborative filtering. In this paper, we proposed a collaborative filtering method that uses interpolative Boolean algebra for calculation of similarity between users. In order to analyze the effectiveness of the proposed approach we used three common datasets: MovieLens 100K, MovieLens 1M, and CiaoDVD. We compared a collaborative filtering based on IBA similarity measure with two standard similarity measures: Pearson correlation and cosine-based coefficient. Even though statistical measures are traditionally used in recommender systems, proposed logic-based approach showed promising results on the tested datasets. A recommender system with IBA similarity measure outperformed the others in most cases.