Research Question: This paper recommends the method for selecting the optimal ammunition caliber for the automatic rifle that will be used to equip Serbian Army units. Motivation/idea: The selection ...of adequate caliber does not only represent a challenge for the decision-makers in the military, but also provides information to the industry to adjust to given requests. In the current conditions, the purpose-built industry of the Republic of Serbia is faced with two key tasks that are completely correlated: meeting the needs of the Serbian Army for quality ammunition and being competitive on the market. Accordingly, the purpose of this paper is to develop a qualitative model based on the DEX method and DEXi software applied in the selection of caliber ammunition for automatic rifles for the needs of members of the Serbian Army. This paper connects for the first time the qualitative DEX method with a product of this industry. Data / Tools: In order to meet the requirements of the multi-criteria decision-making, we developed the DEX model to be used to solve the problem of making decisions about the selection of optimal caliber for rearming the Serbian Army, as well as its cost-effectiveness. The alternatives are calibers for automatic rifles 7.62 mm and 5.56 mm that are currently in use in the Serbian Army, as well as the new 6.5 mm caliber which has been announced by the expert authorities. We defined the selection of criteria functions from technical and logistic standpoints. Using the DEX method and DEXi software enables us to obtain independent recommendations by applying different criteria. Findings: Results of this research show that the caliber is a very important component when it comes to army’s armament. At the same time, this question poses a challenge for the sustainable development of the weapons industry. As the optimal caliber, among the offered, after processing the input data in the DEXi software, the authors propose a caliber of 5.56 mm. This caliber dominates in most criteria and as such, it represents the best choice. Contribution: This paper contributes to the creation of sustainable development policies on the national and regional levels and it helps the key decision-makers in the military make decisions. Results of such and similar research, and the fact that the international market has a growing need for ammunition of this caliber should be guidelines for the domestic weapons industry for future development and investment.
Traditionally, there was a common belief that the main role of successful organizations, especially in business, was to make profit. However, over the past decades, this belief proved to be wrong, ...since many organizations that had been making a lot of profit went bankrupt, while others persisted, and the main reasons were their adaptability and sustainability. In this research, we wanted to find out the indicators on the strategy level that leaders should take into account in order to make their organization sustainable in the most adaptive way. Our research was conducted in two phases. In the first phase, we handpicked 150 indicators and conducted a survey where decision makers were asked to rank them. Principal Component Analysis (PCA) and Varimax rotation were used on the chosen data to find if there was any correlation between the indicators and if they formed any groups. It was found that there were eight perspectives which described 69.38% of the entire sample. Therefore, we chose the indicators that best represented the perspectives. In the second phase, Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) was used to calculate if there were any cause–effect relationships between the indicators. The results show that in order to have a sustainable strategy, organizations need to consider eight perspectives, where the biggest weight belongs to the perspective of Learning. The perspective of Leadership is the second, and Finance perspective has the lowest weight. In conclusion, it is pointed out that the survival of organizations depends on their ability to learn, adapt to changes, and be more sustainable.
•We propose fair data envelopment analysis method.•Additional constraint controls disparate impact in the data envelopment analysis.•As a result, average efficiency scores for the privileged and ...unprivileged group are the same (or similar).•Efficiency scores of the decision-making units from the privileged group are reduced.•The results have shown that efficiency scores are fair regarding the disparate impact and Wilcoxon rank- sum test.
Achieving fairness in algorithmic decision-making tools is an issue constantly gaining in need and popularity. Today, unfair decisions made by such tools can even be subject to legal consequences. We propose a new constraint that integrates fairness into data envelopment analysis (DEA). This allows the calculation of relative efficiency scores of decision-making units (DMUs) with fairness included. The proposed fairness constraint restricts disparate impact to occur in efficiency scores, and enables the creation of a single data envelopment analysis for both privileged and unprivileged groups of DMUs simultaneously. We show that the proposed method - FairDEA - produces an interpretable model that was tested on a synthetic dataset and two real-world examples, namely the ranking between hybrid and conventional car designs, and the Latin American and Caribbean economies. We provide the interpretation of the FairDEA method by comparing it to the basic DEA and the balanced fairness and efficiency method (BFE DEA). Along with calculating the disparate impact of the model, we performed a Wilcoxon rank-sum test to inspect for fairness in rankings. The results show that the FairDEA method achieves similar efficiency scores as other methods, but without disparate impact. Statistical analysis indicates that the differences in ranking between the groups are not statistically different, which means that the ranking is fair. This method contributes both to the development of data envelopment analysis, and the inclusion of fairness in efficiency analysis.
Ranking is a prerequisite for making decisions, and therefore it is a very responsible and frequently applied activity. This study considers fairness issues in a multi-criteria decision-making (MCDM) ...method called VIKOR (in Serbian language—VIšekriterijumska optimizacija i KOmpromisno Rešenje, which means Multiple Criteria Optimization and Compromise Solution). The method is specific because of its original property to search for the first-ranked compromise solutions based on the parameter
v
. The VIKOR method was modified in this paper to rank all the alternatives and find compromise solutions for each rank. Then, the obtained ranks were used to satisfy fairness constraints (i.e., the desired level of disparate impact) by criteria weights optimization. We built three types of mathematical models depending on decision makers’ (DMs’) preferences regarding the definition of the compromise parameter
v
. Metaheuristic optimization algorithms were explored in order to minimize the differences in VIKOR ranking prior to and after optimization. The proposed postprocessing reranking approach ensures fair ranking (i.e., the ranking without discrimination). The conducted experiments involve three real-life datasets of different sizes, well-known in the literature. The comparisons of the results with popular fair ranking algorithms include a comparative examination of several rank-based metrics intended to measure accuracy and fairness that indicate a high-quality competence of the suggested approach. The most significant contributions include developing automated and adaptive optimization procedures with the possibility of further adjustments following DMs’ preferences and matching fairness metrics with traditional MCDM goals in a comprehensive full VIKOR ranking.
Gaussian conditional random fields (GCRF) are a well-known structured model for continuous outputs that uses multiple unstructured predictors to form its features and at the same time exploits ...dependence structure among outputs, which is provided by a similarity measure. In this paper, a Gaussian conditional random field model for structured binary classification (GCRFBC) is proposed. The model is applicable to classification problems with undirected graphs, intractable for standard classification CRFs. The model representation of GCRFBC is extended by latent variables which yield some appealing properties. Thanks to the GCRF latent structure, the model becomes tractable, efficient and open to improvements previously applied to GCRF regression models. In addition, the model allows for reduction of noise, that might appear if structures were defined directly between discrete outputs. Two different forms of the algorithm are presented: GCRFBCb (GCRGBC — Bayesian) and GCRFBCnb (GCRFBC — non-Bayesian). The extended method of local variational approximation of sigmoid function is used for solving empirical Bayes in Bayesian GCRFBCb variant, whereas MAP value of latent variables is the basis for learning and inference in the GCRFBCnb variant. The inference in GCRFBCb is solved by Newton–Cotes formulas for one-dimensional integration. Both models are evaluated on synthetic data and real-world data. We show that both models achieve better prediction performance than unstructured predictors. Furthermore, computational and memory complexity is evaluated. Advantages and disadvantages of the proposed GCRFBCb and GCRFBCnb are discussed in detail.
•Gaussian conditional random field model for structured classification is proposed.•Two different forms of the algorithm are presented Bayesian and non-Bayesian.•The extension of local variational approximation of sigmoid function is presented.•Both variants are evaluated on synthetic data and real-world data.
The use of robotic models with the main functionalities of real objects
together with the implementation of innovative technologies, augmented
reality (AR) in this case, is the focus of the paper. ...Therefore, the concept
of a simplified robotic model (SRM) is presented. This concept is important
because it is useful for achieving the goals of engineering projects, which
is especially justified prior to the construction of the real objects. It
improves presentation, development, and education capabilities that are
unavoidable segments of the project strategy. Additionally, it is possible to
transfer developed solutions to the final objects after certain
modifications. Multidisciplinary building of the unique SRM of the 3-axis
centrifuge for pilot training is described, where multi-attribute
decisionmaking is used to conduct some experiments. The application includes
the use of a physical model, built from LEGO elements, software for
controlling and monitoring the physical model, and an AR mobile app.
In this paper we identify skier groups in data from RFID ski lift gates entrances. The ski lift gates’ entrances are real-life data covering a 5-year period from the largest Serbian skiing resort ...with a 32,000 skier per hour ski lift capacity. We utilize three representative algorithms from three most widely used clustering algorithm families (representative-based, hierarchical, and density based) and produce 40 algorithm settings for clustering skiing groups. Ski pass sales data was used to validate the produced clustering models. It was assumed that persons who bought ski tickets together are more likely to ski together. AMI and ARI clustering validation measures are reported for each model. In addition, the applicability of the proposed models was evaluated for ski injury prevention. Each clustering model was tested on whether skiing in groups increases risk of injury. Hierarchical clustering algorithms showed to be very efficient in terms of finding the high-number-cluster structure (skiing groups) and for detecting models suitable for injury prevention. Most of the tested clustering algorithms models supported the hypothesis that skiing in groups increases risk of injury.
The article solves the problem of more efficient and economical training of combat crews on short range air defense systems. The training so far is based on the use of conventional means, which ...require engagement of a large number of people, expensive equipment, long-term planning and spending a lot of time and space. The use of unmanned aerial vehicles - drones, greatly saves all these resources. Our mathematical model, using the methods of multicriteria decision-making - Analytical Hierarchical Processes (AHP) and optimization - The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), determines the weighting coefficients and then ranks alternatives or, the ranking for drone selection, which would replace classical means for training and co- aching so meeting all parameters and characteristics of the training itself and the training equipment. The methods first prioritize the selection criteria, and then, based on their importance, concretize the solution among the offered alternatives.
Traffic congestion is, nowadays, one of the most important highway problems. Highway tolls with booth operators are one of the causes of traffic congestion on highways, especially in rush hour ...periods, or during seasonal holiday travels. The value of driver waiting time (needed to stop and pay the toll) and the cost of the toll booth operators can reach up to about one-third of the revenue. In this paper we propose a novel methodology for continuous-time optimal control of highway tolls by predicting the optimal number of active modules (booths) in toll stations. The proposed methodology is based on a combination of recurrent neural networks, queuing theory, and metaheuristics. We utilized several recurrent neural network architectures for predicting the average intensity of vehicle arrivals. Moreover, the prediction error of the first recurrent neural network was modelled by another one in order to provide confidence estimates, additional regularization, and robustness. The predicted intensity of vehicle arrival rates was used as an input of the queuing model, whereas differential evolution was applied to minimize the total cost (waiting and service costs) by determining the optimal number of active modules on a highway toll in continuous time. The developed methodology was experimentally tested on real data from highway E70 in the Republic of Serbia. The obtained results showed significantly better performance compared to the currently used toll station opening pattern. The solutions obtained by solving a system of differential equations of the queuing model were also validated by a simulation procedure.
•In this paper we proposed the methodology for unsolved problem of predicting the optimal number of active modules on toll stations.•Flexible modelling of the intensity function without using complicated simulation procedures.•The proposed methodology was applied on real-world data and demonstrate its effectiveness in practice.
•We merge reweighing and adversarial approaches for mitigating bias in machine learning models, while keeping the best from both.•The proposed method can provide interpretable information about ...fairness of individual instances.•We provide theoretical analysis of properties of adversarial re-weighting.•We explore several variants of instance weight estimation including probabilistic ones.•We evaluate the method on four different real-world datasets, compared to the state-of-the-art techniques, and provide qualitative analysis.
With growing awareness of societal impact of artificial intelligence, fairness has become an important aspect of machine learning algorithms. The issue is that human biases towards certain groups of population, defined by sensitive features like race and gender, are introduced to the training data through data collection and labeling. Two important directions of fairness ensuring research have focused on (i) instance weighting in order to decrease the impact of more biased instances and (ii) adversarial training in order to construct data representations informative of the target variable, but uninformative of the sensitive attributes. In this paper we propose a Fair Adversarial Instance Re-weighting (FAIR) method, which uses adversarial training to learn instance weighting function that ensures fair predictions. Merging the two paradigms, it inherits desirable properties from both interpretability of reweighting and end-to-end trainability of adversarial training. We propose four different variants of the method and, among other things, demonstrate how the method can be cast in a fully probabilistic framework. Additionally, theoretical analysis of FAIR models’ properties is provided. We compare FAIR models to ten other related and state-of-the-art models and demonstrate that FAIR is able to achieve a better trade-off between accuracy and unfairness. To the best of our knowledge, this is the first model that merges reweighting and adversarial approaches by means of a weighting function that can provide interpretable information about fairness of individual instances.