The purpose of the journal is to present scientific ideas in the area of operational research (OR) that will contribute to theoretical aspect and practical applications of OR. The journal publishes only original scientific papers in that area. The scope of the journal covers the following topics: linear and non-linear programming, integer programing, combinatorial and discrete optimization, multi-objective programming, stohastic models and optimization, scheduling, macroeconomics, economic theory, game theory, statistics and econometrics, marketing and data analysis, information and decision support systems, banking, finance, insurance, environment, energy, health, neural networks and fuzzy systems, control theory, simulation, practical OR and applications. Journal publisher: Croatian Operational Research Society Co-publishers: University of Zagreb, Faculty of Economics & Business J. J. Strossmayer University of Osijek, Faculty of Economics in Osijek University of Split, Faculty of Economics, Business and Tourism J. J. Strossmayer University of Osijek, School of Applied Mathematics and Informatics University of Zagreb, Faculty of Organization and Informatics
Croatian Operational Research Society (CRORS)
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal - Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. - Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Inverse cost and revenue efficiency in network processes with uncontrollable inputs
Inverse data envelopment analysis (IDEA) estimates the inputs/outputs of each decision-making unit (DMU) based on the perturbations in the outputs/inputs while maintaining relative efficiency. When the cost of inputs or the price of outputs is available, it is possible to calculate cost, revenue, and profit efficiencies. This study develops an inverse network DEA that includes uncontrollable measures. For this purpose, models are presented for calculating relative, cost, and revenue efficiencies. Then, an algorithm is proposed to estimate the inputs of the first stage, considering unchanged technical and cost efficiencies. Also, an algorithm is presented to estimate the outputs of the second stage, considering unchanged output-oriented technical efficiency and revenue efficiency. Then, the introduced algorithms are applied to a numerical example and a dataset related to salmon farming, obtaining logical results. Finally, the proposed method is compared with one of the existing methods, and their differences are discussed.
Copyright (c) 2025 Croatian Operational Research Review
Aspects of blocking on time-varying tandem queueing network
Most real-world service systems are susceptible to queue capacity constraints, leading to a blockage of entities. Several blocking mechanisms can be implemented when capacity constraints influence the flow of entities through the system. This study investigates the comparative performance of blocking-after-service (BAS) and blocking-before-service (BBS) mechanisms by modelling a hospital emergency department with a finite capacity queue. The model is based on a two-station tandem network with finite capacity on the intermediate queue, and we developed transient performance measures for the system in both mechanisms. Using a numerical approach, we highlight how these mechanisms influence the time-varying number of patients and the virtual workload in the system. Our results demonstrate that the BAS mechanism slightly outperforms the BBS mechanism in reducing unwanted congestion.
Numerical methods for checking the stability of gyroscopic systems
Gyroscopic mechanical systems are modeled by the second-order differential equation \begin{equation*}\displaystyle M \ddot x(t) + G\dot x(t) + K x(t) = 0, \end{equation*} where \(M\in\mathbb{R}^{n\times n}\) is a symmetric and positive definite matrix, $G \in\mathbb{R}^{n\times n}$ is a skew-symmetric ($G^T=-G$) matrix, and $K\in\mathbb{R}^{n\times n}$ is a symmetric matrix, representing the mass, gyroscopic, and stiffness matrices, respectively. The stability of such systems, which is the primary topic of this paper, is determined by the properties of the associated quadratic eigenvalue problem (QEP)\begin{equation*} {\mathcal G}(\lambda)x=(\lambda^2M+\lambda G+K)x=0, \quad x\in\mathbb{C}^{n},\ x\not=0. \end{equation*} In this paper, we provide an overview of various linearizations of the QEP and propose numerical methods for checking the stability of gyroscopic systems based on solving the linearized problem. We present examples that demonstrate how the use of numerical methods provides a significantly larger stability region, which cannot be detected using the considered non-spectral criteria, or verify stability in cases where non-spectral criteria are not applicable, highlighting the advantages of numerical methods.
A profit maximization model for sustainable inventory under preservation technology and linear time-dependent holding cost
This paper presents sustainable inventory model with constant deterioration rate when holding cost is time dependent. Demand rate is price and stock dependent. Preservation technology and carbon emission are considered for more sustainable approach. Shortages are allowed and are fully backlogged. In addition, a mathematical model is constructed to maximize the total profit function and the concavity is shown using three-dimensional graph. A numerical experimentation is carried to compute the total profit and the order quantity. To validate the proposed model, the sensitivity analysis is conducted for the total profit function as well as the order quantity. Results and observations are also discussed along with managerial insights.
Measuring trade facilitation for the emerging seven countries (E7) using multi-criteria decision-making methods
Countries provide opportunities for traders not only in imports but also in export performance by simplification and harmonization of documents in order to facilitate certain policy areas that have a tremendous impact on trade volumes. The OECD has established various indicators at different levels and compared countries to measure the trade facilitation between countries. This study aims to compare the procedures applied by the emerging seven countries (E7- Brazil, China, India, Indonesia, Mexico, Russia, and Türkiye) using trade facilitation indicators (TFIs) and multi-criteria decision-making methods. We employ MEREC, WENSLO, ENTROPY, LOPCOW, CVM, CRITIC, ANGLE, and GINI methods to determine the weights of 11 TFIs identified by the OECD. The most suitable weighting method is established through multi-dimensional analysis. E7 countries are then evaluated using the MABAC and ARTASI methods, with a sensitivity analysis comparing results against 38 OECD member countries. In terms of performance across all TFIs, Russia ranks first (MABAC-0.253, ARTASI-1.821), Mexico second (MABAC-0.186, ARTASI-1.802), and Türkiye third (MABAC-0.117, ARTASI-1.731).
An application of an environmentally extended input-output model to the assessment of CO2 emissions of Croatian economic sectors
The adverse effects of pollution and the impacts of different productive sectors on the degradation of the environment have become important issues in economic development analysis. This study aims to estimate the overall carbon footprint of different economic sectors of the Croatian economy. Total CO2 emissions are decomposed by direct and indirect emissions of productive sectors and final demand by applying the environmentally extended input-output analysis (EEIO). EEIO approach enables the decomposition of the total CO2 emissions of specific sectors into emissions related to the production of final goods and services and the production of intermediate inputs delivered to other economic sectors. The highest level of direct emissions has been generated by sectors producing energy products and transportation. As the output of those sectors is required as intermediate inputs used by the other sectors, the EEIO model redistributes emissions to other sectors, delivering final products to domestic users and abroad. Besides transport and electricity, Croatia's highest level of total direct and indirect emissions is found in the construction sector, production of non-metallic mineral products and food industry.
Evaluating upskilling tools for digital, green, and entrepreneurial competences: A multicriteria approach to advancing Europe's green and digital transition
Advancing green and digital transition is a key topic on Europe’s agenda, aligned with the UN’s SDG goals. In this context, this study investigates existing support tools and programs for upskilling digital, green, and entrepreneurial (DGE) competences with the goal to select and reuse the best ones available within the project partner group (11 partners from 9 European countries). The data collection process started with the identification and thorough elaboration of 90 upskilling tools/programs, each differing in their contributions to DGE competences, and in other varying features. The collected database contains more than 30 qualitative and quantitative features per upskilling tool/program. An expert focus group then defined the tool selection criteria and their grading, highlighting the importance of learning outcomes recognition and the development of multiple DGE competence areas, in particular. After the grading process, alternative assessments were analysed using the PROMETHEE II method, resulting in a ranking of the 15 most useful DGE tools/programs. In addition to validating the framework set up for the MCDM that takes into account the complexities of (international) training needs of diverse target groups, the study demonstrated significant application potential. Namely, these tools, both the initial list of 90 and the selected 15, all having confirmed their effectiveness and success in previous projects addressing SDG 4 and SDG 9, are now promoted to (new) relevant stakeholders and prioritised in capacity-building actions in international settings. As such, the collective exercise based on strong scientific foundations had clear practical value.
A memetic NSGA-III for green flexible production with real-time energy costs and emissions
The use of renewable energies strengthens decarbonization strategies. To integrate volatile renewable sources, energy systems require grid expansion, storage capabilities, or flexible consumption. This study focuses on industries that adapt production to real-time energy markets, offering flexible consumption to the grid. Flexible production considers not only traditional goals like minimizing production time, but also minimizing energy costs and emissions, thereby enhancing the sustainability of businesses. However, existing research focuses on single goals, neglects the combination of makespan, energy costs, and emissions, or assumes constant or periodic tariffs instead of a dynamic energy market. We present a novel memetic NSGA-III to minimize makespan, energy cost, and emissions, integrating real energy market data, and allowing manufacturers to adapt energy consumption to current grid conditions. Evaluating it with benchmark instances from literature and real energy market data, we explore the trade-offs between objectives, showcasing potential savings in energy costs and emissions on estimated Pareto fronts.
On the closed-form solution of the monopolist long-run profit maximization problem with linear demand and Cobb-Douglas technology
This paper provides an economic and mathematical analysis of the long-run profit maximization problem of a monopolist with linear demand and a two-input Cobb-Douglas technology to derive the conditions that guarantee the solution to this problem. In addition, the conditions under which the closed-form solution can be derived and the conditions under which the monopolist's profit is positive are discussed. Whenever the problem has a unique solution, the closed-form defines the profit function well for the given demand function. The closed-form solution to the problem depends on the returns to scale. The problem has a unique solution for decreasing returns to scale, for which in general no closed-form can be found. On the other hand, the closed-form of the unique solution can easily be found for constant returns to scale. However, three sub-cases are identified for the case of increasing returns to scale. The analysis is supported by economic interpretations and numerical illustrations.
Going concern prediction – A horse race between traditional and regularization machine learning models
Regularization machine learning (ML) methods have been increasingly applied in accounting research, offering new possibilities in predictive modeling. Their forte lies in the effective regularization methods for resolving the biggest concern of generalization, which is the risk of overfitting the training data. While these sophisticated methods are known to outperform traditional regression approaches in large and balanced datasets, this may not be the case when facing imbalanced and small datasets. Moreover, model validation is also challenging in such settings because traditional performance measures, such as prediction accuracy, may be misleading. We address this problem by comparing two traditional and five regularization-based methods in predicting going concern uncertainty (GCU) on the sample of listed companies in Croatia. We take caution when evaluating the models due to class-imbalanced problems and include different classification performance measures, as well as calibration of the models to account for their uncertainty. As expected, no model performs best across all evaluation criteria, but regularization methods are better calibrated. Given our results, we suggest that model selection should consider the results of the model calibration, a combination of different performance metrics, and the economic impact of the statistical performance of the model, if feasible.
Calculation of ecological land-footprint - based on the input-output model and focusing on the imported commodities
The ecological footprint has been a crucial ecological indicator for more than two decades, and the methodology for calculating it has developed significantly over the years. However, some issues and shortcomings still need to be addressed and specified further. This paper focuses on the embedded land requirements of imported commodities in input-output modelling approaches. We propose a refined model to overcome the shortcomings of two former models. Our model quantifies the embedded ecological land-footprint of imported commodities and their allocation between direct final consumption and production. In addition, we allocate the latter again among final consumption and exports using the framework of linear algebra and matrix arithmetic. We also propose ways of extending the model to overcome the general but misleading assumption in the literature that imported commodities have an equal per unit ecological footprint to domestic products, an approach that is based on the idea that trading partners have the same technological background.
Non-cooperative inventory games for defective items and quantity discounts using strategic complementarity
In this paper, the mathematical analysis for obtaining the equilibrium of the inventory games under strategic complementarities, the existence of defective items, and quantity discounts have been analyzed. The inventory system consists of many buyers who order a single type of product from one supplier. They compete with each other as a player in a non-cooperative game with strategic complementary. They maximize a supermodular payoff function and take into account some fraction of defective items from a lot of the arrival products. The concept of supermodular games is used to obtain the equilibria of these problems. A new existence theorem of Nash equilibrium in a specific condition has been proved. The optimum analysis has been justified for two conditions, that is the condition without discount and another without it. The numerical computations are provided using Python programming. At the end, the numerical result shows that elements of the Nash equilibrium set can be altered when discounts are considered. A quantity discount policy can be used by the supplier to prevent players from choosing the least Nash equilibrium.
TOPSIS-based framework for evaluating employee cybersecurity risk
This paper presents the development and initial validation of a TOPSIS-based framework for evaluating employee cybersecurity risk (TFEECR). The framework offers a structured and objective approach to assessing multiple dimensions of employee behavior and attitudes toward cybersecurity. It integrates the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) with Saaty’s criteria weighting, enabling a comprehensive evaluation across five criteria: knowledge, compliance, risky behaviors, attitudes, and training. The framework is validated using simulated data, and it showed robustness in differentiating between high- and low-risk employees. Such results are useful for organizations that want to implement targeted interventions. A sensitivity analysis highlights the framework's adaptability to various settings. This conceptual framework lays the groundwork for future empirical validation and practical application in enhancing organizational cybersecurity.
New results on system of variational inequalities and fixed point theorem
In (Croat. Oper. Res. Rev, 13(1), 131-135, (2022).) A. Benhadid showed by a counterex- ample that a number of publications in the research on the variational inequalities system contains inaccurate results about applying the Lipschitz continuity concept in relation to both the first and sec- ond variables during the proof of the main theorem on the part of authors. In this paper, We suggest to applying fixed point theorem to correct the main results of some publications.
Optimizing maritime routes: A multi-method analysis from Shanghai to Vladivostok
This research analyzes the marine route plan from Shanghai to Vladivostok utilizing Dijkstra’s algorithm, Markov chain analysis, game theory, and congestion analysis. Dijkstra determines the route through Busan and Hungnam as the shortest, with a total distance of 2114 kilometers and minimal travel time. The Markov chain analysis supported the designated path by demonstrating greater transition probabilities compared to other routes, so establishing it as the most probable option. Experts in game theory, particularly on the Nash equilibrium, demonstrated that cooperation significantly reduced operating expenses. Further congestion research corroborated that the Shanghai-Busan-Hungnam-Vladivostok route offers a cost advantage, and even with the inclusion of congestion, the route remains less expensive. The study collectively advocates for the consideration of distance, likelihood, collaboration, and congestion while selecting the optimal maritime route, hence enhancing efficiency in maritime logistics.
ANFIS computing and cost optimization of an M/M/c/M queue with feedback and balking customers under a hybrid hiatus policy
The present investigation studies a hybrid hiatus policy for a finite-space Markovian queue, incorporating realistic features such as Bernoulli feedback, multiple servers, and balking customers. A hybrid hiatus policy combines both a working hiatus and a complete hiatus. As soon as the system becomes empty, the servers switch to a working hiatus. During a working hiatus, the servers operate at a reduced service rate. Upon completion of the working hiatus and in the absence of waiting customers, the servers enter a complete hiatus. Once the complete hiatus period concludes, the servers resume normal operations and begin serving waiting customers. In the context of Bernoulli feedback, the dissatisfied customer can re-enter the system to receive another service. By utilizing the Markov recursive approach, we examined the steady-state probabilities of the system and queue sizes and other queueing indices, viz. Average queue length, average waiting time, throughput, etc. Using the Quasi-Newton method, a cost function is developed to determine the optimal values of the system’s decision variables. Furthermore, a soft computing approach based on an adaptive neuro-fuzzy inference system (ANFIS) is employed to validate the accuracy of the obtained results.
Copyright (c) 2024 Croatian Operational Research Review
Exact methods for the longest induced cycle problem
The longest induced (or chordless) cycle problem is a graph problem classified as NP-complete and involves the task of determining the largest possible subset of vertices within a graph in such a way that the induced subgraph forms a cycle. Within this paper, we present three integer linear programs specifically formulated to yield optimal solutions for this problem. The branch-and-cut algorithm has been used for two models that cannot be directly solved by any MILP solver. To demonstrate the computational efficiency of these methods, we utilize them on a range of real-world graphs as well as random graphs. Additionally, we conduct a comparative analysis against approaches previously proposed in the literature.
Data mining approach in detecting inaccurate financial statements in government-owned enterprises
The study aims to assess the capability of various data mining techniques in detecting inaccurate financial statements of government-owned enterprises operating in the Federation of Bosnia and Herzegovina (FBiH). Inaccurate financial statements indicate potential financial fraud. Prediction models of four classification algorithms (J48, KNN, MLP, and BayesNet) were examined using a dataset comprising 200 audited financial statements from government-owned enterprises under the supervision of the Audit Office of the Institutions in the Federation of Bosnia and Herzegovina. The results obtained through data mining analysis reveal that a dataset encompassing seven balance sheet items provides the most comprehensive depiction of financial statement quality. These seven attributes are: opening entry of accounts receivable, profit (loss) at the end of the period, operating assets at the end of the period, accounts receivable at the end of the period, opening entry of operating assets, short term financial investments at the end of the period, and opening entry of short-term financial investments. By employing these seven attributes, the MLP algorithm was implemented to construct the most precise predictive model, achieving a 76% accurate classification rate for financial statements. Leveraging the identified attributes, a mathematical model could potentially be formulated to effectively predict financial statements of government-owned enterprises in FBiH. This, in turn, could considerably facilitate the process of selecting GOEs for inclusion in the annual work plan of state auditors. Presently, due to resource constraints, government-owned enterprises in FBiH do not undergo regular annual scrutiny by state auditors, with only 10 to 15 such enterprises being subject to audits each year. The results of this research can also be beneficial to both the public and the Financial Intelligence Agency in the FBiH. The paper contributes to filling the gap in the literature regarding the applied methodology, particularly in the part concerning the attributes used in the research.
CB-SEM vs PLS-SEM comparison in estimating the predictors of investment intention
This paper compares different structural equation modeling approaches in estimating the predictors of investment intention. Covariance-based structural equation modeling (CB-SEM) and partial least squares structural equation modeling (PLS-SEM) techniques were compared in the estimation of the model according to the theory of planned behavior (TPB). Additionally, the consistent PLS algorithm (PLSc) was taken into consideration in the methods comparison. To determine which factors affect stock investment intention, a TPB model with attitude towards behavior, perceived behavioral control, and subjective norm as independent variables was estimated using three different approaches. The factors in the model were measured using survey indicators and the final sample included 200 Croatian residents. The results mostly show matching conclusions about the investment intention predictors, with a small difference observed in the PLS-SEM method. It can also be concluded that the factor loadings are higher according to PLS-SEM, as well as the indicators of convergent validity and reliability. On the other hand, CB-SEM shows stronger structural paths than PLS-SEM, and PLSc results are closer to those of CB-SEM. While CB-SEM shows better model fit, PLS-SEM shows high predictive power. This research further provides explanations of the differences and guidelines on when to use which approach.
On some quantitative indicators of a few features of electoral systems
Electoral systems can be analyzed by means of their numerous properties. Some quantitative indicators of a few technical features of electoral systems are considered. With respect to the effective number of parties in a electoral system, one can observe some known indicators (e.g., fractionalization of vote shares, the Laakso-Taagepera index, the Wildgen index, the Molinar index). With regard to the government stability, one can look at the indicator which is called the expectation of government stability. These indicators are examined from the empirical point of view, i.e., in relation of elections in different countries.
A cost analysis of single-server discouraged arrivals with differentiated vacation queueing model
This paper investigates an M/M/1 queueing system with differentiated vacations and discouraged arrivals, focusing on two types of vacations. The server switches to type I vacation with rate γ1 when the system is empty during an active state. If no customers are waiting when it returns from a type I vacation, it then switches to a type II vacation with a rate of γ2. Both vacation times and service duration follow exponential distributions. The study utilises the Probability Generating Function (PGF) technique to derive steady-state solutions for both vacation policies. Furthermore, the research explores relevant performance metrics and provides numerical examples to illustrate the system's behaviour under various conditions. The cost analysis of the M/M/1 differentiated vacation system with discouraged arrival queueing and various aspects of the system's behaviour under different arrival rates (λ, λ1, λ2) are discussed
A hybrid population-based algorithm for solving the Minimum Dominating Set Problem
The Minimum Dominating Sets (MDS) problem is pivotal across diverse fields like social networks and ad hoc wireless networks, representing a significant NP-complete challenge in graph theory. To tackle this, we propose a novel hybrid cuckoo search approach. While cuckoo search is renowned for its wide search space exploration, we enhance it with intensification methods and genetic crossover operators for improved performance. Comprehensive experimental evaluations against state-of-the-art techniques validate our approach's effectiveness.
A genetics algorithms for optimizing a function over the integer efficient set
In this paper, we propose an algorithm called Directional Exploration Genetic Algorithm (DEGA) to resolve a function $\Phi$ over the efficient set of a multi-objective integer linear programming problem (MOILP). DEGA algorithm belongs to evolutionary algorithms, which operate on the decision space by choosing the fastest improving directions that improve the objectives functions and $\Phi$ function. Two variants of this algorithm and a basic version of the genetic algorithm (BVGA) are performed and implemented in Python. Several benchmarks are carried out to evaluate the algorithm's performances and interesting results are obtained and discussed.
A fresh look at a randomized massively parallel graph coloring algorithm
Petford and Welsh introduced a sequential heuristic algorithm to provide an approximate solution to the NP-hard graph coloring problem. The algorithm is based on the antivoter model and mimics the behavior of a physical process based on a multi-particle system of statistical mechanics. It was later shown that the algorithm can be implemented in a massively parallel model of computation. The increase in computational processing power in recent years allows us to perform an extensive analysis of the algorithms on a larger scale, leading to the possibility of a more comprehensive understanding of the behavior of the algorithm, including the phase transition phenomena.
Multi server queuing model with dynamic power shifting and performance efficiency factor
The need for high performance models in the queuing systems; availability in the fields of computing and communication systems and logistics management poses extensive challenges in design and development of the appropriate modeling. In this work, we propose a robust probabilistic model to mitigate these problems by appropriately choosing a multi-server queuing system with dynamic service facility. The arrival substances, such as packets or jobs or customers or consumers, follow a Poisson arrival process and these arrivals enter a queuing system according to FIFO discipline. The system designed in the system is armed with a fixed number of service stations, in which some servers are capable of allocating additional service capacity by adjusting dynamically. When a customer arrives at the system, if a free server is available, it is immediately served by one of the free processors; if no server is free, that is all are busy, the arrived job is accommodated in the queue and waits for service in the system. In this paper, we proposed a stochastic model to handle peak loads efficiently, boosting service capacity during burst arrivals. Numerical results presented in this paper are generated by the spectral expansion method to demonstrate the model’s performance, offering insights into its efficiency and accuracy. Furthermore, we derived some important special cases of speedup factor, which provide the mathematical estimation of system performance in terms of computation and communication times.
Quantifying the effects of expectation variability on economic dynamics: insights from the Dornbusch overshooting model
The article applies the famous Dornbusch "overshooting" model to investigate the impact of different types of expectations on economic model stability. We tested the well-known Dornbusch model with discrete variables. Initially, we established a foundational model, employing simulations to illustrate the impact of each parameter within the model on the overall solution, starting from an initial value. Subsequently, we explored the influence of different expectation types on the stability of the steady state vector, considering simple, static, adaptive, and rational expectations (Muth-type rational expectation and perfect foresight). It was observed that static expectations and perfect foresight did not contribute to stability. In the case of simple, adaptive, and rational expectations, the stability conditions (parameter combinations) are the same as the stability conditions for the steady state vector of the baseline model. This condition is restricted to four parameters: two related to the interest rate, one to the foreign-domestic price level and one to the adjustment speed. We also run a simulation to show how the inclusion of each type of expectation leads to a change in the global solution of the model for a given initial value.
Analyzing correlations between approaches to analyzing decision-making styles
Descriptive decision-making theories center on the analysis of individuals' decision-making styles (DMSs). Different approaches have been used to study DMSs, but this study focuses on two approaches: those of (1) Scott & Bruce and (2) Rowe. Scott & Bruce categorizes DMSs into rational, intuitive, dependent, avoidant, and spontaneous, whereas Rowe delineates them into analytical, behavioral, conceptual, and directive. Previous studies have independently used these approaches to identify dominant DMSs or to establish correlations between DMSs and various personal characteristics. In this study, both DMS approaches were concurrently used to examine their correlations. The sample comprised 263 students, and correlation analyses were conducted on the entire dataset and its subsets while considering different characteristics (i.e. students' gender and study program, and the data collection timeframe). Multivariate correlation, along with descriptive statistics, was employed to investigate the correlation between the two approaches. The analysis revealed a positive correlation between the behavioral and dependent styles and a negative correlation between the avoidant and analytical styles. Additionally, the examination of the dominant and submissive styles indicated a significant connection between the analytical and rational DMSs. Overall, the two approaches exhibited weak connectivity. For a more comprehensive understanding of decision-makers' behavior and enhanced predictive capabilities, the concurrent application of the two approaches is imperative.
A dual method for polar cuts in disjoint bilinear programming
As one branch of deterministic approaches to disjoint bilinear programming, cutting plane methods are renowned for its ability to systematically reduce the search space by adding cutting planes that are able to cut off regions deemed infeasible or suboptimal. Polar cuts have been widely utilized as a dominating type of cut in terms of deepness. During the establishment of a polar cut, the modified Newton's method is employed to derive the cutting points along the positive or negative extensions of edges emanating from a local solution. Nonetheless, its performance can be further improved along the positive extensions. Drawing inspiration from integer programming, we develop a new approach based on the LP duality theory for this purpose. It re-formulates the original program with a piece-wise linear concave objective function as a single LP. Moreover, we propose a new technique to derive the edges as accommodation to degeneracy. Numerical results show that, by utilizing our newly developed dual method, computing time can be gradually saved as the percentage of generated cutting points along the positive extensions of edges rises.
Impact of technological interdependencies on employment by BCFE method: evidence from GVC participation of developing countries
Technology has a profound impact on the job market, creating both opportunities and challenges for workers, companies and economies. Its effect on employment has been discussed extensively for developed countries while it has not been widely examined in developing countries. This paper provides an examination of the interplay between technology and employment in developing countries through their participation in global value chain in a sample of 33 developing countries over the period 2010-2020. The Dynamic panel bootstrap-corrected fixed effects estimation showed positive and relevant impact of Backward GVC participation and ICT imports on employment, while the effect of forward participation was not significant.
Stock assessment using Cumulative Prospect Theory in DEA cross-efficiency model: a case study of the Indian stock market
Market volatility is becoming increasingly common as numerous factors are implemented in the financial system. As a result, portfolio managers and individual investors require reliable methods to assess stock performance. This study examines stock assessments using cross-efficiency evaluations in cases where negative data is present. An alternative approach to achieve this goal is to use an RDM DDF-based cross-efficiency model which oversees the negative data. We expand the RDM-based cross-efficiency analysis, which uses row and column average values to select portfolios and identify different groups for stock management. To explore the psychological factors that influence the choices made by stock market investors, we incorporate the cumulative prospect theory value for each stock as an output and the variance as an input to evaluate the overall efficiency of the assets. For the empirical analysis, our study focuses on a sample of 30 stocks listed on the Nifty-50 on India's National Stock Exchange. The results of our empirical study verify that the proposed method can serve as an effective tool for stock selection. This demonstrates how the chosen portfolio gives companies a more diversified and well-balanced approach for selecting stocks, thus improving logical decision-making.