This work considers a production inventory system where the state equations involve two types of continuous dynamics, one for the supplier and the other for the buyer. The objective is to minimize the total cost resulting from the squares of the inventory of the supplier, the buyer and the production rate. As this is a multi-dimensional system, we express the model in the form of a linear quadratic regulator in matrix form, which ensures closed-form solutions to the problem. By applying Pontryagin’s maximum principle, analytical solutions are obtained and expressed as the solution of a Hamiltonian Jacobi matrix differential equation with time-varying coefficients. The final time is taken as finite, and the corresponding inventory level is taken as free. Under these assumptions, a change of the co-state variable is designed so that the new co-state variable is obtained as the solution of a matrix form of the Riccati differential equation. This can be solved analytically by following a suitable technique to determine the new costate variable. The optimal values of the state and control variables are then obtained with the help of this costate variable. Finally, the proposed model is illustrated through a numerical example.
The highest rate of fatal accidents in aviation is attributed to the general aviation segment. Due to their specific nature, general aviation organizations lack implemented methods for measuring pilots' task load. Consequently, assessing the operational risk associated with a flight is hardly ever done. This results from the limited reporting of events by flight crews. Implementing task-load assessment based on objective methods could be beneficial from the standpoint of managing the risks associated with aviation operations. The research aimed to verify the usefulness of objective methods of assessing pilot workload from the perspective of small general aviation organizations. The study concentrates on the critical comparison of three objective methods: heart rate (HR), electroencephalography (EEG), and galvanic skin response (GSR). An expert evaluation of selected methods and flight tests of these methods that were deemed suitable in the expert evaluation were performed. Heart rate measurement using a chest strap received the highest ratings from experts and the most positive feedback from pilots during testing. It also provided the most accurate measurements. The study further emphasized the critical role of pilot task load in shaping overall flight safety in the specific conditions of general aviation organizations.
This study investigates the determinants of performance measurement system (PMS) maturity in business support institutions (BSIs) operating within a networked economy. Building on the resource-based view, dynamic capabilities perspective and relational approaches to value creation, the paper proposes and empirically verifies a conceptual model integrating organizational competences, relational value creation, trust, network cohesion and institutional pressure. The empirical analysis is based on survey data collected from 164 senior managers representing public-sector BSIs in Poland. The study employs a multi-stage analytical approach, including descriptive statistics, reliability analysis, correlation analysis (Pearson, Spearman, and Kendall), multiple linear regression and structural equation modelling (SEM). The results demonstrate that PMS maturity is primarily driven by organizational competences and relational mechanisms, rather than by institutional pressure. In particular, organizational competences exert a strong direct influence on PMS maturity and also indirectly affect relational value creation through a partial mediation mechanism. Relational value creation emerges as a key factor stimulating the development of advanced measurement systems, indicating that intensive interorganizational cooperation fosters the need for more integrated, adaptive, and learning-oriented measurement practices. The findings further reveal that while trust and network cohesion are strongly interrelated, their direct impact on PMS maturity is limited, suggesting that they function mainly as enabling conditions for cooperation. Institutional pressure, in contrast, exhibits a weak but consistently negative effect, indicating its role as a constraining contextual factor rather than a primary driver of measurement system development. By shifting the explanatory focus from institutional to relational–competence mechanisms, this study contributes to the literature on performance measurement in public and quasi-public organizations. It highlights that PMS maturity in BSIs is an emergent outcome of interactions between internal capabilities and relational value creation, rather than a direct response to external regulatory demands. The results also offer practical implications for policymakers and managers, emphasizing the importance of developing organizational competences and fostering collaborative networks to enhance the effectiveness of performance measurement systems.
The aim of the paper is to present a mathematical programming model for optimizing the location of bio-belts in agricultural landscapes. The proposed model balances crop production and biodiversity conservation. Bio-belts represent an advanced form of vegetated field margins that improve ecosystem functions and reduce environmental impacts associated with intensive agriculture. The proposed approach uses a distance geometry formulation that enables the flexible placement of multiple bio-belts within irregularly shaped fields. The optimization problem aims to minimize the total unproductive area while ensuring that all parts of the cultivated land remain within an ecologically effective distance of the nearest bio-belt. The resulting nonlinear model is solved using a differential evolution algorithm. An illustrative case study shows the applicability of the proposed approach and illustrates the trade-off between cultivated and conservation areas. The results suggest that the model can serve as a decision-support tool for sustainable land management and may be integrated with precision agriculture and Agriculture 4.0 systems.
Capacity-constrained make-to-order firms often face delivery and quantity deviations that cannot be fully recovered within the available production windows. In such settings, the managerial problem is not only to improve operational performance but also to understand how bounded execution responses buy time, preserve service, and redistribute unresolved burdens across time, quantity, capacity, and recovery mechanisms. This study examines this problem through a case-grounded discrete-event simulation of a constrained make-to-order (MTO) production system. The design separates the two analytical layers. First, an AS-IS replay was used for behavioral validation under the historical order stream and to formalize the AS-IS operating rules. Second, a replicated 23 factorial experiment compares eight policy combinations defined by minimum planned lead time, shipment shortfall tolerance, and limited capacity flexibility. The AS-IS replay reproduces the structural burden of the focal system, including low delivery reliability, long flow time, backlogs, unfinished work, and completion-order exposure. The factorial results identify capacity flexibility as the dominant lever across requested-basis on-time delivery, fill rate, shipped quantity, flow time, backlog, unfinished work, and Economic Performance Proxy. Shipment shortfall tolerance reduces completion exposure and end-state burden; however, its marginal effect becomes smaller when flexible capacity is already active. Overall, this study contributes a simulation-based decision-support approach that interprets policy effects through the lens of physical operational improvement versus managerial burden redistribution in constrained production.
This paper extends prior work on linguistic‑pattern‑based facility layout optimization by enhancing the LP‑Alinks framework with an explicit spatial‑uniformity criterion. While earlier studies demonstrated that linguistic patterns can effectively encode expert knowledge and guide agent‑based layout emergence, their optimization scope remained limited to cost‑oriented objectives. To address this gap, we introduce the Normalized Coverage Score (NCS), a scale‑adjusted measure of spatial evenness that complements the classical flow–distance economic objective and enables systematic exploration of cost–uniformity trade‑offs. A full factorial experiment comprising 324 conditions evaluates the combined effects of problem size, link density, virtual‑force scaling, and two families of membership functions on both objectives. Five‑way and nested three‑way ANOVAs show that structural factors (number of objects and link density) exert a major influence on both economic performance and spatial uniformity. However, while economic cost is governed by the structural characteristics, uniformity is substantially more sensitive to the linguistic‑pattern parameters, which control the dispersion–compaction dynamics of the emerging layouts within the structural constraints. Based on these interactions, we derive a parameter‑selection matrix that prescribes settings for cost minimization, uniformity maximization, or balanced compromise. Comparative analyses with Drezner’s method, MDS, and non‑metric MDS demonstrate that the extended LP‑Alinks framework consistently attains superior uniformity while maintaining competitive economic performance, making it a reliable and interpretable decision‑support tool for early‑stage facility layout design.
This paper develops a Markov–modulated deteriorating inventory model with price–dependent demand, where both demand intensity and deterioration dynamics evolve according to a stochastic environment modeled by a continuous–time Markov chain. Unlike classical deterministic formulations, the proposed framework captures random fluctuations in market conditions and storage quality, allowing a more realistic representation of inventory systems for perishable and time–sensitive products. A finite replenishment cycle with an endogenous markdown policy is considered, and the expected inventory dynamics are described through a coupled system of differential equations driven by the Markovian environment. Based on this structure, an explicit expected average total profit function is constructed and optimized with respect to the markdown time and the replenishment cycle length. Due to the analytical intractability of the resulting objective function, global optimization methods are employed. A detailed numerical study for a two–state environment shows that pricing and replenishment decisions jointly determine the structure of the optimal policy. The results emphasize that higher selling prices lead to delayed markdowns and longer replenishment cycles, while increased deterioration intensities significantly shorten optimal cycles and reduce profitability. In contrast, cost parameters primarily shift the profit level without substantially affecting the optimal policy. These findings highlight the dominant role of revenue–driven decisions and stochastic environmental effects in shaping optimal inventory strategies for deteriorating items.
The rapid development of artificial intelligence (AI) technologies is transforming workplaces across Europe, creating both opportunities and challenges for employees. This study examines how digital competencies and prior experience with digital technologies influence employees’ perceptions of AI and robots in the workplace, using representative Eurobarometer 101.4 survey data from 27 EU member states. Two analytical approaches, a Generalized Structural Equation Model (GSEM) and a two-level mixed-effects logistic regression were employed to capture both individual-level and cross-country variation. The results demonstrate that higher digital competencies significantly increase the likelihood of perceiving AI positively, confirming previous research on the role of digital skills in technological adaptation. Experience with digital technologies also shows a positive, though weaker, effect on AI acceptance. Notably, the interaction between digital competencies and prior experience is negative, indicating that prior experience weakens the positive association between digital competencies and favorable AI perceptions. These findings highlight the multidimensional role of competencies and experience in shaping AI acceptance and suggest that digital transformation initiatives must integrate skill development with transparent communication and ethical considerations. Policy implications include the need to prioritize digital skilling, and targeted training programs that support trust in AI-driven change.
In this paper, we focus on nonlinear programming problems with cardinality constraints that restrict the number of nonzero components of the decision vector. We start with a straightforward mixed-integer programming reformulation. Inspired by previous papers, the binary restrictions on variables are then relaxed, leading to a large number of stationary points based on the Karush-Kuhn-Tucker optimality conditions. Therefore, we employ so-called binary penalties that penalize the non-binary values in the decision vector. This leads to a desirable reduction in the number of stationary points. In the numerical part, we compare the solution approaches on test instances of a portfolio selection problem.
This paper introduces the Estonian Musculoskeletal Disorders Questionnaire (EMDQ), a practice-oriented tool for assessing the prevalence of musculoskeletal disorders in the workplace. Work-related musculoskeletal disorders (WMSDs) are an occupational health concern. Prevention largely depends on workstation-level interventions, where monitoring musculoskeletal discomfort helps set priorities. Although several tools like Nordic Musculoskeletal Questionnaire (NMQ) and the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) exist for mapping WMSD prevalence, they often face usability challenges for both workers completing the questionnaires and occupational health and safety (OHS) specialists analyzing the data. The EMDQ is designed to address these issues. Available in both paper and electronic formats, it features a gender-neutral body map that divides the body into distinct regions, supporting inclusive dissemination among workers. The questionnaire is intentionally brief to minimize worker effort. For OHS specialists, the EMDQ enables automated data processing that: (1) visualizes the most affected body regions, (2) estimates prevalence within the sample and extrapolates to the entire workforce, and (3) compares company-level data with sectoral averages. Additionally, the EMDQ includes guidance to ensure compliance with GDPR, supporting ethical data handling practices.
Despite the critical importance of occupational health and safety (OHS), limited research has employed structural equation modeling (SEM) to systematically examine the impact of occupational accident risk factors on business performance. This study addresses this gap by integrating SEM and Bayesian network (BN) analysis to examine both the direct and indirect effects of occupational risk factors on organizational outcomes, providing a comprehensive and probabilistic framework for decision-making in occupational risk management. A conceptual framework was developed based on an extensive literature review. Data were collected from 200 respondents representing different textile manufacturing firms. The results indicate that occupational risk factors significantly influence production efficiency and business performance. Among the factors examined, psychosocial risks were identified as the most impactful, followed by chemical risks. The BN model validated the SEM findings and provided complementary probabilistic insights, enhancing the results’ robustness.
Portfolio selection is a critical issue in financial management under uncertainty. In this paper, we propose a complex approach for portfolio selection with quantile return approximation. In particular, we propose a practical workflow that combines principal component analysis with quantile regression into the Quantile Regression-Principal Component Analysis (QR-PCA) framework. The use of quantile regression allows to capture asymmetric and heterogeneous conditional behavior of return distributions. This strengthens the dimensionality reduction and robust regression techniques. For comparison, we consider parametric and nonparametric approximation techniques. We also design a new performance measure called quantile ratio (qR) based on approximate quantile expectations of returns incorporated in a portfolio optimization task. The proposed model is applied to a real-world dataset of financial assets, demonstrating its effectiveness in constructing portfolios that outperform traditional portfolio models. The empirical results reveal better risk-adjusted performance compared to those optimized using traditional models.
The transcendental logarithmic (translog) production function is recognised as a flexible functional form for modelling production processes. It accommodates varying elasticities of input substitution and diverse types of returns-to-scale, thus outperforming the Cobb-Douglas production function in empirical accuracy. However, the translog function qualifies as a valid production function only under specific parameter conditions. In this paper, we analyse the properties of the translog function and derive parameter conditions that ensure key properties such as monotonicity, concavity, weak essentiality, and constant returns-to-scale. Leveraging these results, we propose a systematic procedure for generating synthetic production data suitable for Monte Carlo simulations. This data can be employed to assess various features of Data Envelopment Analysis (DEA) models and to facilitate the numerical analysis of algorithms for large-scale DEA. The applicability of the proposed approach is illustrated through a case study, in which several DEA models are evaluated based on the Monte Carlo simulations.
The article introduces a special issue of the Czech Society for Operations Research (CSOR) named Mathematical Methods in Economics: Current Trends and Future Perspectives. Every year, the CSOR organizes an annual international conference Mathematical Methods in Economics (MME) and this issue contains a selection of articles accepted for presentation at MME 2024. In addition to presenting the contents of this issue, the article aims at bibliometric analysis of the activities of Czech operations researchers over the past 10 years with regard to their publications in Central European Journal of Operations Research (CEJOR). The total number of original articles published in CEJOR by Czech researchers since 2016 until now is 64 (including this issue). The published results of the research include both theoretical and applied studies, with the largest representation of topics related to data envelopment analysis, multi-criteria decision making and game theory.
Return is an essential characteristic of an investment whose positive expected value fulfils its purpose. The way of expressing (expected) return is thus a crucial (technical) issue in the investment decision-making process. Return is often stated deterministically, by means of an average characteristic, which fails to provide information about its uncertainty, ambiguity. This imperfection can be partly remedied by a return with an explicitly stochastic nature. Determining the probability distribution, its (invariable) parameters, or integrating it into a mathematical model can be a difficult task, resulting in a distortion of return expectations. Moreover, the return is nowadays increasingly burdened by non-probabilistic influences, for example in the form of various unexpected socio-economic, geopolitical or natural events. It is then more sensible to express the return as a fuzzy element, a (triangular) fuzzy number, which can also be handled very well computationally. Although the fuzzy concept is used in scientific publications, the genesis of its origin is often completely neglected. Thus, the main mission is to reflect on the shape of the fuzzy number, and the determination of its parameters, which will lead to a proper description of reality, in the context of the necessary integration into the multi-objective optimization model, which will be the cornerstone of the fuzzy method supporting a complex selection of the investment portfolio. The proposed fuzzy approach is demonstratively applied to making a portfolio of mutual funds, in the light of the influence of the sustainability phenomenon.
This study investigates mothership-drone route determination using two optimization approaches: a Mixed Integer Linear Programming (MILP) model based on a discrete hexagonal grid, and a Mixed Integer Nonlinear Programming (MINLP) model operating in continuous space. Four model variants are tested for MINLP, while the MILP model is evaluated using hexagonal and micro-hexagonal grid resolutions. The models are then tested across multiple scenarios with varying grid sizes and numbers of targets. All formulations are implemented in AMPL, and parameters are kept as consistent as possible to enable fair comparison. The computational experiments employ two solvers, BARON and Gurobi, revealing notable differences in solution quality and convergence behavior. BARON often struggles to close the optimality gap within the given time limits, particularly in larger or more complex instances, whileGurobi, demonstrates more stable and efficient convergence, frequently achieving lower objective values. Overall, the results indicate that the MILP approach is more suitable for larger routing scenarios due to its computational tractability, whereas the MINLP model is better suited for generating flexible and realistic trajectories in smaller-scale problems. Future research could integrate heuristics and extend the models to 3D or dynamic environments.
The aim of the article is to develop a model for predicting organizational commitment based on passive quitting - a mechanism related to an unintentional decline in energy, motivation, and sense of purpose at work. The study employed a psychometrically validated Passive Quitting Scale (PQS) and the UWES-9 engagement scale. Data were collected through a questionnaire survey conducted using the CAWI method on a sample of 1,040 employees in Poland. Selected machine learning algorithms, including linear, kernel-based, and tree-based models, were applied for prediction. The best results were achieved using Gradient Boosting, which was adopted as the reference model. To interpret the model’s functioning, explainable artificial intelligence (XAI) methods were used: SHAP and Permutation Importance. The analyses indicated that the key predictor lowering engagement is PQS6 (“Work no longer gives me satisfaction…”), supported by PQS2 and PQS7. The article contributes theoretically by empirically confirming the role of passive quitting as a predictor of organizational commitment and highlights the value of ML and XAI methods in employee behavior research. The findings also have practical implications, providing managers with tools to identify early signs of declining engagement.
This study analyzes historical temperature trends using Generalized Autoregressive Score (GAS) models, which capture time-varying dynamics in time series data. Using temperature data from Czechia, we examine average temperatures as well as extremes, including maximum and minimum values, and identify a consistent upward trend across all indicators, particularly since 1950. The GAS models demonstrate superior in-sample fit and out-of-sample forecasting performance compared to traditional approaches such as ARMA and GARCH models. Forecasts based on the GAS model project a substantial increase in temperatures of 1.29 °C over the next 40 years.
The banking sector is characterised by complex production processes involving multiple inputs and outputs, which makes efficiency evaluation challenging. Banks play a central role in the economy by facilitating financial intermediation, supporting economic development, and providing financial services to households and firms. Consequently, the efficiency of banking institutions is of interest not only to bank management but also to regulators, investors, and the general public. In recent years, these issues have gained further importance due to structural changes in the banking industry, problems and failures at some major banks, increasing digitalisation, and the gradual transformation of the role of traditional bank branches. This paper evaluates the efficiency of a bank branch network operating in the Czech banking market using data envelopment analysis (DEA). The analysis is conducted at the branch level, reflecting the growing importance of branch efficiency for cost control, service provision, and customer interaction. Three complementary perspectives on efficiency are considered: production efficiency, profit efficiency, and transactional efficiency, the latter capturing the ongoing shift of banking activities toward digital and alternative service channels. The study examines a dataset of 393 bank branches observed over the period 2019–2021. Several DEA models are applied and adapted to the specific characteristics of banking operations, including Tone’s slacks-based measure model and models with desirable inputs, non-controllable outputs, and undesirable variables. Transactional efficiency is assessed using an output-oriented BCC model, while production and profit efficiency are evaluated using non-oriented SBMT models with variable returns to scale. The results indicate that the branch network operates close to the efficient frontier in terms of transactional efficiency, suggesting limited potential for further improvement in shifting transactions to alternative channels. In contrast, substantial inefficiencies are identified in production and profit efficiency, with average production efficiency around 0.6 and profit efficiency around 0.8. A statistically significant positive relationship between production and profit efficiency is observed, while both show a weak negative correlation with customer satisfaction. The analysis further identifies personnel costs as the primary source of profit inefficiency and insufficient volumes of banking products as the main driver of production inefficiency. The findings demonstrate the usefulness of combining multiple DEA models to obtain a comprehensive assessment of bank branch performance and provide actionable insights for branch-level management and strategic decision-making.
Effective government intervention is critical for managing medical supply shortages during public health crises. This paper focuses on government measures to alleviate medical supply shortages across different infectious disease levels. In particular, we develop some Stackelberg game models that incorporate consumer risk preferences and disease-severity classifications to analyze the impacts of price controls and purchase restrictions on supply-chain performance under different policy scenarios. It is shown that supply-side price caps tent to compress manufacturers' profits but enhance equity by prioritizing access for vulnerable populations and reducing general shortage rates, albeit potentially increasing actual shortages due to reduced production incentives. Meanwhile, the efficacy of demand-side purchase limits is highly contingent on consumer psychology and practical constraints, leading to variable outcomes for profits and supply gaps. Both disease severity and policy type are critical determinants of supply-chain outcomes. Consequently, governments should tailor retail price ceilings to the specific disease level to optimize resource allocation and minimize overall shortages.
