The COVID-19 pandemic, volatile market conditions, shorter lifecycles, and cost-effective goods confront the reconfigurable manufacturing system (RMS). The RMS needs Industry 4.0 and circular economy technologies to boost performance. However, present literature does not integrate these technology and methods. This paper proposes a framework for adopting these technologies and practices. The SWARA method determines relative weights of specified practices, Pythagorean fuzzy COCOSO ranks performance indicators based on expert opinion, along with sensitivity analysis. The important performance metrics in key criteria practices are production lead time, advanced technology usage, and machine utilization rate, according to the report. Waste reduction and production costs are also important to experts. Managers can use the framework to increase RMS performance and handle COVID-19. It enables practitioners use sophisticated technology and CE practices and evaluate their success using metrics.
This study introduces the Multi-League Referee Assignment Problem (MRAP), a practically motivated optimization problem arising in the scheduling of referees for non-professional football. MRAP accounts for multiple concurrent leagues with distinct scheduling constraints, qualification requirements, and fairness considerations. We formulate MRAP as an integer programming model and assess its performance through extensive numerical experiments on realistic synthetic data. The monolithic model becomes computationally infeasible for large instances. Therefore, we propose two heuristic approaches that decompose the problem temporally by weeks or structurally by leagues. These heuristics quickly produce high-quality solutions when referee availability is sufficient. Notably, shorter batches of weeks in temporal decomposition perform better, as longer batches create restrictive commitments. Sensitivity analyses further demonstrate the practical value of optimization-based decision support.
We address the retail shelf space allocation problem, which incorporates product simultaneous categorization across horizontal and vertical shelves. The problem also includes products with varying storage conditions and limited compatibility to be sold nearby. Our mathematical model focuses on optimizing the visibility of the products on the shelves in the retail store. We introduce a novel flower-cutting heuristic, which employs two variations of internal sorting rules. Seventeen parameters were used to narrow the solution space and generate efficient solutions without relying on randomization or exhaustive searches. When compared to optimal solutions obtained from a commercial solver, our approach yields solutions with 98.58%-100% accuracy. Optimal solutions were achieved for 17 out of 25 instances using both heuristics. The developed heuristics offer an approach that focuses on optimizing resource allocation problems.
Railway transportation plays a critical role in supporting sustainable mobility in Indonesia, yet significant fluctuations in passenger demand often lead to congestion and operational challenges. This study presents a systematic evaluation of decomposition-based forecasting frameworks for railway passenger demand prediction by integrating Seasonal-Trend Decomposition using Loess, EMD applied to residual components, and Fuzzy C-Means clustering. Using the Argo Muria train service as a case study, multiple deep learning models, including LSTM, GRU, RNN, CNN, and BiLSTM, are trained on decomposed components, and their forecasts are combined linearly. Model performance is evaluated using a rolling-origin strategy across multiple stations. At the primary destination station, Semarang-Gambir, the best configuration achieves an MAE of 19.88, RMSE of 26.79, sMAPE of 8.97, and R2 of 0.84. Consistent results across stations demonstrate the framework's robustness and generalization capability.
Given the significant shift toward a digital-driven ecosystem, the traditional marketing evaluation framework faces limitations in capturing the complexity of modern organizational performance. This study develops an integrated performance evaluation model based on the expanded 7P framework. By using Multi-Criteria Decision-Making (MCDM), the proposed model standardized 21 key performance indicators and applies a mathematical weight mechanism to prioritize strategic marketing activities. The model was also validated by marketing experts to ensure its practical relevance and adaptability to dynamic market environments. The study demonstrates the possibility of integrating qualitative and quantitative approaches and outlines a simplified methodology that supports the practical application of the 7P model using an MCDM-inspired approach.
The study aimed to examine the association between cultural background and propensity of employees to stay at work despite illness/disability. The scope of the study includes 29 European countries representing significant differences in cultural parameters. The National Culture model by Hofstede was used for the analysis. It distinguishes 6 dimensions of national cultures: power distance, individualism, indulgence, uncertainty avoidance, motivation towards achievements and success, and long-term orientation. All these dimensions characterize social behavior, values, and attitudes across several important areas that – we believe – may be associated with the decision to stay at work despite the rationale for a layoff. Classification trees and cluster analysis were incorporated in the analysis. The work results in the identification of groups of countries displaying similar characteristics of presenteeism in relation to cultural models.
In this paper, we propose a reduction operator addressing discrete optimization problems, more precisely, for optimization problems with binary represented decisions. This operator may be integrated within various metaheuristics in order to reduce the dimension of the target problem. This is, by performing an iterative supervision over the browsed admissible space throughout the search process. We thus use the information entropy concept to measure the current uncertainty towards decision variables current values provided by an iterative heuristic search algorithm. Hence, it allows to explore the decision space more efficiently. Furthermore, we present two frameworks as effective applications of this operator: a pre-process for a hybrid approach and an interactive heuristic approach. We compare it against established approaches from the literature.
This study examines barriers to QRIS adoption among Indonesian traditional merchants by integrating technological anxiety and regulatory burden into the UTAUT2 model. Using mixed methods with data from 185 merchants across three major cities, PLS-SEM analysis shows effort expectancy (β = 0.342) and performance expectancy (β = 0.295) significantly drive adoption intention. However, technological anxiety (β = −0.218) and perceived regulatory burden (β = −0.186) are significant inhibitors. Qualitative findings reveal additional concerns about transaction costs, administrative complexity, and internet dependency. The study provides practical recommendations for Bank Indonesia and fintech providers to design more inclusive adoption strategies for micro-merchants.
In management science, mergers and acquisitions are common strategies to enhance the efficiency of decision-making units (DMUs). While several methods have been proposed within data envelopment analysis (DEA) to facilitate merging and acquiring DMUs, none adequately consider the specific targets of decision-makers. This study first introduces a novel approach to identify a desirable DMU based on the gradient of the target function; subsequently, all units are ranked according to their distance from these corresponding target units. Following this, a method is presented to select the best partner for the merger or acquisition of a specified unit. Furthermore, new models are developed for horizontal mergers and acquisitions that align with the gradient of the decision-maker's target function, thereby enabling the formation of a new unit with the desired efficiency level. Finally, the proposed models are applied to rank, merge, and acquire banks in Iran.
This study focuses on solving multi-objective linear fractional programming (MOLFP) problems, which include linear fractional objective functions and a feasible polyhedron set. Based on a linearization technique, two new approaches are presented to determine efficient (Pareto) and properly efficient solutions to MOLFP problems. In the first approach, the efficiency status of an arbitrary feasible point of MOLFP is examined. If this point is not efficient, an efficient projection onto the efficient space is obtained. This method is able to find an efficient solution in just one step in a linear manner; therefore, it needs much less time and computation compared to some famous previously presented methods. Two numerical examples are given to show the applicability and advantages of the new approaches. (original abstract)
Generated quantity and market prices are major risk sources for electricity market participants, especially renewable producers with intermittent generation. Before delivery, wind and solar power plants must decide their day-ahead bids, which, through deviations from actual generation, determine the direction and volume of intraday trade. We identify an arbitrage opportunity between these short-term markets that increases expected profits and reduces trading risk. To exploit it, we propose a novel multiple-split approach for estimating multidimensional probabilistic forecasts of market fundamentals. This semi-parametric method produces ensembles preserving true error correlations and supports forecasting of linear and nonlinear functions such as price spreads, residual load, or trading income. Using German electricity market data, we demonstrate superior predictive and economic performance over standard benchmarks. (original abstract)
This research examines the impact of non-dominated alternatives on rankings and explores rank reversal (RR) in the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a widely used distance-based MCDM method. A theoretical analysis reveals how the mathematical operations of TOPSIS contribute to RR through the relative closeness and separation measures. Four scenarios are outlined to identify conditions where RR becomes unavoidable. The study provides new insights into the mathematical foundations of RR and its implications for decision makers. To address this issue, three strategies are proposed: identifying non-dominated alternatives, recognizing conditions leading to close performance margins, and normalizing ideal solutions to fixed reference values. These findings offer practical guidance for developing distance-based MCDM methods that minimize rank reversal.
Capacitated Clustering Problem (CCP) assigns objects to capacitated clusters. Among various CCPs, this work focuses on the capacitated P-median problem (CPMP). Imposing a strict capacity on each cluster, often leads to inefficient overlap for geographical data, which significantly diminish efficiency. We propose a four-stage clustering algorithm, applying convex polygon boundary perturbation to minimize overlap while satisfying capacity constraints. Necessitating a fixed number of clusters in many applications introduces a challenge in meeting capacity constraint. While most methods open a new cluster for points left unassigned, this paper designs a placement algorithm without allowing new clusters. The proposed algorithm demonstrates superior performance compared to three state-of-the-art approaches on specific instances, achieves similar performance on others, with some results reaching optimality. A novel metric is introduced to compare different methods on the same benchmarks.
The Markowitz mean return-standard deviation portfolio selection model refers to single-period investing. A common practice is using this model for multi-period investing with portfolio rebalancing at specified times. Such investing is suboptimal compared to dynamic multi-period investing but is much simpler, which matters in practice. Usually, the investor controls the risk implicitly by selecting the desired mean return within the range of possible mean returns. Next, depending on the location of the selected value in the range, the investor assesses the risk according to his/her mean return-risk profile. However, this setup does not apply to multi-period investing because the ranges of possible mean returns and the relation between mean return and risk vary from period to period. As a result, the mean return selected in one period can correspond to a different level of risk in the next one. To address this issue, we propose to use a flexible approach, where the investor's risk is assessed relative to the possible mean return range and thus can be kept relatively consistent throughout the entire investment horizon. We experimentally examine the adequacy and predictive power of the proposed relative risk assessment in multi-period investments with rebalancing versus single-period over-the-whole-horizon investing.
We consider the problem of optimizing rolling stock operations, including preventive maintenance, to enhance technical availability. Our approach accounts for various types of rolling stock and their multi-level maintenance schedules, integrating both time-based and distance-based cycles, a combination seldom explored in existing literature. Given the complexity of this problem, we focus on short-term planning, as it provides decision makers with implications for practice regarding current operational challenges and may also influence strategic decisions. We propose a branch and bound approach to derive optimal solutions for rolling stock operations, addressing limitations in traditional manual planning commonly used in business practice. Additionally, we developed an efficient heuristic to find a satisfactory solution in a short time. Simulations for real data show that our algorithms can provide feasible rolling stock schedules that significantly improve fleet availability in the enterprise. This improvement not only reduces maintenance and downtime costs but also enables the fulfillment of more transport orders, aligning with business priorities.
Several studies have reported about sustainability issues of the National Health Insurance Fund (NHIF) due to a mismatch between assets and liabilities. Despite these studies, no study has worked on the asset and liability management (ALM) of NHIF. In response to that, we applied a stochastic programming model to devise a decision framework that proposed an appropriate funding strategy to ensure financial sustainability of NHIF over the next 10 years. The model was formulated based on the regulations of Tanzania, with scenarios for random parameters generated by ARIMA and VAR models. We also performed convergence and stability tests to determine the appropriate scenario tree for our model. Numerical results suggest that remedial contributions in some stages, along with formulating an investment portfolio that favors government securities and fixed deposits, promises financial sustainability of NHIF over the next decade.
We introduce the use of a global, nonlinear price function in linear programming and the simplex method. The usual, linear price function of this method captures the objective's behavior over the cone of directions defined by the nonbasic columns, whereas the global price function adds information about the topology of the whole feasible set. This is achieved by using an exterior penalty function. Further, degeneracy is effectively handled through explicit degeneracy-breaking constraints. The use of the global price function leads to a pricing problem that yields an improving, non-edge feasible direction, which is converted to an auxiliary variable to be used in the simplex method. Preliminary computational results with nondegenerate and highly degenerate linear programs show that the introduction of a global price function in the simplex method, and degeneracy-breaking constraints if needed, may significantly enhance its performance.
Compared to deciding under a single criterion, in Multicriteria Decision Aiding the role of the decision-maker is much more predominant. This is because in the former case, once the decision model is formulated, the notion of optimal decision is well defined, whereas in the latter, it is not. The behavioral biases influencing the model construction can be in both cases the same; however, in Multicriteria Decision Aiding decisions are selected from a range of incomparable Pareto optimal candidates. Thus, in Multicriteria Decision Aiding there is more space where behavioral biases can emerge. In this study we identify, within the Multicriteria Decision Aiding setting, the most important decision process drivers that cause the decisions to be satisficing but rarely the best possible. To tame the impact of those drivers on the decision aiding process, we propose a generic Multicriteria Decision Aiding tool, a sort of {\it what-if analysis}, and show how to make it operational.
According to the data provided by Coface platform, there are almost 3.8 million registered companies in the Visegrad Group (V4), with a significantly increased number of bankruptcies over the last years. Therefore, the main aim of this paper is to identify stable key indicators that determine the financial condition of these companies, which is of crucial importance for stakeholders and investors. To address this topic, we rely on the original dataset consisting of 145,638 company-years from the V4 countries, covering six main sectors during the period of 2018-2021. We calculate 78 financial and non-financial ratios, and we build a robust framework for the identification of the most important ones. Our framework relies on explainable machine learning techniques followed by cross-country and cross-sectional comparisons of the indicators. The results reveal that most of the non-financial indicators included in the analysis are important in assessing the financial condition of companies.
Unemployment is a key macroeconomic indicator for the labour market's health. Economic shocks, political changes, and structural shifts in the UK have shaped its dynamics. Using 326 monthly observations from 1997–2024 (UK Office for National Statistics), this study forecasts unemployment via ARIMA(1,1,1), ARIMAX, Random Forest, and XGBoost. Especially, ARIMA works for short-term predictions but misses structural breaks and non-linearities. ARIMAX, with gross value added as an exogenous variable, offers slight gains yet suffers from heteroskedasticity. XGBoost delivers the best performance by capturing nonlinear relationships, but direct interpretability is limited. The structural stability test was inconclusive, constraining regime-switching or rolling forecasts. Future research should address these limitations and integrate SHAP-based interpretability with feature significance analysis to better understand model behaviour and the drivers of unemployment.
