Preface to the Special Issue “Advanced Applications of Multi-Criteria Decision-Making Methods in Operational Research”

Decision-making is a consistent part of the daily activities of individuals and organizations. All decisions are based on the evaluation of individual decision options, usually grounded in the preferences, experience, and other data of the decision-maker. Some decisions are relatively simple, especially when the consequences of a wrong choice are small, while others are highly complex and have significant effects. Frequently, real-life problem-solving involves several competing viewpoints that need to be considered to arrive at a reasonable decision. Formally, a decision can be defined as a choice made based on available information or a course of action intended to solve a specific decision problem. Multi-criteria decision analysis (MCDA) involves assessing various courses of action or options, ultimately selecting the most preferable alternative or ranking them from best to worst. In our daily lives, the use of MCDA is crucial to indicate the best rational alternative to the decision-maker, allowing for the allocation of finite resources among competing and alternative interests, whether in an organizational or household environment. Recognizing the importance and advancement of this field, this Special Issue, entitled “Advanced Applications of Multi-criteria Decision-Making Methods in Operational Research,” presents nine articles selected from the 24 submissions received. These articles, which successfully passed the peer-review process and were published between February 2023 and April 2025, bring original research ideas that significantly contribute to operational research, with a strong emphasis on developing and applying decision-support methods.

In the first article, Barbara et al. (Contribution 1) present waspasWEB, an online decision-making tool based on the WASPAS method, and an R package available on CRAN. The tool facilitates the application of multi-criteria decision analysis by providing an intuitive solution. The article details the platform and validates its application through a case study. Li et al. in (Contribution 2) advance the study of multi-objective bi-matrix games by incorporating fuzzy payoffs (MBGFP), addressing the challenge of imprecise information in game theory. The main innovations include establishing the conditions for a fuzzy Pareto–Nash equilibrium and developing a parametric bilinear programming method to calculate this equilibrium. In addition, the article introduces the concept of fuzzy weighted Pareto–Nash equilibrium, providing the existence conditions and a calculation method, thus offering new tools for analyzing games with fuzzy uncertainties. Basilio et al. in (Contribution 3) present a new hybrid method, EC-PROMETHEE, for weighting criteria in decision-making processes. This method’s innovation uses a weight range per criterion, combining the ENTROPY and CRITIC methods with the PROMETHEE method. This approach generates multiple sets of weights, allowing for multiple final rankings and providing decision-makers with a more robust analysis. The EC-PROMETHEE method aims to reduce uncertainty and improve the quality of decisions by considering a range of weights rather than a single weight per criterion. In the fourth article, Yu and Lou (Contribution 4) present a new approach, integrating Data Envelopment Analysis (DEA) with Projection Pursuit Regression (PPR) to improve performance measurement and prediction. This DEA-PPR combined model addresses the limitations of traditional DEA models, particularly their inability to forecast future efficiency, and outperforms other combined models like DEA-BPNN and DEA-SVR, especially with small and non-normal distribution samples. The model demonstrates superior global optimization, convergence, accuracy, and robustness, offering a more reliable efficiency analysis and prediction tool. Gao and Lyu in (Contribution 5) propose a new three-target multiple threat assessment method designed to deal with heterogeneous information and assign relevance in complex battlefield environments. The method innovatively uses heterogeneous forms to represent dynamic assessment information and employs heterogeneous CRITIC to calculate attribute weights. It also adaptively determines risk avoidance coefficients and uses the weighted Heronian mean operator to construct comprehensive loss function matrices. In the sixth article, Salomon and Gomes (Contribution 6) present a powerful and efficient procedure for increasing the consistency of AHP pairwise comparison matrices. Utilizing means and standard deviations, the method addresses stalled decisions by deriving a more consistent matrix with minimal alterations. Ekel et al. in (Contribution 7) address the problem of resource allocation with various objectives, developing a decision-making scheme for uncertain conditions. The methodology employs a possibilistic approach with fuzzy set theory to handle uncertainty and integrate quantitative and qualitative data through transformation functions. Innovations include the uncertainty scheme and combining fuzzy sets and transformation functions for robust solutions. In the eighth article, Torres and Ramos (Contribution 8) evaluate the efficiency of postgraduate activities in Brazilian Higher Education Institutions (HEIs) using a two-stage dynamic network DEA model. It introduces a novel approach that considers graduate programs’ formative and scientific production stages and incorporates shared resources. The study also presents an efficiency decomposition method and a bi-dimensional representation of the efficiency frontier, offering new insights into evaluating HEI performance. Finally, Mirčetić et al. in (Contribution 9) address the application of MCDM methods in HRM, identifying a gap in understanding how these methods prioritize innovative HRM practices and classify companies. The study proposes an innovative MCDM approach using CRITIC and PIPRECIA-S to prioritize HRM practices and COBRA to assess companies.

The Guest Editors sincerely thank all authors for their valuable contributions to this Special Issue. We are also profoundly grateful to the anonymous reviewers for their insightful and professional evaluation reports, which have significantly enhanced the quality of the submitted manuscripts. Furthermore, we acknowledge the excellent collaboration with the publisher, the constant assistance provided by the MDPI associate editors in bringing this project to an end, and the excellent support of the Managing Editor of this Special Issue, Ms. Kelly Su.