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ABSTRACT This paper presents na integrated surgery scheduling and post-surgical bed planning problem for a standard hospital setting. The setting gives rise to a general healthcare modelling problem with a number of innovations with respect to the literature. The model includes multiple post-surgical recovery trajectories involving possible stays at the intensive (ICU) or semi-intensive care unit (SICU) and allows the decision maker to assign a bed allocation plan that considers the maximum length of stays at both SICU and ICU. The approach is designed to ensure a seamless patient flow, avoiding surgery cancellations due to insufficient downstream resources, and enables tactical planning that considers the long-term balance between demand and surgery provision across all specialities. To validate the model and investigate the sensitivity with respect to model parameters and the availability of resources, we use a series of experiments that were based on the actual operation of a military hospital’s orthopaedic department. The results illustrate the demand pressures, as na optimised allocation with the current demand and resources results in na occupation of 96.5%. We also show that increases in demand should be matched by a similar percentage increase in operating theatre capacity in order to keep the occupation below 100%.

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ABSTRACT This paper focuses on a scheduling problem that arises when job processing times are variable and subject to change based on due dates. The objective is to minimize the makespan on a single machine, while ensuring that jobs are completed independently and without interruption. This problem is challenging due to the possibility of a job’s processing time deteriorating over time. We have developed two mixed integer programming models to obtain optimal solutions, but due to the NP-hardness of this problem, we recommend using heuristics and metaheuristics (genetic algorithm and simulated annealing) as well. To validate our approach, we conducted computational experiments using randomly generated test instances. Our findings indicate that the improved second model can produce better solutions for up to 40 jobs within a one hour time frame. Furthermore, the genetic algorithm performs comparably better than other heuristics and simulated annealing for larger instances.

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ABSTRACT In response to the increasing uncertainties in agricultural decision-making, driven by market fluctuations and changes in environmental conditions, this research implemented the Balanced DecisionMaking Method (BDMM) to improve crop selection on a farm in western Bahia. The BDMM integrates multiple decision-making methods-TOPSIS, PROMETHEE II, and the Borda method-to provide a comprehensive and balanced evaluation. Both qualitative and quantitative data were gathered from literature, insights from two decision-makers, and the authors of the article. Qualitative factors, such as cost estimates, market prices, and labor requirements, were sourced from various references, while quantitative data focused on customer demand and expert consensus regarding pest risk. The combination of these data sources and the application of the proposed method enabled a detailed analysis that reflected the combined preferences and needs of each producer. The BDMM facilitated consensus among the producers, supporting decisions that optimize both economic performance and environmental sustainability.

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ABSTRACT Export market selection (EMS) is a critical strategic decision that significantly impacts the success or failure of exporting companies. This study presents an innovative multi-criteria decision-making framework that integrates Fuzzy Delphi, Best-Worst Method (BWM), and Fuzzy TOPSIS to tackle complex decision-making challenges in the context of export market selection for liquefied natural gas (LNG) and naphtha. Through a comprehensive literature review, the most important criteria for EMS are identified and ranked, culminating in an evaluation of five potential export markets. The findings reveal that ”market potential/elasticity” is the foremost criterion for EMS, with China emerging as the optimal export market for LNG and naphtha. This research not only offers a systematic methodology for export market selection but also highlights practical implications for businesses and policymakers striving to enhance export performance while aligning with broader sustainability goals, including the United Nations Sustainable Development Goals (SDGs). By providing valuable insights into market prioritization and decision-making frameworks, this study contributes to the fields of international business and petrochemical exports.

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ABSTRACT In this paper, we focus on the multi-objective stochastic multiple knapsack problem, in which the object weights are random. We propose a new approach called the multi-objective memetic algorithm with either Martello and Toth’s heuristic method (MTHM) or the method denoted MTHMn, recently developed and inspired by MTHM. In each iteration of this approach, crossover, mutation, and local search techniques are applied to the current feasible population (parents), initially generated by the greedy algorithm. These operations generate new solutions that form a population of descendants. Next, a selection operator is employed to choose the best elements from the combined population comprising parents and offspring, using the non-domination sorting and the crowding distance calculation algorithms. A comparative analysis of the memetic algorithm using the MTHM and MTHMn heuristics with an exact method is performed, and the experimental results demonstrate the significant efficiency achieved by the memetic algorithm with the MTHMn heuristic.

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ABSTRACT Finding suitable locations to expand the coverage of healthcare systems is a significant challenge, especially when different types of facilities provide different types of services to the population. In this study, we report a systematic review of the maximum coverage facility location problem (MCLP), highlighting its application to the location of healthcare facilities. This study focuses on various areas within the healthcare sector, covering emergency services such as ambulances and fire stations, as well as hierarchical services including emergency departments, specialized clinics or hospitals, among others. It also examines humanitarian aid operations and the implementation of drones or electric vertical take-off and landing (eVTOL) vehicles in medical care. We present the main modeling assumptions, solution methods and applications that have been reported in the literature, especially in the location of hierarchical facilities and in the context of the COVID-19 pandemic. We also highlight research opportunities and future perspectives. MCLPs still represent a challenge for the research community, especially those related to hierarchical healthcare systems, as can be seen from the substantial number of work in recent years.

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ABSTRACT In resource-constrained developing countries like India, delivering low cost primary care services is a challenge. Patients requiring primary healthcare spend a sizeable amount on travel, medicines, diagnostic services etc. that leads to a rise in out-of-pocket (OOP) expenses. Encouraging telemedicine practices for primary healthcare can be helpful in reducing OOP payments by patients. Additionally, telemedicine also holds the potential to handle sudden healthcare demand in pandemics like COVID-19. This article presents an optimization model for regulating OOP payments by integrating telemedicine services in existing primary healthcare network. The objective is to maximize patient coverage by regulating OOP expenses simultaneously. A trade-off between new facilities and telemedicine services is to be obtained for this purpose. Solution methods based on greedy search and genetic algorithm have also been provided. The algorithms are novel as they are based on cost-effectivenss of the facility/telemedicine center that is driven by OOP expenses and establishment cost. Numerical experiments have been conducted for validating the proposed model and to derive useful insights for policy making.

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ABSTRACT In this paper, we present a new deterministic method to optimize a linear function over the efficient set of a multiple objective integer linear problem, it is called the 2-phase algorithm. This algorithm is based on two phases : the first uses the student copula to determine the pilot objective and the second optimises it to obtain the optimal solution. To check the algorithm’s performance, we compare it with two benchmark algorithms from the literature. A detailed didactic example is given to illustrate the different steps. The algorithm is implemented on Machine and interesting results are obtained and discussed.

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ABSTRACT In this paper, we present a branch and bound-based technique for generating the nondominated set for the multi-objective assignment problem. The approach utilizes the Hungarian algorithm to solve a single-objective assignment problem at each node of the search tree. It then selects the set of candidate variables based on the descent directions of the objective functions, which enables the triggering of the branching process. The algorithm is enhanced with fathoming rules to expedite the convergence of the method. Computational experiments were conducted on a large number of instances, considering more than three objectives. These experiments demonstrate the effectiveness of the method and its competitiveness when compared to previous works.

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ABSTRACT ESG risks can no longer be ignored by portfolio managers when allocating financial resources in capital markets. However, ESG risk analysis remains a very technical subject, not easy for portfolio managers to incorporate in their daily routine. In this work, a multiple criteria decision approach is proposed to incorporate ESG risks in the asset selection process of portfolio managers. The proposal relies on ESG scores made public by an independent investment research company. Fuzzy mathematics is used to model the uncertainties present in the judgmental criteria. The Brazilian over-the-counter corporate fixed income market was chosen for the numerical examples, including fuzzy sensitivity analyses. The empirical evidence suggests that the consideration of ESG risks in the asset selection process can substantially alter the final list of securities when compared to an analysis based solely on expected return, market risk and credit risk.

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ABSTRACT In recent years, we have observed growth in the number of investors in the financial market. This work seeks to improve the decision-making process in real estate investment funds (REITs). To this end, a literature review was carried out in the Scopus database using bibliometrix to analyze the applications of the multicriteria decision aid (MCDA) in the environment of Brazilian REITs. The literature review was used as a basis for structuring the problem and the CATWOE analysis was developed to define the alternatives of the application of a hybrid PSI-CoCoSo method for ordering Brazilian REITs in the corporate sector traded on the São Paulo stock exchange (B3). As a result, the hybrid method proved to be effective in the ranking process, providing robust results on the application and evaluation of Brazilian REITs.

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ABSTRACT Airport efficiency is crucial for the competitiveness and sustainability of air transportation. This study applies a Network Data Envelopment Analysis (NDEA) model integrated with Game Theory to assess the efficiency of 31 publicly managed airports in Brazil. The model decomposes airport performance into two interrelated stages: the first stage evaluates infrastructure efficiency (runway, terminal, and apron areas), while the second stage assesses operational efficiency, focusing on converting aircraft movements into passenger and cargo throughput. The proposed approach incorporates both cooperative and non-cooperative models. In the non-cooperative scenario, a Stackelberg leader-follower game examines how prioritizing infrastructure or operations impacts overall efficiency. In the cooperative model, both stages are treated as an integrated system, working together to maximize efficiency. Findings indicate that prioritizing operational efficiency leads to superior overall performance. This study provides an innovative approach by integrating NDEA with non-cooperative and cooperative games in the airport segment, providing a more comprehensive framework for evaluating airport efficiency. From a managerial perspective, investment strategies should prioritize operational optimization before expanding physical infrastructure, offering valuable insights for policymakers in resource allocation.

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ABSTRACT The notions of the new extended b-fuzzy metric space and µ-extended b-fuzzy metric space are introduced here. In these new metric settings, several topological concepts are derived that are necessary to prove the proposed results. Some Banach-type fixed-point theorems are formulated and demonstrated. These theorems guarantee the existence and uniqueness of fixed points for mappings defined in this new setting. Suitable examples are provided to illustrate these theorems. As an application, the results presented here guarantee the existence of a solution to the Fredholm integral equation.

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ABSTRACT The study presents the application of Genetic Algorithms to solve the production sequencing problem in a motorcycle factory in the Industrial Hub of Manaus, inspired by the Traveling Salesman Problem. The goal is to minimize changeover times between motorcycle models on the assembly lines. The methodology utilizes Genetic Algorithms to search for an optimized production sequence through continuous evaluation and improvement processes. The results demonstrated the approach’s effectiveness in achieving a production sequence that significantly reduces changeover times and makes the production process more efficient.

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ABSTRACT The growing demand for a computational tool to support the selection of multicriteria decision-making (MCDM) methods, or families of methods, based on problem structuring has become a significant concern for researchers. The study addresses this need by conducting a Systematic Literature Review (SLR), which identified and stratified twenty-seven relevant papers according to four predefined research questions. This stratification clarified commonly used computational tools and their specific application to the proposed MCDM methods. Furthermore, the review revealed that existing tools are often tailored to individual methods, highlighting a gap in tools designed to recommend the appropriate MCDM or family of methods based on problem characteristics. Consequently, the SLR identified a clear opportunity for further research and the development of a computational tool that facilitates method selection based on the structured problem. This paper describes the development process, methodological framework, and refined analysis of the findings with a robust protocol for this SLR.

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ABSTRACT This paper aims to provide a comprehensive overview of the research developed in production scheduling by researchers affiliated with Brazilian institutions. A total of 863 articles indexed in the Scopus database over the past four decades were collected. This comprehensive dataset enabled the identification of the most prolific authors, the most prominent institutions, and the most frequently studied production environments. Additionally, various quantitative indicators were examined to discern trends within this field. This study uniquely identifies critical gaps in Brazilian production scheduling research, emphasizing areas for international collaboration and practical applications. A higher concentration of studies was observed in the Southeast region, particularly at the University of São Paulo. The flow shop production environment emerged as the most studied topic, garnering a significantly higher number of citations than other production environments.

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ABSTRACT Establishing partnerships has been a significant challenge for construction project managers. This study proposes a methodology that seeks to structure the problem of partnerships by identifying partner profiles according to the values of the project-oriented company. Based on this identification, the activities to be subcontracted will be classified according to the partnership profiles identified for the project under study, facilitating the partner selection process., facilitating the partner selection process. A case study is conducted on a highway project. Firstly, the VFT method was applied to structure the problem and support identifying the objectives and criteria to be considered when establishing partnerships. Secondly, the PROMSORT method was used to sort the potential activities to be subcontracted. By applying the methodology proposed in the case study, the company defined partnership profiles for which the manager established distinct strategies for selecting partners. As a result, there was a significant improvement in the performance of the organization’s subcontracting processes, improved the quality of partnerships, and structured the subcontracting sector.

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ABSTRACT This study presents an approach to assess the prosperity of countries by integrating the K-means clustering algorithm with the ELECTRE III multi-criteria ranking method. Using data from the Prosperity Index compiled by the Legatum Institute, 167 countries were analyzed considering 12 indicators. Although the Prosperity Index itself provides a composite ranking, by averaging the 12 indicators, integrating K-means with ELECTRE III adds significant value by uncovering nuanced groupings among countries based on their multifaceted performance. This combination enables policymakers to better understand overall rankings and specific patterns of strengths and weaknesses among similar countries. The approach proposed in this study aims to provide insights through visualizations of country clusters, preference relations established by ELECTRE III, and the resulting rankings. In addition, a robustness analysis was conducted using Monte Carlo simulations, with 10,000 runs performed to explore variations in the ELECTRE III parameters through probability distributions. The approach was implemented considering the scenarios of 2019 and 2023, aiming to capture the circumstances before and after the COVID-19 pandemic. The results indicate that developed countries were allocated to preferred clusters in both years analyzed. However, when applying the approach to assess the variation between the two periods, these countries were assigned to the lowest-ranked cluster. Practically, this methodology can assist policymakers in identifying critical areas for improvement, detecting performance shifts over time, and recognizing peer nations with similar prosperity profiles for potential benchmarking and collaborative development initiatives.

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ABSTRACT Electric vehicles (EVs) offer a strategic solution to environmental challenges by combining energy efficiency with reduced emissions. This study conducts a bibliometric analysis and technological mapping of the sector, comparing Brazil’s position to that of leaders such as China, the United States, and Germany. The objective is to identify trends in the national development of EVs, with a focus on scientific and technological production. The methodology involves collecting and analyzing data from bibliographic databases (Web of Science) and patent databases (Derwent Innovation Index), encompassing scientific publications and technological records. The central issue is Brazil’s low scientific and technological output, despite its renewable energy potential, which reflects a dependence on foreign technologies and a lack of integration among academia, industry, and government. The rationale for this study is rooted in the need to understand how Brazil can position itself competitively in the global EV market, considering its clean energy matrix and programs like Rota 2030. The results indicate that China leads in battery technology and infrastructure, while Brazil showcases low scientific output and a reliance on foreign technology. Despite its renewable energy matrix and initiatives like Rota 2030, there is an absence of integrated policies for innovation. It is concluded that Brazil must strengthen its scientific research, integrate academia with the productive sector, and invest in infrastructure to compete in the global electric vehicle market.

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ABSTRACT Based on the compounding mechanism, a unique discrete probability distribution is investigated in this paper. The Poisson distribution is mixed with a lifetime model called as the Fav-Jerry model. The important statistical properties are investigated and inferred. Further, the Actuarial measures have also been studied. The Bayesian estimation approach and the maximum likelihood method are used for the estimation process. Using a Monte-Carlo simulation technique, the behavior of the maximum likelihood estimate is examined. Additionally, when data is reproduced from different competing models, the model compatibility is being examined. The study further introduces a novel count data regression model for overdispersed data by assuming that the response variable follows the Poisson mixture of Fav-Jerry model. Various performance criteria are considered and real-world data sets are used to investigate the model’s applicability in real life. The outcomes are juxtaposed with few potentially intriguing models. The real world application of the regression model is also investigated.

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ABSTRACT Academic spin-offs (ASOs) are an important channel for technology transfer in universities and are created with the participation of researchers or inventors. However, their development and market success require actions and resources from various stakeholders, making the process challenging. This study proposes a structured model for technology transfer through ASOs in public universities, using a case study at the Federal University of Mato Grosso do Sul (UFMS). The research follows the guidelines of the Soft Systems Methodology (SSM) to structure the problem and define a set of essential activities for the creation and consolidation of spin-offs. The main results include a list of key actions for implementing the process and a detailed model for creating ASOs at UFMS, aiming to strengthen innovation and academic entrepreneurship.

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ABSTRACT This work introduces the Gonzaga’s problem as potential test case for evaluating infeasibleinterior-point algorithms in linear programming. The Gonzaga’s problem is a simple linear programming instance with two decision variables and a non-interior starting point. The study explores the impact of adjusting the optimality parameter µ to achieve algorithms with complexity O(nL)-iterations. Two classes of algorithms, central surface and homogeneous and self-dual, are implemented and presented graphically. Results show that adjusting µ can sometimes improve efficiency, but the lack of theoretical validation remains a challenge. The work suggests that the Gonzaga’s problem can serve as a benchmark for developing and testing new infeasible-interior-point algorithms.

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ABSTRACT This research presents a comprehensive empirical assessment of Artificial Intelligence (AI) adoption in the India’s Banking, Financial Services, and Insurance (BFSI) sector, examining its operational transformation and strategic implications. Through systematic analysis of current AI implementations, this study investigates how financial institutions are leveraging AI technologies to enhance operational efficiency, improve customer experience, and strengthen risk management frameworks. The research employs a mixed-methods approach, combining quantitative analysis of industry data with qualitative assessment of AI adoption patterns across 128 responding Indian BFSI institutions from a sample frame of 165 institutions during January 2023 March 2024, achieving a 77.6% response rate. Key findings reveal that 69% of Indian banks have implemented AI/ML solutions, resulting in 30-40% reduction in operational costs and 50% improvement in processing times. The study identifies critical challenges including data privacy concerns (cited by 62% of institutions), skill gaps in AI expertise (70% of institutions), and regulatory compliance complexities. This research contributes to operations research literature by providing empirical evidence of AI’s transformative impact on financial services operations and proposing a framework for strategic AI implementation in emerging economies.

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ABSTRACT This article addresses the high time and cost associated with life testing by proposing a repetitive acceptance sampling plan for time-truncated life tests, specifically for the Exponentiated Frechet Distribution (EFD) with known shape parameters. The objective is to minimize the average sample number (ASN) while ensuring that the lot acceptance probabilities meet predefined quality levels. The plan characteristics, such as sample size and acceptance number, are determined based on the test’s median lifetime as a critical quality criterion. The proposed methodology is compared with the traditional single sampling plan, demonstrating its efficiency in reducing the required sample size. Additionally, a real-life data are used to illustrate the application of the method, providing practical insights. Tables are presented for various values of the shape parameters to facilitate implementation. The study contributes a novel approach to improving efficiency in life testing while maintaining reliability. The results shows that the proposed repetitive acceptance sampling plan reduces sample sizes. This consistent reduction highlights the superiority of the proposed method in terms of cost and efficiency.

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This paper proposes a complementary approach to assess the performance of Industrial Engineering undergraduate programs within the Brazilian Higher Education System. This approach presents an alternative Composite Indicator formulation derived from a Data Envelopment Analysis (DEA) model and the Benefit-of-Doubt (BoD) approach. This method enhances comparability among programs across diverse Higher Education Institutions (HEIs) by optimizing weights and incorporating weight restrictions. The application of the novel approach was conducted with the analysis of 73 undergraduate programs within the Brazilian Federal Public System in 2019, the study identified both programs with suboptimal performance levels and benchmark programs showcasing best practices. This feature of the BoD model can support the HEIs assessed as suboptimal in pursuing improvement opportunities in a benchmarking exercise. The incorporation of Weight Restrictions (WRs) enables a more comprehensive consideration of key performance indicators (KPIs) within the Preliminary Program Grade (CPC) framework, thereby revealing additional improvement opportunities. This research contributes to the field by integrating DEA and BoD into the official STEM performance criteria framework for the first time, offering insights into evaluating undergraduate Industrial Engineering programs within Brazilian HEIs.

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ABSTRACT Quadratically constrained quadratic programming (QCQP) problems appear in a wide range of engineering fields, including computer science, communication engineering, and finance. A key difficulty in solving these problems lies in efficiently finding global solutions, especially for large-scale and nonconvex instances. To address this, Wen & Yin (2013) proposed a method that reformulates semidefinite programming (SDP) relaxations of QCQPs into a nonlinear and nonconvex low-rank problem. This reformulated problem can be efficiently solved using a curvilinear search method combined with Barzilai-Borwein (BB) steps, known as the CSBB algorithm. In this study, we compare two approaches for solving QCQPs: the conventional convex SDP relaxation and the nonconvex low-rank reformulation introduced by Wen and Yin. We propose a set of general QCQP models that are compatible with the low-rank framework and conduct a series of numerical experiments to evaluate their performance. This study evaluates performance using several classes of NP-hard problems, including Binary Integer Quadratic (BIQ) problems, Max-Cut problems, Boolean Least Squares (BLS) problems, and 0-1 Quadratic Knapsack Problems (QKP). The results demonstrate that the low-rank approach offers competitive performance and shows strong potential for solving large-scale QCQPs more efficiently than traditional convex methods.

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ABSTRACT This study examines the application of information criteria in the selection of multilevel structural equation models (MSEM), analyzing different estimation methods (ULS, GLS, and ML) and statistical criteria (AIC, BIC, BCC, and CAIC). The study defines MSEM and specifies fit functions for different estimation methods. Using Monte Carlo simulations, the analysis evaluates the performance of these criteria in model selection under various data heterogeneity and distribution scenarios. The results demonstrate that the choice of information criterion can significantly influence both variable selection and the interpretation of model results. The study concludes that a comprehensive understanding of the behavior of information criteria can help researchers select and interpret the most appropriate models for hierarchical data, recommending a combined approach tailored to the specific objectives of the analysis.

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ABSTRACT In this manuscript, our aim is to describe the concept of intuitionistic fuzzy b-metric-like spaces. We investigate some properties of this new space and give some useful examples. We also define the concepts of convergence, Cauchy sequence and completeness in intuitionistic fuzzy b-metric-like space. Furthermore, we establish some fixed point theorems in this setting and give an application to integral equations to illustrate the usability of the obtained results.

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ABSTRACT The aim of this study is to investigate the field of supply chain (SC) in small and medium businesses (SME) over the past 10 years by means of a bibliometric analysis of papers released in Scopus-indexed journals. The approach applied is bibliometric analysis and scientific mapping. This study utilizes bibliographic data of indexed papers retrieved from the Scopus database. This work detects research clusters for topological analysis, identification of important research subjects, interrelations, and cooperation patterns using bibliometric techniques. According to the results, 804 papers were published between 2015 and 2024; a total of 12,109 citations were made to these papers, and with an average of 96.80 citations per year. With 2,293 authors from 72 countries having published works, co-authorship and international cooperation have grown. The leading publishing countries for SC in SME in Scopus-indexed journals include China, Indonesia, and India. This study solely covers papers from 2015 to 2024; we did not examine publications released prior to 2015. This study solely examined bibliographic data of publications; it did not examine the contents of papers, including titles, approaches, or other components. It also aids in identifying present areas of interest for research and possible paths of inquiry.

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ABSTRACT In this study, we propose an exact method for optimizing a linear function over the efficient set of a multi-objective transportation problem (MOTP). This type of problem arises when a decision maker needs to optimize a preference function across numerous efficient solutions. We developed a cutting plane method that iteratively adds efficient cuts to the relaxation of the original problem until a feasible and optimal solution is achieved. The method is enhanced by a lower bound, enabling a more efficient search for the optimal solution.

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ABSTRACT This paper proposes an exact method to solve an integer indefinite quadratic bilevel problem with multiple objectives at the upper level, where the objective functions at both levels are a product of two linear functions. The suggested algorithm uses a branch and cut algorithm based on a multiobjective integer linear problem obtained by replacing the indefinite quadratic objectives of the upper level by their two linear functions and the classical branch and bound technique for integer decision variables. Then, the integer solutions obtained are tested for optimality of the lower level problem by using a library IBM CPLEX 12.8 for C++ programs. The integer indefinite quadratic bilevel programming problem with single objective at both levels is solved in the first step, based on the dantzig cut. The second phase explorates with the efficient cut to provide the set of efficient solutions without listing the whole integer domain. After the presentation of the algorithm, a numerical example and computational experiments are provided.

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ABSTRACT Problem: The project management structure faces significant challenges in supporting the high demand for innovation projects. This creates a dilemma between providing personalized support for each proposal, which is unfeasible for a limited-resource Project Management Office (PMO), and adopting generic approaches that fail to meet all initiatives adequately. Objective: This study aims to develop a method for grouping projects based on their shared characteristics to effectively address PMO management actions for each grouping. Method: The research analyzes data from a 2018 experience at the National Cancer Institute (INCA), which aimed to formulate a cooperation agreement using the elevator pitch technique. Although the initiative was not completed, the data collected was valuable for testing the proposed techniques. A procedure combining the elevator pitch technique with k-means clustering and the Analytic Hierarchy Process (AHP) was applied to evaluate 48 project proposals, based on 186 three-minute presentations evaluated by five panels across five criteria. Findings: The k-means clustering revealed distinct performance groups and intra-group similarities. Clustering with distinct criterion weights enhanced the discrimination of group performances regarding ”technical feasibility,” allowing for targeted actions instead of generic approaches. Lessons learned were also documented, confirming successful strategies and identifying areas for improvement. Value: This study offers a transparent, results-oriented decision-making procedure for PMOs, promoting tailored actions and facilitating organizational learning in project selection. It also fosters the development of solutions to complex challenges, contributing to more efficient resource allocation in projects.

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ABSTRACT This study evaluates ensemble models for wind energy forecasting in southern Brazil, the country’s second-largest wind energy producer. Forecasting is essential due to complex wind patterns and the region’s seasonal variability, which can impact wind farms and raise costs by requiring less sustainable energy sources. Monthly data were analyzed to forecast power generation over a 12-month horizon, comparing hybrid models with the individual models of Holt-Winters Additive (HW-A), Multiplicative (HW-M), and Simple Recurrent Neural Networks (RNN). Using the Mean Absolute Percentage Error (MAPE) criterion, the hybrid Holt-Winters Additive with Multiplicative (HW-AM) achieved the best accuracy at 7.67, followed by HW-AM combined with a two-layer neural network (HW-AM-RNN2) at 8.38 and a singlelayer neural network (HW-AM-RNN1) at 8.46. These predictive models provide critical tools for decision makers as they enable accurate production estimates and identify periods when other energy sources may be needed.

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ABSTRACT The rapid growth of the Internet of Things (IoT) has intensified security challenges, particularly for devices with limited computational and energy resources. Symmetric encryption, due to its efficiency and adaptability, has emerged as a crucial solution for safeguarding IoT environments. This study conducts a bibliometric analysis of 130 peer-reviewed publications indexed in the Web of Science (2011- 2024), mapping the intellectual structure, emerging themes, and influential contributors in symmetric cryptography for IoT. Using VOSviewer and Biblioshiny, the results highlight China and India as leading contributors and identify key research areas such as lightweight encryption, hardware-optimized cryptographic protocols, and blockchain-based security solutions. Additionally, the findings underscore increasing interest in AI-driven cryptographic models and scalable key management strategies. Despite providing valuable insights, the study acknowledges limitations in database scope and methodological depth. Future research is recommended to expand data sources, explore hybrid cryptographic approaches, and design intelligent, energy-efficient security frameworks tailored to evolving IOT needs.

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ABSTRACT The NP-hard Two-Dimensional Strip Packing Problem (2SPP) demands efficient exact solutions. This paper presents SAT-based Order Encoding models incorporating item rotation and adapted symmetry-breaking (SB). It compares three height-minimization strategies: Non-Incremental SAT Bisection, Incremental SAT Bisection, and Direct MaxSAT Optimization. Benchmarks on established 2SPP instances show our Non-Incremental SAT and Direct MaxSAT strategies significantly outperform earlier incremental techniques. Direct MaxSAT excelled for non-rotational 2SPP, while Non-Incremental SAT was superior for rotational cases among our methods. Google CP-SAT, integrated into our Non-Incremental Bisection search framework, performed best overall. Nevertheless, our specialized SAT/MaxSAT methods were highly competitive, outperforming other CP and MIP solvers. SB was generally beneficial. Item rotation increased encoding complexity; while yielding improved optimal heights for some instances, it did not increase the total number of instances solved optimally by our SAT/MaxSAT methods. This work offers insights into SAT/MaxSAT strategy trade-offs and their performance against general solvers.

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ABSTRACT This study presents a systematic literature review on humanitarian operations, with emphasis on resource optimization and logistics in forced displacement scenarios. Following a rigorous review protocol, it applies bibliometric analysis to examine operations research contributions, methodologies, and research gaps, particularly in resource allocation, facility location, and humanitarian logistics. The review highlights recent studies using mathematical models and decision support tools, demonstrating the effectiveness of optimization algorithms in improving emergency response. Limited to articles published between 2020 and 2024, the study offers a comprehensive overview that bridges theory and practice, providing valuable insights for researchers and practitioners seeking to enhance humanitarian operations.

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ABSTRACT This paper consolidates four extensive bodies of literature work-life balance (WLB), work-life integration (WLI), workplace spirituality (WS) and employee engagement (EE) into a single, portable process model for sustaining human energy and performance. Using a PRISMA-2020 guided search and screening of twenty studies published between 2020 and 2025 across the domains of hospitality, healthcare, banking/finance, technology and higher education, it conducts a concept-centric synthesis. The model identifies two upstream levers: values alignment (WS embedded within an ethical climate of shared norms and fair process) and boundary quality (WLB guardrails plus the quality of WLI governed by boundary control, not flexibility alone). These levers operate through carriers of employee engagement with job satisfaction in parallel to predict well-being, performance and retention. The findings clarify construct boundaries (separating WLB guardrails from WLI interfaces), explain heterogeneous effects via a single moderator (control, combining job and boundary control), and translate this into an actionable playbook: codify ethical climate, govern connectivity (e.g., core hours, meeting discipline, right-to-disconnect) and investment in caring Human Resources Management (job design, development, supervisor support), while tracking engagement and satisfaction as leading indicators. An original feature of this theory-only, sector-agnostic framework is that it is parsimonious enough for operational use and precise enough for multilevel mediation tests. A research agenda calling for longitudinal and field-experimental evaluations of boundary norms and AI-related contract shifts closes the study.

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ABSTRACT This research describes a fundamental step in the study of the vital dynamics of dengue in the municipality of Seropédica, Rio de Janeiro: the development and verification of the Finite Element Method (FEM), combined with the Crank-Nicolson method and the Newton method. The simulation is described by the MSIR (Mosquito-Susceptible-Infected-Recovered) model, characterized by a system of nonlinear partial differential equations. The implementation and verification of the problem consisted of constructing a test problem with a known analytical solution. It was successfully verified, demonstrating that the maximum absolute error gradually decreases with the refinement of the spatial mesh, reaching an order of 10-4 to 10-6, depending on the variable.