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.

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.

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.

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.

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%.

ABSTRACT This paper highlights the challenges and opportunities introduced by disruptive technology for organizations. The convergence of emerging technologies holds the potential to enhance logistics and supply chain decision-making through heightened automation. The paper aims to conduct a multi-criteria assessment of readiness for implementing disruptive technology in supply chain management. The proposed approach employs the multi-criteria decision analysis methodology to assess organizational readiness and technological readiness. The approach suggests a procedure involving an analysis of benefits, opportunities, costs, and risks. It prioritizes and ranks factors associated with both organizational and technological readiness for the implementation of disruptive technology in supply chain management. Additionally, it introduces a supply chain risk response framework to tackle challenges related to readiness. Supply chain management and disruptive technology are expanding fields that provide significant opportunities for exploration. One of these opportunities involves assessing readiness from multiple perspectives, as presented in this paper, contributing to the literature.

ABSTRACT The study employed the THOR-2 method to select a federal unit (UF) in Brazil’s Midwest and South regions for investments in biodiesel production, considering ESG criteria. Evaluating six criteria, such as actual production and installed capacity, it was identified that Rio Grande do Sul stood out as the best option, for its established capacity, operational efficiency and growth potential. The ranking of UFs highlighted the importance of multi-criteria-based choices, aiming to balance economic impacts with social and environmental benefits. Thus, the study provided a framework for choosing a UF for biodiesel investments and integrated ESG considerations, contributing to sustainability in the energy sector.

ABSTRACT Diabetes represents a significant global public health challenge. Effective diabetes management depends on adopting nutrient-controlled diets. This paper proposes a multi-objective mathematical optimization model for crafting appropriate diets for individuals with diabetes and ensuring adherence to nutritional requirements. Integrating integer variables, the model prioritizes minimizing carbohydrate and lipid consumption, recognizing the inherent trade-offs between these objectives. Balancing these objectives is critical considering the potential adverse health effects of excessive fat intake. In addition, the model facilitates the structure of the meal-based diet and enforces dietary diversity, promoting patient adherence while meeting vital nutritional criteria for diabetes control.

ABSTRACT The main goal of this study is to determine the relative efficiency of the application of Lean Healthcare, based on a set of Brazilian public hospitals, using Data Envelopment Analysis (DEA). The study methodology consisted of establishing na analysis of the efficiency of Lean Healthcare, comparing the hospitals that applied it before and after, establishing the time and displacement as inputs and value as output, expressed in number of hospitalizations, to simulate the development conditions of the Lean in health. For this, the non-oriented SBM-DEA model was used, simulating the conditions of the medical clinics of the established DMUs. As a result, it was possible to observe that the hospitals were all inefficient before the application of Lean and had na improvement in efficiency after the application, but only a part actually became efficient. Thus, it is concluded that a metric that aligns the development of the Lean Healthcare application with DEA may be interesting, given the possibility of improvement arising from the benchmarks for the group of hospitals that remained inefficient.

ABSTRACT We present a novel approach to the inverse data envelopment analysis (DEA) process, based on the slacks-based measure (SBM) model in the presence of undesirable outputs. In the first step, we determine the minimum undesirable outputs levels that a decision-making unit (DMU) can produce, according to the level of inputs and desired outputs, based on weak disposability. We then introduce counterpart (hypothetical) units corresponding to each DMU. In the second step, we determine the optimal level of inputs and outputs for the newly created units, based on the SBM model in the presence of undesirable outputs. We present two new criteria models based on the SBM model, to compare the efficiency of the newly created unit in the inverse DEA process with the original unit. We demonstrate that the efficiency scores obtained from these two models are equal. Using the SBM-based inverse non-radial DEA process, we can use all inefficiency slacks related to all input and undesirable output components in the inverse non-radial DEA process to estimate inputs and outputs simultaneously.

ABSTRACT This paper describes the psychometric validation procedure for the creation of an Artificial Intelligence (AI) engine based on the theory of precision psychology and expert system-type machine learning algorithms. The focus is on individual coping strategies and the factor of reconciliation in victims of armed conflict in Colombia. A non-experimental methodology with a cross-sectional design was used, with a total of 363 participants. The results highlight that elements related to economic factors, individual condition, and political concerns in the victims explain the conditions required to generate a reconciliation process in the context of reintegrating into civilian life. It is concluded that the use of artificial intelligence enhances the process of psychological care by providing precision in the etiological information of the illness and the data, which enables discrimination between the health status, symptoms, and disease of the victims.

ABSTRACT This research evaluates the quality of life of the ninety-two cities in the state of Rio de Janeiro using a non-compensatory composite aggregator index. It was a multimethodological research, which combines a qualitative conceptual map with a quantitative multicriteria model to assess variables of difficult definition. The criteria are based on Maslow’s concepts, which also define the weights between them, bringing more coherence to the index. We conclude that our measurement model with non-compensatory rationality offers an alternative for calculating the quality of life. The cities of Niteroi, Rio de Janeiro and Volta Redonda scored best in our proposed index. Silva Jardim, Sumidouro and São Francisco de Itabapoana scored worst. Niteroi scored best in Physiological and Esteem needs. Nova Friburgo scored best in Safety needs. Mesquita scored best in Love and belonging needs. A sensitivity analysis shows that our method’s structure makes results distortion difficult.

ABSTRACT Facility location-related decision problems pose a significant challenge for managers due to the multiple and conflicting factors involved. Moreover, incorrect decisions can lead to substantial impacts on companies' long-term strategic planning, resulting in losses for the business. This paper deals with a facility location problem in the educational sector in the northeast region of Brazil, which concerns the definition of the best location to place a private sector technical school, considering multiple objectives throughout a multicriteria approach. The decision-making process is structured based on a 9-step multicriteria model, and the preference elicitation phase is aided by the Flexible and Interactive Tradeoff (FITradeoff) method, using partial information provided by the decision-maker (DM). In the application presented in this paper, preference modeling is conducted considering the combination of two preference elicitation paradigms in the FITradeoff decision process: elicitation by decomposition and holistic evaluations, throughout a flexible approach. The DM’s preferences are elicited interactively, by means of a Decision Support System (DSS). At each interaction, the information given by the DM acts as constraints for a linear programming problem (LPP) model, which is computed in order to verify the potential optimality of each alternative. At the end of the elicitation process, a sensitivity analysis is performed so as to verify the robustness of the results obtained. Insights on the preference modeling paradigms combination with potential advantages for the decision process are also discussed in this paper.

ABSTRACT Uncertainty and disruption can significantly impact supply chains, and risk response is a means to diminish or circumvent these challenges. Therefore, this investigation aims to assess lead time variability in the supply chain of active ingredients. The research proposes a decision-making process to evaluate lead time variability in the active ingredient supply chain of the Brazilian chemical industry. It adopts Game Theory and linear programming in its solution. The two major contributions of this study are as follows: first, it presents a procedure to assess lead time variability; second, it proposes a supply chain risk response framework to address the lead time variability problem. Supply chain and lead time variability are emerging areas that offer significant opportunities for exploration, one of which is to evaluate lead time variability from the multiple perspectives presented in this study, thereby contributing to the current literature through a real application.

ABSTRACT This paper introduces the Group Extended Entropy VIKOR (GE2-VIKOR) MCDM approach for assessing defense technology criticality in developing countries. GE2-VIKOR addresses uncertainty and complexity in this scenario by leveraging VIKOR’s ability to propose compromise solutions in discrete alternatives’ scenarios. GE2-VIKOR can be applied in both individual and group decision-making, incorporating weights based on decision-makers’ knowledge degree and criteria weights determined by the sponsor or Shannon’s entropy theory. The method aims to offer a compromise solution that maximizes group utility while minimizing individual regret under majority, consensus, or veto rules. A detailed demonstration illustrates GE2-VIKOR’s applicability and effectiveness in assessing defense technology criticality in developing countries. The results highlight the method’s strengths and limitations and suggest future research avenues, particularly exploring its applicability in developed countries, where technology criticality concepts differ.