The traveling salesman problem (TSP) is one of the best-known combinatorial optimization problems. Many methods derived from TSP have been applied to study autonomous vehicle route planning with fuel constraints. Nevertheless, less attention has been paid to reinforcement learning (RL) as a potential method to solve refueling problems. This paper employs RL to solve the traveling salesman problem With refueling (TSPWR). The technique proposes a model (actions, states, reinforcements) and RL-TSPWR algorithm. Focus is given on the analysis of RL parameters and on the refueling influence in route learning optimization of fuel cost. Two RL algorithms: Q-learning and SARSA are compared. In addition, RL parameter estimation is performed by Response Surface Methodology, Analysis of Variance and Tukey Test. The proposed method achieves the best solution in 15 out of 16 case studies.

Reinforcement learning is an important technique in various fields, particularly in automated machine learning for reinforcement learning (AutoRL). The integration of transfer learning (TL) with AutoRL in combinatorial optimization is an area that requires further research. This paper employs both AutoRL and TL to effectively tackle combinatorial optimization challenges, specifically the asymmetric traveling salesman problem (ATSP) and the sequential ordering problem (SOP). A statistical analysis was conducted to assess the impact of TL on the aforementioned problems. Furthermore, the Auto_TL_RL algorithm was introduced as a novel contribution, combining the AutoRL and TL methodologies. Empirical findings strongly support the effectiveness of this integration, resulting in solutions that were significantly more efficient than conventional techniques, with an 85.7% improvement in the preliminary analysis results. Additionally, the computational time was reduced in 13 instances (i.e., in 92.8% of the simulated problems). The TL-integrated model outperformed the optimal benchmarks, demonstrating its superior convergence. The Auto_TL_RL algorithm design allows for smooth transitions between the ATSP and SOP domains. In a comprehensive evaluation, Auto_TL_RL significantly outperformed traditional methodologies in 78% of the instances analyzed.

Mediante a obrigatoriedade de contratação de pessoas com deficiência advinda com a Lei de Cotas, o processo de inclusão de pessoas com deficiência (PcD) nas organizações brasileiras tem sido permeado por desafios, tanto para as empresas quanto para as próprias PcD. Este trabalho foi desenvolvido com o propósito de verificar a influência da percepção de suporte social e de suporte organizacional no significado do trabalho para PcD. As investigações foram realizadas com uma amostra de 169 pessoas com deficiência que trabalham em organizações de diversos segmentos nas diferentes regiões do Brasil. Concluiu-se que as categorias que melhor descrevem o significado do trabalho concreto das pessoas com deficiência estão associadas à garantia de condições econômicas de sobrevivência e de sustento pessoal e familiar, da estabilidade no emprego, do salário e do progresso social. As percepções de 'Suporte Social no Trabalho' e de 'Suporte Organizacional' são variáveis preditivas do Significado do Trabalho.

The expectation–maximization (EM) algorithm, often used for maximum likelihood estimation, has not seen much application in addressing high-dimensional regularization challenges within linear mixed-effects models. This study introduces the EMLMLasso algorithm, which merges the EM algorithm with the widely used and efficient R package glmnet, enabling Lasso variable selection for fixed effects in such models. We thoroughly evaluate its performance, comparing it to two existing algorithms from R packages glmmLasso and splmm. Our findings, based on simulations and real-world cases, demonstrate the robustness and effectiveness of our approach, even when the number of predictors (p) exceeds the number of observations (n). Notably, across most scenarios, the EMLMLasso algorithm consistently outperforms both glmmLasso and splmm. Moreover, our method is versatile and straightforward to implement, with the potential for extensions to include ridge and elastic net penalties in linear mixed-effects models.

This study aimed to identify the predictive power of the Work Design (WD) variables on Burnout Syndrome (BS). The sample consisted of 300 professionals, 188 from the field of education and 112 from health care. Two instruments were used in this research, a reduced version of the Work Design Questionnaire (WDQ) and the Brazilian version of the Spanish Burnout Syndrome Inventory (SBI). The results showed that in both professional categories, the factors of work design are predictors of the 4 dimensions of BS, especially factors related to social and task characteristics. In the education field, characteristics of knowledge required by work appear as predictors of only psychological exhaustion; and in health care professionals, as predictors of psychological exhaustion and decreased Enthusiasm towards the job. Implications of these findings will be discussed in terms of their applicability in interventions to prevent BS based on work redesign actions.