The integration between production and distribution to minimize total elapsed time is an important issue for industries that produce products with a short lifespan. However, the literature focus on production environments with a single stage. This paper enhances the complexity of the production system of an integrated production and distribution system by considering flowshop environment decisions integrated with a vehicle routing problem decision. In this case, each order is produced in a permutation flowshop subsystem and then shipped to its destination by a capacitated vehicle, and the objective is to sequence these orders to minimize the makespan of the schedule. This paper uses two approaches to address this integrated problem: a mixed-integer formulation and an Iterated Greedy algorithm. The experimentation shows that the Iterated Greedy algorithm yields results with a 0.02% deviation from the optimal for problems with five jobs, and is a viable option to be used in practical cases due to its short computational time.

We investigated a variant of the customer order scheduling problem taking into consideration due dates to minimize the total tardiness. Since the problem under study is NP-hard, we propose an efficient size reduction algorithm (SR). We perform an extensive computational experience and compare our proposition with JPO-20 matheuristic, the best existing algorithm for the problem under study. We use the Relative Deviation Index (RDI) and the Success Rate (SRa) as the statistical indicators for the performance measure. We must emphasize that SR presented the lowest average RDI (around 15.5 %), whereas the JPO-20 presented an average RDI approximately three times higher (around 52.5 %). Furthermore, the proposed SR presented a higher average SRa (around 66.9%), whereas the JPO-20 presented a lower average success (around 25.7%). Our proposal used a lower computational effort, resulting in a reduction for the computation times of approximately 22%. The obtained results point to the superiority of the proposed SR in comparison with the JPO-20.

This work aims to present the application of Multi-Criteria Decision-Making methods to the process of recruiting candidates for the position of project manager, considering aspects of the decision maker's preferences in uncertain and risk scenarios. Applied, descriptive and experimental, made up of the combined employment TODIM-FSE methods for multi-criteria classification of available candidates, and the method Behavioral TOPSIS, to choose the ideal project manager. The hybrid application of the Multi-Criteria Decision-Making methods TODIM-FSE, method based on Prospect Theory, and Behavioral TOPSIS, which considers the concept of loss aversion of Economic Behavior, is essentially innovative. When using TODIM-FSE and Behavioral TOPSIS, it was verified the explicit incorporation of the risk profile of the decision maker - aggressive, neutral, or conservative - in the context of aversion or propensity to the risks associated with the management of a project. Through the personal recruiting process from a large Brazilian organization, the possibility of adopting the hybrid model resulting from the combination of the two methods in a real situation was validated. Such validation allowed us to conclude that the candidates' classifications and choices, previously normally accepted, were at odds with the profile and risk propensity of the decision makers.

The generalized exponential distribution could be a good option to analyse lifetime data, as an alternative for the use of standard existing lifetime distributions as exponential, Weibull or gamma distributions. Assuming different non-informative prior distributions for the parameters of the model, we introduce a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Some numerical illustrations considering simulated and real lifetime data are presented to illustrate the proposed methodology, especially the effects of different priors on the posterior summaries of interest.

The use of saturated two-level designs is very popular, especially in industrial applications where the cost of experiments is too high. Standard classical approaches are not appropriate to analyze data from saturated designs, since we could only get the estimates of the main factor effects and we would not have degrees of freedom to estimate the variance of the error. In this paper, we propose the use of empirical Bayesian procedures to get inferences for data obtained from saturated designs. The proposed methodology is illustrated assuming a simulated data set.

In this paper, we present a Bayesian analysis of a data set selected from a Brazilian food company. This data set represents the times taken for different quality control analysts to test manufactured products arriving at the company’s quality control department. The samples selected from each batch contain mixtures of different products, which may be submitted to quality testing taking different times. From preliminary analysis of the data, it was observed that the histograms presented two clusters, indicating a mixture of distributions. A mixture of parametrical distributions was thus assumed in the presence of a covariate in order to analyze the data set and to establish standards to be used by the company for the times taken by the analysts. Inferences and predictions are obtained using a Bayesian approach with standard existing Markov Chain Monte Carlo (MCMC) methods.

Portfolio management has become a strategic activity in response to increasing organizational complexity, requiring models capable of reconciling multiple criteria, limited resources, and uncertainty. In this context, Portfolio Decision Analysis (PDA) has emerged as a well-established field within Operations Research dedicated to supporting such decision-making processes. This study presents a systematic literature review conducted in accordance with the PRISMA protocol, aiming to analyze the evolution of mathematical models applied to portfolio management. The search resulted in a final sample of 152 articles published between 1994 and 2025. The findings reveal a significant increase in publications from 2014 onward, with applications predominantly concentrated in project management and new product development (NPD), and a predominance of financial and technical-operational decision criteria. The results also indicate a methodological transition from classical optimization techniques to hybrid approaches that integrate multicriteria decision-making methods, fuzzy logic, mathematical programming, and, more recently, machine learning. The main research gaps identified include the need for broader empirical validation, the expansion of applications to different contexts, and the development of new hybrid approaches. This study provides an integrated overview of the methodological evolution of Portfolio Decision Analysis, offering a valuable reference for future research.

This study aims to analyze how the application of Scrum improves project development in companies and positively impacts the organizational environment. The research was conducted through a review of scientific articles published between 2013 and 2024, allowing the identification of the main motivators, challenges, and benefits of the agile methodology in organizations. The results show that Scrum improves internal communication, increases delivery efficiency, and enhances the quality of products and processes, delivering value to stakeholders. Furthermore, the analysis revealed that implementing the methodology depends on factors such as organizational culture, team training, and the team's understanding of agility. As a limitation, the study identified temporal variation in the articles: their distribution over the years was not homogeneous, and there was no in-depth quantitative evaluation to accurately measure the impacts of applying Scrum. Based on these conclusions, it is suggested that future research explores the integration of Scrum with other agile methodologies, conduct a comparative analysis with traditional methods, examine its application in specific sectors, and assess its impact on organizational culture.

The purpose of this article is to describe how leadership impacts knowledge management in the context of projects. To do so, a systematic literature review (SLR) was carried out using the Scopus and Web of Science databases. The research followed a structured protocol of search, selection and analysis of articles, focusing on publications that address the intersection between leadership, knowledge management and project management. The total number of articles analysed was 103. SLR revealed that leadership is a key element in project knowledge management, impacting multiple organizational levels and acting as a key enabler in creating a culture of knowledge as a strategic asset. Furthermore, different leadership styles, such as transformational leadership, contribute to knowledge-oriented leadership and have demonstrated significant effectiveness in fostering collaborative environments and knowledge sharing. The study also identified the growing role of technology in supporting knowledge management, highlighting the importance of knowledge-oriented leadership in promoting its strategic adoption. The study offers a comprehensive overview of the enablers and barriers that impact knowledge management in projects, allowing leadership and organizations to anticipate challenges and implement more effective practices. Identifying barriers helps organizations develop preventive strategies, while understanding enablers allows them to enhance successful knowledge management practices with the help of knowledge-oriented leadership.

In this paper, we address the problem of scheduling jobs in a no-wait flowshop problem with sequence-dependent setup times with the objective of minimizing makespan. This problem is well-known for being nondeterministic polynomial-time hard, and small contribution to the problem has been made. We propose a new constructive heuristic named GAPH based on a structural property. The effectiveness of the structural property is crucial given that it is responsible for 100% of the success rate of the total problems tested. The computational results demonstrate that the proposed approach is superior than three of the best-know methods in the literature such as the twos by Bianco, Dell’Olmo and Giordani (INFOR Journal: 37 (1), 3-19, 1999) and TRIPS heuristic adapted for sequence-dependent setup times objective by Brown, Mcgarvey and Ventura (Journal of the Operational Research Society, 55 (6), 614-621, 2004) in terms of the solution quality and that it requires less computational effort.