Abstract
In this article the application of the discrete version of the bat algorithm to flowshop scheduling problems is presented and compared with Simulated Annealing, Local Search, as well as versions of each that start from constructive heuristics (Palmer and CDS). Bat algorithm is a novel metaheuristic, developed for continuous problems that has shown exceptional results. This paper intends to assess its effectiveness and efficiency for discrete problems when compared with other optimization techniques, including Simulated Annealing and Local Search, whose results are already proven. First, it was developed a literature review about those algorithms, then they were implemented in VBA with Microsoft Excel. Once implemented, the parameterization was carried out, ensuring an adequate application of the algorithms before they can be compared. Then, the methods were applied for 30 normally distributed instances, in order to draw broader conclusions. Finally, a statistical evaluation was carried out and concluded the inferiority of the Local Search in relation to the metaheuristics and the superiority of the hybrid version of the Bat Algorithm with CDS in relation to Simulated Annealing, with significantly better solutions, in an equal computation time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Meta-heuristics are some of the best-known techniques to approach hard optimization problems, however, there are still questions about what makes some meta-heuristics better than others in a specific problem. This paper presents an analysis of the Firefly and Cuckoo Search Algorithm, such as others meta-heuristics. In order to assess the performance of the Firefly Algorithm and the Cuckoo Search Algorithm, they were compared with other well-known optimization techniques, such as Simulated Annealing and Local Search. Both meta-heuristics analysed in an in-depth computational study, reaching the conclusion that both techniques could be useful in Scheduling Problems and lead to satisfactory solutions quickly and efficiently. Moreover, the results of the analysis show that the Firefly Algorithm, despite having a high runtime, performs better than the other techniques. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
In today's competing business market, companies are constantly challenged to dynamically adapt to customer expectations by diminishing the time response that goes from the beginning of the business opportunity to the satisfaction of the customer need. Simultaneously, there is increased recognition of the advantages that companies obtain in focusing on their core business and seeking other competencies through partnerships with other partners by forming collaborative networks. These new collaborative organizational structures require a new set of methods and tools to support the management of manufacturing processes across the entire supply chain. The present paper addresses the collaborative production planning problem in networks of non-hierarchical, decentralized, and independent companies. By proposing a collaborative planning intelligent framework composed of a web-based set of methods, tools, and technologies, the present study intends to provide network stakeholders with the necessary means to responsively and efficiently address each one of the market business opportunities. Through this new holistic framework, the managers of the networked companies can address the challenges posed during collaborative network formation and supply chain production planning.

Abstract
In the last few years, the industrial, scientific, and technological fields have been subject to a revolutionary process of digitalization and automation called Industry 4.0. Its implementation has been successful mainly in the economic field of sustainability, while the environmental field has been gaining more attention from researchers recently. However, the social scope of Industry 4.0 is still somewhat neglected by researchers and organizations. This research aimed to study Industry 4.0 and sustainability themes using data science, by incorporating open data and open-source tools to achieve sustainable Industry 4.0. To that end, a quantitative analysis based on open data was developed using open-source software in order to study Industry 4.0 and sustainability trends. The main results show that manufacturing is a relevant value-added activity in the worldwide economy; that, foreseeing the importance of Industry 4.0, countries in America, Asia, Europe, and Oceania are incorporating technological principles of Industry 4.0 in their cities, creating so-called smart cities; and that the industries that invest most in technology are computers and electronics, pharmaceuticals, transport equipment, and IT (information technology) services. Furthermore, the G7 countries have a prevalent positive trend for the migration of technological and social skills toward sustainability, as it relates to the social pillar, and to Industry 4.0. Finally, on the global scale, a positive correlation between data openness and happiness was found.

Abstract
There are Optimization Problems that are too complex to be solved efficiently by deterministic methods. For these problems, where deterministic methods have proven to be inefficient, if not completely unusable, it is common to use approximate methods, that is, optimization methods that solve the problems quickly, regardless of their size or complexity, even if they do not guarantee optimal solutions. In other words, methods that find acceptable solutions, efficiently. One particular type of approximate method, which is particularly effective in complex problems, are metaheuristics. Particle Swarm Optimization is a population-based metaheuristic, which has been particularly successful. In order to broaden the application and overcome the limitation of Particle Swarm Optimization, a discrete version of the metaheuristics is proposed. The Discrete Particle Swarm Optimization, DPSO, will change the PSO algorithm so it can be applied to discrete optimization problems. This alteration will focus on the velocity update equation. The DPSO was tested in an instance of the Traveling Salesman Problem, att48, 48 points problems proposed by Padberg and Rinaldi, which showed some promising results.