Abstract
In this paper, a decision support application, for the air cargo planning and distribution, is proposed. The freight forwarding sector has been working to be assertive and efficient in responding to the market through an efficient approach to planning and allocation problems. The main goal is to minimize costs and improve performance. A real air cargo distribution problem for a freight forwarder was addressed. This project emerged from the need to efficiently plan and minimize costs for the distribution of thousands of m(3) (cubic meters) of air cargo, while considering the market restrictions, such as aircraft availability and transportation fees. Through the GRG algorithm adaptation to the real problem, it was possible to respond to the main goal of this paper. The development of an easy-to-use application ensures a quick response in the air distribution planning, focusing on cost reduction in transportation. With the application development it is possible to obtain real earnings with immediate effect.

Abstract
The present study consists in the comparison of two metaheuristics in a scheduling problem (SP), in particular in the minimization of the makespan in flowshop problem. The two selected metaheuristics were DABC (Discrete Artificial Bee Colony) and ACO (Ant Colony Optimization). For the performance analysis, the metaheuristics were tuned with an extensive DOE study, subsequently, several tests were performed. Thirty-one evenly distributed instances were generated for a in-depth analysis and each one was subjected to three runs for each metaheuristic. Through the results obtained, it was possible to concluded that the DABC has a better performance when compared to SA and ACO. SA and ACO have a similar performance in the chosen problem. These conclusions were supported by descriptive statistics and statistical inference. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
In the context of the COVID-19 pandemic, public spaces had to be quickly adapted to the new circumstances especially under the uncertainty of the pandemic development. Door handles are some of the most touched surfaces and so, this point of contagion was chosen to be tackled and two solutions were developed that would prevent direct touch with the handle: a portable and a fixed device. The portable device (HYHOOK + HYTIP) is a hook-like device holding a finger cover, which permits to open doors and push buttons safely. The fixed device (HANDGENIC) is meant to be assembled in door handles to equip buildings, such as universities or schools. With the fixed device, the user can open the door using their forearm which makes them less likely to transfer any particles to eyes, nose or mouth. The 3D printing Fused Filament Fabrication (FFF) process was selected as manufacturing technique, which allows the fast production of prototypes. This work portrays the development process and design iterations taking into consideration the concerns about the functioning of the devices and possible failures or alternative uses. To assure structural integrity of the parts, finite element (FE) analysis was used to verify its mechanical response. As conclusion, it was found that FE analysis indicate that the devices are structurally sound to be used in public spaces and that 3D printing is a useful way to rapidly develop devices while testing several design possibilities.

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.