Engenharia de Produção - PPGEP-So
https://repositorio.ufscar.br/handle/ufscar/8236
2024-03-29T01:52:56ZEstudo do impacto da consideração de custos de emissão de CO2 nas decisões de planejamento e roteamento de veículos de uma fábrica de móveis
https://repositorio.ufscar.br/handle/ufscar/19368
Estudo do impacto da consideração de custos de emissão de CO2 nas decisões de planejamento e roteamento de veículos de uma fábrica de móveis
Moraes, Felipe Goulart
At the same time that the competitive scenario is seen in organizations, which seek to maximize profits and consumer satisfaction, there are also global concerns about increasing productivity and its impact on the environment, such as the emission of greenhouse gases, mainly CO2. To assist in decision-making and achieve reduction of economic and environmental costs, linear modeling and optimization techniques can be adopted and implemented. Through these techniques this dissertation aims to study the problem of integrated production planning and vehicle routing considering the impacts of reducing carbon emissions in furniture industries. The consideration of carbon emissions is linked to vehicle fuel emissions and applying this view brings a more realistic scenario as it considers the load transported in vehicles and speeds. Through experiments will be analyzed the gains from considering the unloading of vehicles, the speed selection that the vehicle adopts and how much the inclusion of carbon costs can affect the trade off between production and routing. An additional model was also developed that considers a heterogeneous fleet in relation to CO2 potential emission
2023-12-18T00:00:00ZIntegração das tecnologias da indústria 4.0 com o lean manufacturing
https://repositorio.ufscar.br/handle/ufscar/19209
Integração das tecnologias da indústria 4.0 com o lean manufacturing
Martins, Tailise Mascarenhas
The research aims to analyze the impact of the interaction between Industry 4.0 technologies and the Lean Manufacturing concept on organizational operational performance, from the perspective of manufacturing managers, considering relevant information from a systematic literature review. The objective of integrating LM and I4.0 is to understand how LM impacts operational performance through systematic and continuous pursuit of waste reduction and improvement by interacting with I4.0 technologies, which introduce automation and interconnectivity and can mitigate pre-existing management difficulties. The study is based on a systematic literature review conducted in the SCOPUS and Web of Science databases, covering 54 articles, and on subsequent multiple case studies. The review identified 15 Lean Manufacturing tools, 18 I4.0 technologies, and 12 most cited improvements/benefits to date, which served as the basis for the case questionnaire and analyses of the multiple case studies. Multiple case studies were conducted in three manufacturing companies, using questionnaires, data collection from company websites, and interviews to triangulate data. The aim was to identify the interaction of I4.0 technologies with Lean Manufacturing tools and understand the maturity trajectory of I4.0 in each company. The results indicate that Lean Manufacturing is the foundation for successful implementation of Industry 4.0. This integration provides benefits such as increased confidence in data, productivity, and operational performance. The study offers managerial implications, highlighting how Lean tools can eliminate waste and effectively collaborate with the introduction of I4.0 technologies. Lessons learned from the case study companies provide valuable insights for improving the overall performance of companies integrating these tools and technologies.
2023-12-07T00:00:00ZAnalyzing the allocation of warehouses in the São Paulo metropolitan region: an exploratory study
https://repositorio.ufscar.br/handle/ufscar/18784
Analyzing the allocation of warehouses in the São Paulo metropolitan region: an exploratory study
Simões, William Douglas Barros
Urban logistics has opened a broad field of discussion regarding the spatial allocation of warehouses. It is known that the dynamics that govern business decisions in this resource allocation process are not random but follow the rational search for maximizing benefits common to all companies and economic agents. The public sector is also part of this discussion on investment in physical capital, which needs to understand the directional axis of investments by economic agents to offer adequate transport and security infrastructure for companies that have set up or will set up in each location. This dynamic of spatial allocation can occur both in the format of spreading or concentration of warehouses in large urban centers, or even through a diffuse behavior. This process is influenced by a series of associated macro and microeconomic variables, in addition to variables related to the transport infrastructure in large urban centers. In this dissertation, the main objective is to analyze the variables and their relationships with the logistic spatial phenomena, based on economic theory, to better understand the dynamics of physical capital allocation in São Paulo Metropolitan Region (SPMR), specifically the distribution warehouses. To deepen this analysis, systematic review methodologies, spatial exploratory analyses, economic theory, and econometric methods were used. Initially, the variables studied in the specialized literature that are related to this spatial dynamic were sought, and which were also contemplated in the theories of urban economics and New Economic Geography (NGE). The variables found were: (a) economic growth and market size, (b) accessibility to transportation, (c) distance from industrial, service, and urban centers, (d) property prices and rents, (e) cargo theft, (f) regulatory and tax policies (g) population and labor market, (h) area size and land use. To deepen the analysis of these variables, the econometric model was built based on a multiple linear regression, initially using all the variables mentioned above plus the variables Human Development Index and number of freight vehicles. The results indicated a strong correlation of this spatial allocation dynamic with economic theory, as well as establishing the statistical significance of the variables GDP, distance from the center of São Paulo and number of cargo vehicles, their magnitudes, and implicit and explicit theoretical implications in relation to the variable explained. The results of this modeling corroborate the premise of rationality of economic agents and pre-existing studies of logistics sprawl/agglomeration in specialized literature, diverging occasionally in some results, a clear demonstration of the complexity and spatiality of the dynamics of warehouse allocation. With this theoretical-empirical compendium established, an exploratory spatial analysis and a causality study were carried out based on Granger and cointegration methodologies using the variables Gross Domestic Product (GDP) and Cargo theft as explanatory variables and the number of warehouses installed as a dependent. The results showed strong heterogeneity in causal relationships, both in the short and long term and were specific to each of the variables under study.
2023-06-28T00:00:00ZESIREOS: Avaliação internal, eficiente e escalável de métodos não supervisionados de detecção de anomalias
https://repositorio.ufscar.br/handle/ufscar/18227
ESIREOS: Avaliação internal, eficiente e escalável de métodos não supervisionados de detecção de anomalias
Alves, William Adriano
Anomaly (outlier) detection is one of the main problems in data mining. Since anomalies can translate into important information in numerous fields, several methods were developed to identify them, especially unsupervised methods, which is the focus of this work. To soften the need for studies on assessing and quantifying the quality of the result of these unsupervised methods, the IREOS index was proposed as the first internal evaluation technique for unsupervised anomaly detection methods. IREOS allows one to select the best algorithm and parameters for a given problem using only intrinsic information from the data. However, IREOS demands the training of many highly complex classifiers for each object in the dataset whose outlier detection solutions are being analyzed. This feature limits the application of IREOS to small datasets since the classifiers use all points in the dataset during its training. In the present work, we propose ESIREOS, the first version of IREOS that addresses its performance and processing deficiencies using Massive Parallel Computing techniques that efficiently implement horizontal computational scaling for many machine learning problems. ESIREOS also makes use of approximated Nearest Neighbor Graphs to reduce the volume of data and processing power demanded by IREOS without any significant loss in the quality of the results. We evaluate ESIREOS theoretically, estimating its asymptotic complexity and with experiments over real and synthetic datasets to attest to its effectiveness and performance compared to the original version, including large datasets. The results showed that ESIREOS resulted in a significant improvement in computational complexity when compared to the original IREOS while maintaining quality. ESIREOS showed to be capable of evaluating solutions for very large datasets, even those which IREOS was not capable of evaluating in a feasible time. Therefore, this efficient and scalable new version can be used in many scenarios, mainly, but not limited to, those with large or distributed data.
2023-05-25T00:00:00Z