Input Data Analysis

Vincent, Stephen

doi:10.1002/9780470172445.ch3

Cited by 35 publications

(19 citation statements)

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“…Such data characteristics do not allow the simulation practitioner to check whether being i.i.d. is a reasonable assumption for the underlying input process; see Vincent (1998) for further examples that turn input modeling into a challenging problem. The input-modeling techniques of this section, on the other hand, assume that the historical data on hand are statistically i.i.d.…”

Section: Preliminary Analysis Of the Historical Datamentioning

confidence: 99%

“…It shows the sample correlations for various lags; note that a lag ' correlation is the correlation between data points that are ' values apart. Vincent (1998) reports that lags of size one through ten are the most informative about a sample, whereas lags over 20 are non informational. For an i.i.d.…”

Section: Preliminary Analysis Of the Historical Datamentioning

confidence: 99%

“…A way of doing this is to analyze the data for any discernible increasing or decreasing patterns over time; see Vincent (1998) for further discussion on assessing the stationarity of the underlying input process.…”

Section: Preliminary Analysis Of the Historical Datamentioning

confidence: 99%

“…A distribution that is often used for this purpose is the exponential distribution. A more detailed presentation of the empirical distribution function with a focus on simulation input modeling can be found in Vincent (1998).…”

Section: Model Fitting and Parameter Estimationmentioning

confidence: 99%

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S-model

2013

Encyclopedia of Operations Research and Management Science

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Section: Preliminary Analysis Of the Historical Datamentioning

confidence: 99%

Section: Preliminary Analysis Of the Historical Datamentioning

confidence: 99%

Section: Preliminary Analysis Of the Historical Datamentioning

confidence: 99%

Section: Model Fitting and Parameter Estimationmentioning

confidence: 99%

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S-model

2013

Encyclopedia of Operations Research and Management Science

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“…É comum que os dados do sistema estejam indisponíveis, ou que não estejam no formato desejado para o desenvolvimento do estudo e necessitem de tratamento. Assim, alguns procedimentos são importantes para a coleta dos dados, dentre eles, visitas à planta, entrevistas, acessos a bancos de dados, coleta manual dos dados e, no caso de as informações não existirem, algumas considerações são necessárias (VINCENT, 1998). Para este estudo, os seguintes dados foram coletados:…”

Section: Modelagem E Simulação 41 Coleta De Dados E Restriçõesunclassified

Análise dos estoques em processo de um sistema de produção de cabinas de caminhões com o uso da simulação de eventos discretos

Filho¹,

Gilberto²

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AgradecimentosAo Professor Titular Arthur José Vieira Porto, pela orientação, ajuda, amizade e apoio profissional desde o início deste trabalho.Aos meus pais e ao meu irmão pelo incentivo, apoio, disciplina, amizade, educação, exemplo e amor.À empresa DaimlerChrysler do Brasil pela oportunidade e apoio dados para a realização e sucesso desse trabalho, em especial à Sra. Regina Maria Saporito e ao Sr. Renato Ferreira Junior pelo apoio, confiança e oportunidade. À Fundação para o Incremento da Pesquisa e do Aperfeiçoamento Industrial(FIPAI) pela bolsa concedida e toda ajuda e suporte dados durante a realização de todo o trabalho, em especial à Rosana, pela ajuda em diversos momentos. Aos amigos do Laboratório de Simulação e Controle da EESC / USP JoséHamilton, Mamoru, Ana Paula, Chin, Denise, Ricardo, Merylin, Daniel Maeda, Silmara, Mariella, Ana Rita, Heleno, Heráclito, Hilano, pela amizade e auxílio na convivência diária. Um agradecimento especial à Beth.A todos os colegas, professores e funcionários do Departamento de Engenharia Mecânica da EESC/USP, pelas suas colaborações. Em especial à Escola de Engenharia de São Carlos e à Universidade de SãoPaulo. Rohbau...................................................................................62 Figura 3.12 The analysis and reduction of the selective banks are useful because the storage of cabins represents some production risks, is hard to control and implies in high costs.The developing of this work will contribute to the knowledge of the difficulties and benefits of the use of Discrete Event Simulation applied to high complexity production systems like the automotive industry. The size of the products, the production mix, the throughput variability and the other simultaneous variables turn the storage analysis to a hard level. In this way, the Discrete Event Simulation is the most indicated technique to help the analysis and the decision making. The whole study of the system, the modeling, the validation and verification, the simulation runs and the results were done according to known procedures of using the Discrete Event Simulation, which are mentioned in the references. The presented results show the efficiency of the utilized method and propose optimized scenery of the studied system. The simulation software Arena® 5.0 was used in this study.Key words: Discrete Event Simulation, Selective Banks Analysis, Automotive Industry. 131. Introdução ContextoDesde a década de 80, as empresas produtivas e as prestadoras de serviço vêm modificando sua estrutura, seus conceitos e filosofia. Buscam, assim, sobreviver frente à competitividade proveniente da difusão mundial da cultura e da tecnologia, à fragmentação contínua dos mercados de massa, a clientes mais exigentes, à segmentação da cadeia produtiva e ao impacto crescente de valores sociais em constante alteração (GOULART, 2000 apud PREISS et al., 1998. No mesmo sentido, Corrêa et al. (2001) citam que, como não há mercados demandantes suficientes para todos os ofertantes venderem seus produtos, alguns conseguir...

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An Overnight Parcel Logistics Company’s Capillary Distribution Network Design by Regression Analysis

Martín

Macià

2013

Annals of Industrial Engineering 2012

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This article will explore and analyse how to design the capillary distribution network of an Overnight Parcel Logistics Company (hereby referred to as OPLC). The OPLC needs to design a capillary distribution network that is capable of collecting all the merchandise that their clients wish to deliver, and, at the same time, have the capacity to distribute all shipments to their destination. To reach this goal, the designers have to decide the type of vehicles (van, lorry or trailer) and the quantity of each type of vehicle the capillary distribution network needs to cover the area around each hub efficiently. This number of vehicles has to satisfy the delivery/collection quality requirements with the minimum cost to guarantee the maximum profit for the OPLC. This research will demonstrate that regression analysis is a valid technique to help an OPLC in the decision making related to the distribution costs of the capillary network. The way the costs of each distribution areas are obtained for the design of the capillary network is by regression models. Most models related to logistics companies have been based on deterministic techniques so far.

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Input Data Analysis

Cited by 35 publications

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S-model

Análise dos estoques em processo de um sistema de produção de cabinas de caminhões com o uso da simulação de eventos discretos

An Overnight Parcel Logistics Company’s Capillary Distribution Network Design by Regression Analysis

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