Pub Date : 2021-12-30DOI: 10.30564/jmser.v5i1.4068
Md. Touhidul Islam, Md. Nahid Hassan
Denim was produced in the city of Nîmes in France and was originally called the serge de Nîmes. The word denim is an English colloquialism of the French term: “denim.” Day by day Bangladesh denim sector very much developed and helps to increase productivity. Bangladesh have seen a significant increase in investing in denim fabric manufacturing, increasing the country’s production performance by reducing fabric dependence on imports. It is important due to its aspects of durability, and not easily torn which benefited physical laborers much. The government also plays a vital role in denim textile industry. This paper shows different section of denim textile industry such as: sewing section, cutting section, washing, IE and finishing department. The main aim of this paper is how to role all the section of denim textile industry. Textile education is insufficient without industry attachment, which bridges the gap between theoretical and practical aspects and acclimates students to the industrial world. We can gain about theoretical development on an industrial level from this attachment. We can understand more about the machines used in various departments, their technical specifications, characteristics, operating system, and so on, and we believe that without this type of industrial connection, it is impossible to obtain industry-based information about textile engineering adequately. The Industrial Attachment on Denim Manufacturing Technology was used to organize this study (sewing section, cutting, IE, washing section, CAD Section, and finishing department. Various operating procedures for the production of denim in the industry are presented in this paper. The technique and process of several procedures and processes are presented here such as machine specifications, manpower, maintenance, layout of the different section, dye processes and wet processes.
牛仔布是在法国n mes市生产的，最初被称为serge de n mes。denim这个词是法语单词“denim”的英语口语。孟加拉国的牛仔行业日益发达，并有助于提高生产率。孟加拉国在牛仔面料制造方面的投资显著增加，通过减少对进口面料的依赖，提高了该国的生产绩效。它很重要，因为它的耐用性，不易撕裂，这对体力劳动者很有好处。政府在牛仔纺织行业中也起着至关重要的作用。本文介绍了牛仔纺织工业的各个环节:缝纫环节、裁剪环节、洗涤环节、IE环节和整理环节。本文的主要目的是如何发挥牛仔纺织行业各部门的作用。没有行业实习，纺织教育是不够的，因为行业实习弥合了理论和实践方面的差距，使学生适应了工业世界。我们可以从这种依恋中获得关于工业层面的理论发展。我们可以更多地了解各个部门使用的机器，它们的技术规格、特点、操作系统等等，我们认为如果没有这种类型的工业连接，就不可能充分获得纺织工程的行业基础信息。本研究采用《牛仔布制造技术工业附件》(缝纫段、裁剪段、IE段、洗涤段、CAD段、整理段)组织。本文介绍了工业生产牛仔布的各种操作规程。本文从设备规格、人力、维护、各工段布置、染色工艺、湿法工艺等几个工序和工艺的技术和流程进行了介绍。
{"title":"The Basic Layout of a Denim Textile Industry: A Basic Review","authors":"Md. Touhidul Islam, Md. Nahid Hassan","doi":"10.30564/jmser.v5i1.4068","DOIUrl":"https://doi.org/10.30564/jmser.v5i1.4068","url":null,"abstract":"Denim was produced in the city of Nîmes in France and was originally called the serge de Nîmes. The word denim is an English colloquialism of the French term: “denim.” Day by day Bangladesh denim sector very much developed and helps to increase productivity. Bangladesh have seen a significant increase in investing in denim fabric manufacturing, increasing the country’s production performance by reducing fabric dependence on imports. It is important due to its aspects of durability, and not easily torn which benefited physical laborers much. The government also plays a vital role in denim textile industry. This paper shows different section of denim textile industry such as: sewing section, cutting section, washing, IE and finishing department. The main aim of this paper is how to role all the section of denim textile industry. Textile education is insufficient without industry attachment, which bridges the gap between theoretical and practical aspects and acclimates students to the industrial world. We can gain about theoretical development on an industrial level from this attachment. We can understand more about the machines used in various departments, their technical specifications, characteristics, operating system, and so on, and we believe that without this type of industrial connection, it is impossible to obtain industry-based information about textile engineering adequately. The Industrial Attachment on Denim Manufacturing Technology was used to organize this study (sewing section, cutting, IE, washing section, CAD Section, and finishing department. Various operating procedures for the production of denim in the industry are presented in this paper. The technique and process of several procedures and processes are presented here such as machine specifications, manpower, maintenance, layout of the different section, dye processes and wet processes.","PeriodicalId":127326,"journal":{"name":"Journal of Management Science & Engineering research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128072834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-29DOI: 10.30564/jmser.v4i2.3707
Asefachew Belete Tseganeh, Henok Fikre Geberegziabher, A. Chala
Expansive soils undergo high volume change due to cyclic swelling and shrinkage behavior during the wet and dry seasons. Thus, such problematic soils should be completely avoided or properly treated when encountered as subgrade materials. In the present study, the biomedical waste incinerator ash and lime combination was proposed to stabilize expansive soil. Particle size analysis, Atterberg limits, free-swell, compaction, unconfined compression strength, and California bearing ratio tests were conducted on the natural soil and blended with 3%, 5%, 7%, 9%, and 11% biomedical waste incinerator ash (BWIA). The optimum content of BWIA was determined based on the free-swell test results. To further investigate the relative effectiveness of the stabilizer, 2% and 3% lime were also added to the optimum soil-BWIA mixture and UCS and CBR tests were also conducted. In addition, scanning electron microscopy (SEM) tests for representative stabilized samples were also conducted to examine the changes in microfabrics and structural arrangements due to bonding. The addition of BWIA has a promising effect on the index properties and strength of the expansive soil. The strength of the expansive soil significantly increased when it was blended with the optimum content of BWIA amended by 2% and 3% lime.
{"title":"Stabilization of Expansive Soil Using Biomedical Waste Incinerator Ash","authors":"Asefachew Belete Tseganeh, Henok Fikre Geberegziabher, A. Chala","doi":"10.30564/jmser.v4i2.3707","DOIUrl":"https://doi.org/10.30564/jmser.v4i2.3707","url":null,"abstract":"Expansive soils undergo high volume change due to cyclic swelling and shrinkage behavior during the wet and dry seasons. Thus, such problematic soils should be completely avoided or properly treated when encountered as subgrade materials. In the present study, the biomedical waste incinerator ash and lime combination was proposed to stabilize expansive soil. Particle size analysis, Atterberg limits, free-swell, compaction, unconfined compression strength, and California bearing ratio tests were conducted on the natural soil and blended with 3%, 5%, 7%, 9%, and 11% biomedical waste incinerator ash (BWIA). The optimum content of BWIA was determined based on the free-swell test results. To further investigate the relative effectiveness of the stabilizer, 2% and 3% lime were also added to the optimum soil-BWIA mixture and UCS and CBR tests were also conducted. In addition, scanning electron microscopy (SEM) tests for representative stabilized samples were also conducted to examine the changes in microfabrics and structural arrangements due to bonding. The addition of BWIA has a promising effect on the index properties and strength of the expansive soil. The strength of the expansive soil significantly increased when it was blended with the optimum content of BWIA amended by 2% and 3% lime.","PeriodicalId":127326,"journal":{"name":"Journal of Management Science & Engineering research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116325956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-24DOI: 10.30564/jmser.v4i2.3261
C. Ibrahima, Jianwu Xue, Thierno Gueye
Demand forecasting and big data analytics in supply chain management are gaining interest. This is attributed to the wide range of big data analytics in supply chain management, in addition to demand forecasting, and behavioral analysis. In this article, we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications, identify gaps, and provide ideas for future research. Algorithms will then be classified and then applied in supply chain management such as neural networks, k-nearest neighbors, time series forecasting, clustering, regression analysis, support vector regression and support vector machines. An extensive hierarchical model for short-term auto parts demand assessment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series. The concept of extensive relevance assessment was proposed, and subsequently methods to reflect the relevance of automotive demand factors were discussed. Using a wide range of skills, the factors and cofactors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components. Then, it is compared with the existing data and predicted the short-term historical data. The result proved the predictive error is less than 6%, which supports the validity of the prediction method. This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.
{"title":"A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components","authors":"C. Ibrahima, Jianwu Xue, Thierno Gueye","doi":"10.30564/jmser.v4i2.3261","DOIUrl":"https://doi.org/10.30564/jmser.v4i2.3261","url":null,"abstract":"Demand forecasting and big data analytics in supply chain management are gaining interest. This is attributed to the wide range of big data analytics in supply chain management, in addition to demand forecasting, and behavioral analysis. In this article, we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications, identify gaps, and provide ideas for future research. Algorithms will then be classified and then applied in supply chain management such as neural networks, k-nearest neighbors, time series forecasting, clustering, regression analysis, support vector regression and support vector machines. An extensive hierarchical model for short-term auto parts demand assessment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series. The concept of extensive relevance assessment was proposed, and subsequently methods to reflect the relevance of automotive demand factors were discussed. Using a wide range of skills, the factors and cofactors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components. Then, it is compared with the existing data and predicted the short-term historical data. The result proved the predictive error is less than 6%, which supports the validity of the prediction method. This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.","PeriodicalId":127326,"journal":{"name":"Journal of Management Science & Engineering research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125606596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-04DOI: 10.30564/jmser.v4i2.3336
Jênifer Ribeiro Dona, I. C. Paula, Alceu Terra do Nascimento, A. C. Gularte
Brazilian public managers have been structuring and updating policies to support workers’ employment and income strategies. However, when the vulnerable individual has social, emotional, or technical limitations, success in this operation becomes uncertain. This research aim was to propose a methodology to identify profiles in vulnerable populations, viewing to promote the efficient elaboration of employment and income strategies. The unit of analysis was vulnerable population of waste pickers, in a large city from South Brazil, in the scope of a municipal program named "All of us are Porto Alegre". A literature review allowed the identification of tools from marketing, economy and design adequate to profile analysis. A workshop with social educators responsible for giving support to the individuals. Insights from workshop and the literature allowed the proposition of a methodology including cluster analysis and the creative tool named personas. The methodological approach suggests it is adequate in confirming the differences in profiles. The theoretical contribution lies in the use of quantitative-creativity tools to support policymaking. The practical contribution is to provide consistent information for governmental decision-making at the labor access market.
{"title":"Identification of the Profile of Vulnerable Population to Elaborate Efficient Employment Strategy: Proposition of a Quantitative-creative Approach","authors":"Jênifer Ribeiro Dona, I. C. Paula, Alceu Terra do Nascimento, A. C. Gularte","doi":"10.30564/jmser.v4i2.3336","DOIUrl":"https://doi.org/10.30564/jmser.v4i2.3336","url":null,"abstract":"Brazilian public managers have been structuring and updating policies to support workers’ employment and income strategies. However, when the vulnerable individual has social, emotional, or technical limitations, success in this operation becomes uncertain. This research aim was to propose a methodology to identify profiles in vulnerable populations, viewing to promote the efficient elaboration of employment and income strategies. The unit of analysis was vulnerable population of waste pickers, in a large city from South Brazil, in the scope of a municipal program named \"All of us are Porto Alegre\". A literature review allowed the identification of tools from marketing, economy and design adequate to profile analysis. A workshop with social educators responsible for giving support to the individuals. Insights from workshop and the literature allowed the proposition of a methodology including cluster analysis and the creative tool named personas. The methodological approach suggests it is adequate in confirming the differences in profiles. The theoretical contribution lies in the use of quantitative-creativity tools to support policymaking. The practical contribution is to provide consistent information for governmental decision-making at the labor access market.","PeriodicalId":127326,"journal":{"name":"Journal of Management Science & Engineering research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131924451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-05DOI: 10.30564/jmser.v4i2.3242
S. Cisse
Forecasting is predicting or estimating a future event or trend. Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century. As the competitiveness between supply chains intensifies day by day, companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits. Excessive inventory (overstock) and stock outs are very significant issues for suppliers. Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory. Excess inventory can also lead to increased storage, insurance costs and labor as well as lower and degraded quality based on the nature of the product. Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store. If clients are unable to find the right products on the shelves, they may switch to another vendor or purchase alternative items. Demand forecasting is valuable for planning, scheduling and improving the coordination of all supply chain activities. This paper discusses the use of neural networks for seasonal time series forecasting. Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.
{"title":"Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks","authors":"S. Cisse","doi":"10.30564/jmser.v4i2.3242","DOIUrl":"https://doi.org/10.30564/jmser.v4i2.3242","url":null,"abstract":"Forecasting is predicting or estimating a future event or trend. Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century. As the competitiveness between supply chains intensifies day by day, companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits. Excessive inventory (overstock) and stock outs are very significant issues for suppliers. Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory. Excess inventory can also lead to increased storage, insurance costs and labor as well as lower and degraded quality based on the nature of the product. Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store. If clients are unable to find the right products on the shelves, they may switch to another vendor or purchase alternative items. Demand forecasting is valuable for planning, scheduling and improving the coordination of all supply chain activities. This paper discusses the use of neural networks for seasonal time series forecasting. Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.","PeriodicalId":127326,"journal":{"name":"Journal of Management Science & Engineering research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116301237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-05DOI: 10.30564/JMSER.V4I2.3300
E. Najafi, A. Mirzaei, M. Rezvanyvardom, Mahdi Zolfaghar
PV plants are increasing all over the world and they are becoming a distinct part of electric grids. Due to abundance of solar irradiation and almost constant amount of it in certain geographical latitudes, selection of proper capacity of PV plants depends mostly on available places for the site. In this paper, important measures for safe connection of a PV plant in terms of voltage requirements are addressed and several guidelines are introduced for this purpose. In addition, simulation results are included to prove some of the mentioned suggestions. A general algorithm is finally proposed to show the directions for safe connection of PV plants.
{"title":"Managing New PV Plant Connection to Available Grids to Stay within Standard Limits with a Case Study","authors":"E. Najafi, A. Mirzaei, M. Rezvanyvardom, Mahdi Zolfaghar","doi":"10.30564/JMSER.V4I2.3300","DOIUrl":"https://doi.org/10.30564/JMSER.V4I2.3300","url":null,"abstract":"PV plants are increasing all over the world and they are becoming a distinct part of electric grids. Due to abundance of solar irradiation and almost constant amount of it in certain geographical latitudes, selection of proper capacity of PV plants depends mostly on available places for the site. In this paper, important measures for safe connection of a PV plant in terms of voltage requirements are addressed and several guidelines are introduced for this purpose. In addition, simulation results are included to prove some of the mentioned suggestions. A general algorithm is finally proposed to show the directions for safe connection of PV plants.","PeriodicalId":127326,"journal":{"name":"Journal of Management Science & Engineering research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131214613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-22DOI: 10.30564/jmser.v4i2.3166
Md. Tareque Rahaman, Tarekul Islam
This research work was intended to analyze the unleashed issues related to apparel trade during COVID-19 pandemic & made an attempt to find the best possible responses to uphold marketing campaigns during & after the pandemic situation. Apparel industries in Asia, the largest global hub of both the textile & apparel import-export trade have been severally damaged by the COVID-19. Over the last one year, the global community had already realized the fact that how pandemic situation disrupted the supply chain management of textile, apparel & fashion manufacturing industries throughout the world. Bangladesh, one of the top ranked garments exporter countries have been facing the burning bridges, due to the scarcity of raw materials & gradual cancelation export orders. The contribution of the apparel industry is more significant for the socio-economic growth of a 3rd world countries like Bangladesh, just because apparel contributes almost 84% of its total export income with the involvement of 4.5 million people approximately. The following research paper conveys a three-fold story. In the very beginning portion, there are some reviews & analysis of the overall scenarios of the COVID-19 pandemic with presence of several business reports, academic journals, market research, manufactures' opinions & stakeholders' strategies. The second phase of the research work forecasts the possible responses need to be projected during & after the pandemic situation. Finally, this study predicts an ideal foot print to cope up with similar sort of situations in future.
{"title":"Impacts and Possible Responses Related to COVID-19 in the Textile and Apparel Industry of Bangladesh","authors":"Md. Tareque Rahaman, Tarekul Islam","doi":"10.30564/jmser.v4i2.3166","DOIUrl":"https://doi.org/10.30564/jmser.v4i2.3166","url":null,"abstract":"This research work was intended to analyze the unleashed issues related to apparel trade during COVID-19 pandemic & made an attempt to find the best possible responses to uphold marketing campaigns during & after the pandemic situation. Apparel industries in Asia, the largest global hub of both the textile & apparel import-export trade have been severally damaged by the COVID-19. Over the last one year, the global community had already realized the fact that how pandemic situation disrupted the supply chain management of textile, apparel & fashion manufacturing industries throughout the world. Bangladesh, one of the top ranked garments exporter countries have been facing the burning bridges, due to the scarcity of raw materials & gradual cancelation export orders. The contribution of the apparel industry is more significant for the socio-economic growth of a 3rd world countries like Bangladesh, just because apparel contributes almost 84% of its total export income with the involvement of 4.5 million people approximately. The following research paper conveys a three-fold story. In the very beginning portion, there are some reviews & analysis of the overall scenarios of the COVID-19 pandemic with presence of several business reports, academic journals, market research, manufactures' opinions & stakeholders' strategies. The second phase of the research work forecasts the possible responses need to be projected during & after the pandemic situation. Finally, this study predicts an ideal foot print to cope up with similar sort of situations in future. ","PeriodicalId":127326,"journal":{"name":"Journal of Management Science & Engineering research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132224245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-09DOI: 10.30564/JMSER.V3I2.2613
Schneider Wilnei Aldir, Tezza Rafael
Consumer behavior in electronic commerce has been the theme of hundreds of studies conducted by researchers of many nationalities in the past twenty years. The purpose of this study was to review and classify the concepts used in papers published between 2003 and 2014 to explain the consumer behavior in electronic commerce. A systematic search of the literature in nine databases was performed and 136 papers published in double-blind peer reviewed journals were selected. Reference models were prepared based on a classification of the concepts found. This article reports only the concepts that displayed statistical significance in the studies analyzed. Finally, we suggest new studies that can be conducted
{"title":"Online Shopping: Antecedents of Attitude, Intention and Use","authors":"Schneider Wilnei Aldir, Tezza Rafael","doi":"10.30564/JMSER.V3I2.2613","DOIUrl":"https://doi.org/10.30564/JMSER.V3I2.2613","url":null,"abstract":"Consumer behavior in electronic commerce has been the theme of hundreds of studies conducted by researchers of many nationalities in the past twenty years. The purpose of this study was to review and classify the concepts used in papers published between 2003 and 2014 to explain the consumer behavior in electronic commerce. A systematic search of the literature in nine databases was performed and 136 papers published in double-blind peer reviewed journals were selected. Reference models were prepared based on a classification of the concepts found. This article reports only the concepts that displayed statistical significance in the studies analyzed. Finally, we suggest new studies that can be conducted","PeriodicalId":127326,"journal":{"name":"Journal of Management Science & Engineering research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124599714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-09DOI: 10.30564/JMSER.V3I2.2689
Gaosheng Wang, Yi Ding
In Container terminals, a quay crane’s resource hour is affected by various complex nonlinear factors, and it is not easy to make a forecast quickly and accurately. Most ports adopt the empirical estimation method at present, and most of the studies assumed that accurate quay crane’s resource hour could be obtained in advance. Through the ensemble learning (EL) method, the influence factors and correlation of quay crane’s resources hour were analyzed based on a large amount of historical data. A multi-factor ensemble learning estimation model based quay crane’s resource hour was established. Through a numerical example, it is finally found that Adaboost algorithm has the best effect of prediction, with an error of 1.5%. Through the example analysis, it comes to a conclusion: the error is 131.86% estimated by the experience method. It will lead that subsequent shipping cannot be serviced as scheduled, increasing the equipment wait time and preparation time, and generating additional cost and energy consumption. In contrast, the error based Adaboost learning estimation method is 12.72%. So Adaboost has better performance.
{"title":"Research on the Prediction of Quay Crane Resource Hour based on Ensemble Learning","authors":"Gaosheng Wang, Yi Ding","doi":"10.30564/JMSER.V3I2.2689","DOIUrl":"https://doi.org/10.30564/JMSER.V3I2.2689","url":null,"abstract":"In Container terminals, a quay crane’s resource hour is affected by various complex nonlinear factors, and it is not easy to make a forecast quickly and accurately. Most ports adopt the empirical estimation method at present, and most of the studies assumed that accurate quay crane’s resource hour could be obtained in advance. Through the ensemble learning (EL) method, the influence factors and correlation of quay crane’s resources hour were analyzed based on a large amount of historical data. A multi-factor ensemble learning estimation model based quay crane’s resource hour was established. Through a numerical example, it is finally found that Adaboost algorithm has the best effect of prediction, with an error of 1.5%. Through the example analysis, it comes to a conclusion: the error is 131.86% estimated by the experience method. It will lead that subsequent shipping cannot be serviced as scheduled, increasing the equipment wait time and preparation time, and generating additional cost and energy consumption. In contrast, the error based Adaboost learning estimation method is 12.72%. So Adaboost has better performance.","PeriodicalId":127326,"journal":{"name":"Journal of Management Science & Engineering research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116523790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-12DOI: 10.30564/jmser.v2i2.1851
Qiu Fang, Zhiming Li, Mengtian Leng, Jincheng Wu, Zhen Wang
In recent years, the rise of machine learning has made it possible to further explore large data in various fields. In order to explore the attributes of loyalty of public transport travelers and divide these people into different clustering clusters, this paper uses K-means clustering algorithm (K-means) to cluster the holding time, recharge amount and swiping frequency of bus travelers. Then we use Kernel Density Estimation Algorithms (KDE) to analyze the density distribution of the data of holding time, recharge amount and swipe frequency, and display the results of the two algorithms in the way of data visualization. Finally, according to the results of data visualization, the loyalty of users is classified, which provides theoretical and data support for public transport companies to determine the development potential of users.
近年来，机器学习的兴起，使得在各个领域进一步探索大数据成为可能。为了探究公交乘客的忠诚度属性，并将其划分为不同的聚类，本文采用K-means聚类算法(K-means)对公交乘客的等待时间、充值金额和刷卡次数进行聚类。然后利用核密度估计算法(Kernel Density Estimation Algorithms, KDE)分析持电时间、充值量和刷电频率数据的密度分布，并以数据可视化的方式显示两种算法的结果。最后，根据数据可视化结果对用户忠诚度进行分类，为公交企业确定用户发展潜力提供理论和数据支持。
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