工程與項目管理課題： 運籌學(xué)在資源優(yōu)化及生產(chǎn)效率提升中的應(yīng)用探究

專業(yè)：商業(yè)

項目類型：國外1對1

開始時間：2023年11月04日

是否可加論文：是

項目周期：7周在線小組科研學(xué)習+5周不限時論文指導(dǎo)學(xué)習

語言：英文

有無剩余名額：名額充足

建議學(xué)生年級：大學(xué)生 高中生

是否必需面試：否

適合專業(yè)：商業(yè)分析金融學(xué)財務(wù)管理數(shù)據(jù)分析創(chuàng)業(yè)創(chuàng)新風險管理數(shù)學(xué)商業(yè)統(tǒng)計公司管理商業(yè)決策

地點：無

建議具備的基礎(chǔ)：工程管理、項目管理、工業(yè)工程、運營管理、風險管理、運籌學(xué)等專業(yè)或者希望修讀相關(guān)專業(yè)的學(xué)生；具有代數(shù)及微積分基礎(chǔ)的學(xué)生優(yōu)先

產(chǎn)出：7周在線小組科研學(xué)習+5周不限時論文指導(dǎo)學(xué)習 共125課時 項目報告 優(yōu)秀學(xué)員獲主導(dǎo)師Reference Letter EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等級別索引國際會議全文投遞與發(fā)表指導(dǎo)（可用于申請） 結(jié)業(yè)證書 成績單

項目背景：在大數(shù)據(jù)時代，高管需要以不同的方式處理不確定性的各個維度。他們需要承認、接受這一點，并確定如何充分利用不確定的數(shù)據(jù)。大數(shù)據(jù)的重要作用之一便是可以作為客戶和企業(yè)之間的雙向通道。航空調(diào)度、物流選址和配送、網(wǎng)絡(luò)規(guī)劃（路勁優(yōu)化）、電商（彈性）定價、消費者行為分析等都涉及到運籌學(xué)理論，例如，特斯拉電動車在駕駛和停車時產(chǎn)生大量數(shù)據(jù)。在行駛中，司機持續(xù)地更新車輛的加速度、剎車、電池充電和位置信息。數(shù)據(jù)也傳回工程師以了解客戶的駕駛習慣，用于優(yōu)化汽車性能。本項目旨在探索如果獲取更多的不同種類的數(shù)據(jù)，并通過數(shù)據(jù)看運籌學(xué)的實際應(yīng)用場景。并培養(yǎng)數(shù)據(jù)分析能力，包括軟件工具和使用這些數(shù)據(jù)分析工具的必備技能。 Operations and analytics is the sensible use of data and quantitative models for informing decisions and actions. Operations and analytics can help companies make better decisions by showing present and historical data within their business context. Analysts can leverage business analytic to provide performance and competitor benchmarks to make the organization run smoother and more efficiently. Analysts can also more easily spot market trends to increase sales or revenue. Used effectively, the right data can help with anything from compliance to hiring efforts.

項目介紹：數(shù)據(jù)建模是工業(yè)中有效的管理決策的重要技能。通過對項目的資源、利潤和其他關(guān)鍵指標的趨勢建模，可以對這些指標的未來進行有效的科學(xué)預(yù)測。通過數(shù)據(jù)分析和建模了解可能發(fā)生的季節(jié)性、年度或任何規(guī)模的變化，可以讓項目運行有備無患。該項目內(nèi)容為包括概率模型側(cè)重不確定性和風險處理；統(tǒng)計分析側(cè)重數(shù)據(jù)呈現(xiàn)以及如何通過數(shù)據(jù)獲取有用信息和有效推論；優(yōu)化模型和決策分析側(cè)重運用數(shù)據(jù)進行決策。學(xué)生將在項目中運用Excel或Mintab進行數(shù)據(jù)分析，在項目結(jié)束時提交報告，進行成果展示。 Business Analytics and modeling are important skills for effective managerial decision-making in business and industry. Advances in technology (computers, scanners, cell phones) have made a significant amount of data available to managers. Furthermore, business analytics provides a way for businesses to plan for the future. By modeling the trends in a businesses' sales, profits, and other key metrics, these indicators can be projected into the future. Understanding the changes that are likely to occur seasonally, annually, or on any scale allows businesses to better prepare. The techniques learned in this program will help students infer data and as such make better-informed decisions. The program covers statistical analysis, probability distributions, sampling distributions, confidence intervals, hypothesis testing, and regression models. Probability models provide tools to handle uncertainty and risk. The statistical analysis focuses on the presentation of data and techniques to draw useful and valid inferences from data.

項目大綱：統(tǒng)計建模：線性回歸與相關(guān)性分析 Statistical Modeling:Linear regression and correlation analysis 簡單回歸模型與多元回歸模型 Simple regression models; multiple regression models 優(yōu)化建模：排隊理論與容量規(guī)劃 Optimization Modeling：Linear programming and Queuing theory/capacity planning 運營管理：運營策略和流程設(shè)計 Operations Management：Operations strategy and process design 基于Excel的建模和數(shù)據(jù)分析 Spreadsheet-Based Modeling and Data Analysis 項目回顧與成果展示 Program Review and Presentation 論文輔導(dǎo) Project Deliverables Tutoring