Introduction to Logistics Systems Management: With Microsoft Excel and Python Examples 3/e

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【簡介】 Description Introduction to Logistics Systems Management provides an in-depth introduction to the methodological aspects of planning, organization, and control of logistics for organizations in the private, public and non-profit sectors. Based on the authors’ extensive teaching, research, and industrial consulting experience, this classic textbook is used in universities worldwide to teach students the use of quantitative methods for solving complex logistics problems. Fully updated and revised, the third edition places increased emphasis on the complexity and flexibility required by modern logistics systems. In this context, the extensive use of data, descriptive analytics, predictive models, and optimization techniques will be invaluable to support the decisions and actions of logistics and supply chain managers. Throughout the book, brand-new case studies and numerical examples illustrate how various methods can be used in industrial and service logistics to reduce costs and improve service levels. The book: includes new models and techniques that have emerged over the past decade; describes methodologies for logistics decision making, forecasting, logistics system design, procurement, warehouse management, and freight transportation management; includes end-of-chapter exercises, Microsoft® Excel® files and Python® computer codes for each algorithm covered; includes access to a companion website with additional exercises, links to video tutorials, and supplementary teaching material. To facilitate creation of course material, additional LaTeX source data containing the formulae, optimization models, tables and algorithms described in the book is available to instructors. Introduction to Logistics Systems Management, Third Edition remains an essential textbook for senior undergraduate and graduate students in engineering, computer science, and management science courses. It is also a highly useful reference for academic researchers and industry practitioners alike. 【目錄】 Table of Contents 1 Introducing Logistics 2 Forecasting Logistics Data 3 Designing the Logistics Network 4 Selecting the Suppliers 5 Managing a Warehouse 6 Managing Freight Transportation Index

Applied Logistic Regression (3版)

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【簡介】 A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: •A chapter on the analysis of correlated outcome data •A wealth of additional material for topics ranging from Bayesian methods to assessing model fit •Rich data sets from real-world studies that demonstrate each method under discussion •Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines. 【目錄】 1 Introduction to the Logistic Regression Model 2 The Multiple Logistic Regression Model 3 Interpretation of the Fitted Logistic Regression Model 4 Model-Building Strategies and Methods for Logistic Regression 5 Assessing the Fit of the Model 6 Application of Logistic Regression with Different Sampling Models 7 Logistic Regression for Matched Case-Control Studies 8 Logistic Regression Models for Multinomial and Ordinal Outcomes 9 Logistic Regression Models for the Analysis of Correlated Data 10 Special Topics