Introduction to Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets (7版)

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Introduction to Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets 作 / 譯 者 ： Frederick S. Hillier,Mark S. Hillier,Kari Schmedders,Molly Stephens I S B N - 13 ： 9781265040055 I S B N - 10 ： 1265040052 類 別： 管理科學 版 次： 7 版 年 份： 2023 規 格： 742 頁 出 版 商： McGraw Hill Education 目錄 PART I: THE ESSENCE OF MANAGEMENT SCIENCE AND BUSINESS ANALYTICS Ch 1 Introduction Ch 2 Overview of the Analysis Process PART II: MODELS FOR PREDICTIVE ANALYTICS Ch 3 Classification and Prediction Models for Predictive Analytics CH 4 Predictive Analytics Based on Traditional Forecasting Methods PART III: USING LINEAR PROGRAMMING TO PERFORM PRESCRIPTIVE ANALYTICS Ch 5 Linear Programming: Basic Concepts Ch 6 Linear Programming: Formulation and Applications Ch 7 The Art of Modeling with Spreadsheets Ch 8 What-If Analysis for Linear Programming Ch 9 Network Optimization Problems PART IV: USING INTEGER OR NONLIEAR PROGRAMMING TO PERFORM PRESCRIPTIVE ANALYTICS Ch10 Integer Programming Ch11 Nonlinear Programming PART V: TRADITIONAL UNCERTAINTY MODELS FOR PERFORMING PREDICTIVE OR PRESCRIPTIVE ANALYTICS Ch12 Decision Analysis Ch13 Queueing Models Ch14 Computer Simulation: Basic Concepts Ch15 Computer Simulation with Analytic Solver

MODELING AND SIMULATION BASED SYSTEMS ENGINEERING

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Modeling and simulation (M&S) based systems engineering (MSBSE) is the extension of MBSE, which enhances the value of MBSE and the ability of digitally evaluating and optimizing the whole system through comprehensive applications of M&S technologies. This book puts together the recent research in MSBSE, and hopefully this will provide the researchers and engineers with reference cases in M&S technologies to support the R&D of complex products and systems. Sample Chapter(s) Preface Chapter 1: Introduction Contents: Introduction (Lin Zhang and Chun Zhao) Using Modeling and Simulation and Artificial Intelligence to Improve Complex Adaptive System Engineering (Andrew Talk, Philip Barry and Steven C Doskey) DEVS and MBSE: A Review (Bernard P Zeigler) XDEVS: A Hybrid System Modeling Framework (Kunyu Xie, Lin Zhang, Yuanjun Laili and Xiaohan Wang) An Integrated Intelligent Modeling and Simulation Language for Model-Based Systems Engineering (Lin Zhang, Fei Ye, Kunyu Xie, Pengfei Gu, Xiaohan Wang, Yuanjun Laili, Chun Zhao, Xuesong Zhang, Minjie Chen, Tinggu Lin and Zhen Chen) Modeling for Heterogeneous Objects Based on X Language: A Modeling Method of Algorithm-Hardware (Yue Liu and Chun Zhao) Data-Driven Modeling Method with Reverse Process (Guodong Yi, Lifan Yi, Zaizhao Zhang and Chuihui Li) Simulation-Oriented Model Reuse in Cyber-Physical Systems: A Method Based on Constrained Directed Graph (Wenzheng Liu, Heming Zhang, Chao Tang, Shuangfei Wu and Hongguang Zhu) Model Maturity Towards Modeling and Simulation: Concepts, Index System Framework and Evaluation Method (Lin Zhang, Ying Liu, Yuanjun Laili and Weicun Zhang) FPGA-Based Edge Computing: Task Modeling for Cloud-Edge Collaboration (Chuan Xiao and Chun Zhao) Hybrid Intelligent Modeling Approach for Online Predicting and Simulating Surface Temperature of HVs (Ming Tie, Hong Fang, Jianlin Wang and Weihua Chen) Knowledge-Driven Material Design Platform Based on the Whole-Process Simulation and Modeling (Gongzhuang Peng, Tie Li, Xiang Zhai, Wenzheng Liu and Heming Zhang) A Model Validation Method Based on the Orthogonal Polynomial Transformation and Area Metric (Huan Zhang, Wei Li, Ping Ma and Ming Yang) A Mixed Reality Simulation Evaluation Method for Complex System (Lijun Wang, Yang Xue, Yi Lv, Yufen Wu, Dawei Wang and Shuhong Xu) Readership: Graduate students, engineers and researchers specializing in the fields of System Engineering.

MODELING AND SIMULATION FOR COLLECTIVE DYNAMICS

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The thematic program Quantum and Kinetic Problems: Modeling, Analysis, Numerics and Applications was held at the Institute for Mathematical Sciences at the National University of Singapore, from September 2019 to March 2020. Leading experts presented tutorials and special lectures geared towards the participating graduate students and junior researchers. Readers will find in this significant volume four expanded lecture notes with self-contained tutorials on modeling and simulation for collective dynamics including individual and population approaches for population dynamics in mathematical biology, collective behaviors for Lohe type aggregation models, mean-field particle swarm optimization, and consensus-based optimization and ensemble Kalman inversion for global optimization problems with constraints. This volume serves to inspire graduate students and researchers who will embark into original research work in kinetic models for collective dynamics and their applications. Sample Chapter(s) Foreword Chapter 4: Consensus-based Optimization and Ensemble Kalman Inversion for Global Optimization Problems with Constraints Contents: Foreword Preface Individual and Population Approaches for Calibrating Division Rates in Population Dynamics: Application to the Bacterial Cell Cycle (Marie Doumic and Marc Hoffmann) Collective Dynamics of Lohe Type Aggregation Models (Seung-Yeal Ha and Dohyun Kim) Mean-field Particle Swarm Optimization (Sara Grassi, Hui Huang, Lorenzo Pareschi and Jinniao Qiu) Consensus-based Optimization and Ensemble Kalman Inversion for Global Optimization Problems with Constraints (José Antonio Carrillo, Claudia Totzeck and Urbain Vaes) Readership: Graduate students and researchers in computational and applied mathematics, mathematical biology, mathematical modeling, computational science and engineering.