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Operations Research and Man...
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monografia Rebiun35344910 https://catalogo.rebiun.org/rebiun/record/Rebiun35344910 m o d | cr ||||||||||| 240221s2024 sz a ob 000 0 eng d 3-031-47206-3 UPNA0570550 MiAaPQ eng rda pn MiAaPQ MiAaPQ 519.3 23 Operations Research and Management Quantitative Methods for Planning and Decision-Making in Business and Economics Franz W. Peren and Thomas Neifer, editors 1st ed Cham, Switzerland Springer Nature Switzerland AG [2024] Cham, Switzerland Cham, Switzerland Springer Nature Switzerland AG 2024 1 online resource (270 pages) 1 online resource (270 pages) Text txt rdacontent computer c rdamedia online resource cr rdacarrier Springer Texts in Business and Economics Series Includes bibliographical references Intro -- Editors -- Preface -- Contents -- Part I Linear Optimization and Heuristics -- The Decision Tree Procedure -- 1 Introduction -- 1.1 Theoretical Classification of the Decision Tree Procedure -- 1.2 Basic Structure of a Decision Model -- 1.2.1 Decision Rule -- 1.2.2 Decision Domain -- 1.3 The Decision Tree -- 1.4 Process of Decision-Making with the Decision Tree -- 2 Methods of Decision Tree Procedure -- 2.1 Complete Enumeration -- 2.2 Incomplete Enumeration -- 2.3 Dynamic Optimization -- 2.3.1 Decomposition -- 2.3.2 Backward Calculation -- 2.3.3 Forward Calculation -- 2.4 Branching and Bounding -- 2.4.1 Branching -- 2.4.2 Bounding -- 3 Economic Relevance and Critical Appraisal -- 3.1 Economic Relevance -- 3.2 Critical Appraisal -- 4 Case Studies and Software -- 4.1 Case Study I -- 4.2 Case Study 2 -- 4.3 Example from Practice -- 5 Conclusion -- References -- Linear Optimization -- 1 Introduction -- 2 The Linear Programming Approach -- 3 Graphical Solution -- 4 Primal Simplex Algorithm -- 5 Simplex Tableau (Basic Structure) -- 6 Dual Simplex Algorithm -- References -- Cutting and Packaging Optimization -- 1 Introduction -- 2 Basics of C& -- P Problems -- 2.1 General Structure of C& -- P Problems -- 2.2 Characteristics of C& -- P Problems -- 2.2.1 Dimensionality -- 2.2.2 Assignment Restrictions -- 2.2.3 Type of Assortment -- 2.2.4 Shape of Small Items -- 2.2.5 Other Characteristics of C& -- P Problems -- 3 Dyckhoff's Classification of C& -- P Problems -- 4 Improved Typology -- 5 Solution Approach and ResultingWorkflow -- 6 Case Study: Packaging Optimization -- 7 Available Software -- 7.1 Software for Packing Problems -- 7.1.1 PackAssistant -- 7.1.2 MultiMix Modules -- 7.2 Software for Cutting Problems -- 7.2.1 CutPlanner -- 7.2.2 OptiCut -- 8 Conclusion -- References -- Queueing Theory -- 1 Overview 1.1 Fundamentals of the Queueing Theory -- 1.2 Economic Relevance -- 2 Queueing Model -- 2.1 Elements of the Queueing Model -- 2.1.1 Arrivals -- 2.1.2 Queue -- 2.1.3 Server -- 2.1.4 Departures -- 2.2 Structures and Queue Disciplines -- 3 Description of Queueing Systems -- 3.1 Kendall Notation -- 3.2 Excursion: Markov Process -- 3.3 Parameters -- 4 Example -- 5 Exercise -- 6 Summary -- References -- Sequencing Problems -- 1 Introduction -- 2 Definition of Terms -- 3 Solving Sequencing Problems -- 3.1 Traveling Salesman Problem -- 3.1.1 Heuristics -- 3.1.2 Exact Solution -- 3.1.3 Case Study -- 3.2 Flow Shop Scheduling Problem -- 3.2.1 Terms and Definitions of Flow Shop and Job Shop -- 3.2.2 Premises Using Johnson's Rule -- 3.2.3 Case Study -- References -- Regression Analysis Using Dummy Variables -- 1 Introduction -- 2 Methodology -- 2.1 Derivation of the Simple and Multiple Regression Function -- 2.2 Estimation of the Regression Parameters according to the Least Squares Method -- 2.2.1 Simple Regression -- 2.2.2 Multiple Regression -- 2.3 Integration of Categorial Influences via Dummy Variables -- 2.4 Quality Measures -- 2.4.1 Goodness of Fit (Global Quality Criteria) -- 2.4.2 Quality Criteria of the Regression Coefficients -- 2.4.3 Checking the Model Assumptions -- 3 Examples -- 4 Case Study -- 4.1 Data Preparation -- 4.2 Conducting the Regression Analysis -- 4.2.1 Single Regression -- 4.2.2 Multiple Regression with Dummy Variables -- 5 Conclusion -- References -- Heuristic Methods -- 1 Introduction -- 1.1 Definition and Characteristics -- 1.2 Analytical vs. Heuristic Methods -- 1.3 Heuristic Procedures -- 2 Selected Heuristic Problems -- 2.1 Traveling-Salesman Problem -- 2.1.1 Opening Procedures for the TSP -- 2.1.2 Improvement Procedures for the TSP -- 2.2 Dynamic Warehousing -- 2.3 Knapsack Problem -- 2.3.1 An Intuitive Approach 2.3.2 Optimization of the Approximation Method -- 3 Conclusion -- References -- Part II Simulation -- Simulation Processes in Business and Economics: Fundamentals of the Monte Carlo Simulation -- 1 Fundamentals of Simulation Processes -- 1.1 Definition and Purpose -- 1.2 Workload -- 1.3 Fields of Application -- 1.4 Types of Simulations -- 2 Monte Carlo Simulations -- 2.1 Introduction -- 2.2 Case Study Project Management -- References -- Markov Chain Monte Carlo Methods -- 1 Introduction -- 2 Theoretical Foundations -- 2.1 Bayes Theorem -- 2.2 Multivariate Distributions -- 2.3 Markov Chain -- 2.4 Monte Carlo Simulation -- 3 Markov Chain Monte Carlo Simulation -- 3.1 Metropolis(-Hastings) Approximation Algorithm -- 3.2 Gibbs Sampling Algorithm -- 4 Solving Traveling Salesman Problem using Metropolis Algorithm in Python -- References -- Part III Nonlinear Optimization -- Nonlinear Optimization: The Nelder-Mead Simplex Search Procedure -- 1 Introduction -- 2 Basic Properties of Nonlinear Optimization -- 3 Nonlinear Optimization Methods -- 3.1 Search Strategies -- 3.2 Deterministic Search Strategies -- 3.3 The Nelder-Mead Simplex Search Procedure -- 4 Conclusion -- References -- Dynamic Programming -- 1 Introduction -- 2 Theoretical Foundations -- 2.1 Definition and Properties of DO Models -- 2.2 Solution Principle of Dynamic Optimization -- 2.3 Bellman's Functional Equation Method -- 3 Applications -- 3.1 Basic Example: Finding the Shortest Route -- 3.2 Bellman-Ford Algorithm in Python -- 4 Conclusion -- References -- Part IV Project Management -- Network Analysis Method -- 1 Introduction -- 2 Theory -- 2.1 The Graph in the Context of Network Analysis Methods -- 2.2 Benefits of Network Analysis Methods -- 2.3 Different Types of Illustration Facilities -- 2.4 The Metra Potential Method (MPM) -- 2.4.1 The Network Diagram 2.4.2 Characteristics of the Network Diagram -- 2.4.3 Characteristics of the Network Diagram -- 3 Case Study Moving -- 3.1 Step 1: Identify activities, predecessors and determine durations -- 3.2 Step 2: Illustrate Dependencies in a Network Diagram -- 3.3 Step 3: Forward and Backward Calculation -- 3.4 Step 4: Float Calculation -- 3.5 Step 5: Critical Path -- 3.6 Case Study Insights -- 4 Computer-based Application with OmniPlan3 (macOS -- 5 Using Excel to Create a Precedence Diagram -- 6 Conclusion -- References -- The Peren-Clement Index -- 1 Introduction -- 1.1 Definition of a Foreign Direct Investment -- 1.2 Structural Features -- 2 Theory of Direct Investment -- 2.1 Justification for Foreign Direct Investment -- 2.2 Valuation Perspectives -- 3 Direct Investments and Site Selection -- 3.1 Framework for Decision-Making -- 3.1.1 Macro-environment -- 3.1.2 Localization (micro-environment) -- 3.2 Risk Assessment -- 3.3 Case Study -- 4 Conclusion -- References -- The Peren Theorem -- 1 Synopsis -- 2 The Current Human Lifestyle Cannot be Continued -- 3 The Peren Theorem -- 4 Options for Securing Human Livelihood -- 5 Individual Prosperity Effects -- References Mathematical optimization Peren, Franz W. editor Neifer, Thomas editor 9783031472053 Springer texts in business and economics