Column Generation is an insightful overview of the state of the art in integer programming column generation and its many applications. The volume begins with "A Primer in Column Generation" which outlines the theory and ideas necessary to solve large-scale practical problems, illustrated with a variety of examples. Other chapters follow this introduction on "Shortest Path Problems with Resource Constraints," "Vehicle Routing Problem with Time Window," "Branch-and-Price Heuristics," "Cutting Stock Problems," each dealing with methodological aspects of the field. Three chapters deal with transportation applications: "Large-scale Models in the Airline Industry," "Robust Inventory Ship Routing by Column Generation," and "Ship Scheduling with Recurring Visits and Visit Separation Requirements." Production is the focus of another three chapters: "Combining Column Generation and Lagrangian Relaxation," "Dantzig-Wolfe Decomposition for Job Shop Scheduling," and "Applying Column Generation to Machine Scheduling." The final chapter by François Vanderbeck, "Implementing Mixed Integer Column Generation," reviews how to set-up the Dantzig-Wolfe reformulation, adapt standard MIP techniques to the column generation context (branching, preprocessing, primal heuristics), and deal with specific column generation issues (initialization, stabilization, column management strategies).

This book contains selected papers from International Symposium for Production Research 2021, held on October 7–9, 2021, online, Turkey. The book reports recent advances in production engineering and operations. It explores topics including production research; production management; operations management; industry 4.0; industrial engineering; mechanical engineering; engineering management; and operational research. Presenting real-life applications, case studies, and mathematical models, this book is of interest to researchers, academics, and practitioners in the field of production and operation engineering. It provides both the results of recent research and practical solutions to real-world problems.

1 Introduction.- 1.1. Purpose of the Investigation.- 1.2. Methodology Used.- 1.3. Structure of the Book.- 2 Cutting and Packing Problems as Geometric-Combinatoric Problems.- 2.1. Basic Logical Structure.- 2.2. Phenomena of Cutting and Packing.- 2.2.1. Cutting and Packing in Spatial Dimensions.- 2.2.2. Cutting and Packing in Abstract Dimensions.- 2.2.3. Related Problems.- 2.3. Delimitation in Investigation.- 3 The Treatment of Cutting and Packing Problems in the Literature.- 3.1. Models as Idealized Images of Actual Phenomena.- 3.2. Sources on Cutting and Packing Problems.- 3.2.1. Differentiation According to Thematic Criteria.- 3.2.2. Differentiation According to Bibliographical Criteria.- 3.3. Delimitation of Investigated Literature.- 4 Systematic Catalogue of Properties for the Characterization of Cutting and Packing Problems.- 4.1. Basis for Characteristic Properties.- 4.2. Design of the Catalogue.- 4.3. Characteristics Based on the Logical Structure.- 4.3.1. Dimensionality.- 4.3.2. Type of Assignment.- 4.3.3. Characteristics of Large Objects and Small Items.- 4.3.4. Pattern Restrictions.- 4.3.5. Objectives.- 4.3.6. Status of Information and Variability of Data.- 4.3.7. Solution Methods.- 4.4. Reality-Based Characteristics.- 4.4.1. Kind of Objects and Items, and Branch of Industry.- 4.4.2. Planning Context.- 4.4.3. Software.- 4.5. Overview.- 5 Types of Cutting and Packing Problems in the Literature.- 5.1. Principles of Type Definition.- 5.2. Hierarchical Catalogue of Types.- 5.2.1. General Types.- 5.2.2. Special Types.- 5.2.3. Summarized Description of the Hierarchy of Types.- 5.3. Properties of the Derived Problem Types.- 6 Bin Packing Types (BP).- 6.1. One-dimensional Bin Packing Type (BP1).- 6.2. Two-dimensional Bin Packing Types (BP2).- 6.2.1. BP2-Type with a Heterogeneous Assortment of Large Objects.- 6.2.2. BP2-Type with a Homogeneous Assortment of Large Objects.- 6.3. Actual Bin Packing Problems.- 7 Cutting Stock Types (CS).- 7.1. One-dimensional Cutting Stock Types (CS1).- 7.1.1. CS1-Type with Continuous Quantity Measurement of Large Objects.- 7.1.2. CS1-Types with Discrete Quantity Measurement of Large Objects.- 7.1.2.1. Discrete CSl-Type with a Homogeneous Assortment of Large Objects.- 7.1.2.2. Discrete CSl-Type with a Heterogeneous Assortment of Large Objects.- 7.2. Two-dimensional Cutting Stock Types (CS2).- 7.2.1. CS2-Type with Non-rectangular Small Items.- 7.2.2. CS2-Types with Rectangular Small Items.- 7.2.2.1. Rectangular CS2-Types with Only One Large Object per Figure.- 7.2.2.2. Rectangular CS2-Types with Guillotine Patterns.- 7.2.2.3. Rectangular CS2-Type with Nested Patterns.- 7.3. Three-dimensional Cutting Stock Type (CS3).- 7.4. Actual Cutting Stock Problems.- 8 Knapsack Types (KS).- 8.1. One-dimensional Knapsack Type (KS1).- 8.2. Two-dimensional Knapsack Type (KS2).- 8.3. Three-dimensional Knapsack Type (KS3).- 8.4. Actual Knapsack Problems.- 9 Pallet Loading Types (PL).- 9.1. Two-dimensional Pallet Loading Type (PL2).- 9.2. Three-dimensional Pallet Loading Type (PL3).- 9.3. Actual Pallet Loading Problems.- 10 Conclusions.- I. A Bibliography of Further C&P-Problems.- A. Published Surveys.- B. Literary References not Closely Analysed.- C. Most Recent Sources.- II. Brief Description of the Characteristics.- III. LARS Data Base System.- List of Abbreviations for the Journals.- I. General Literature.- II. C&P-Literature.

Application of Decision Science in Business and Management is a book where each chapter has been contributed by a different author(s). The chapters introduce and demonstrate a decision-making theory to practice case studies. It demonstrates key results for each sector with diverse real-world case studies. Theory is accompanied by relevant analysis techniques, with a progressive approach building from simple theory to complex and dynamic decisions with multiple data points, including big data, lot of data, etc. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support analysis of multi-criteria decision-making problems with defined constraints and requirements. The book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of decision making. It is complementary to other sub-disciplines such as economics, finance, marketing, decision and risk analysis, etc.

This book includes a selection of refereed papers presented at the "Annual International Conference of the German Operations Research Society (OR2016)," which took place at the Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg, Germany, Aug. 30 - Sept. 2, 2016. Over 700 practitioners and academics from mathematics, computer science, business/economics, and related fields attended the conference. The scientific program included around 475 presentations on the theme Analytical Decision Making, focusing on the process of researching complex decision problems and devising effective solution methods towards better decisions. The book presents papers discussing classical mathematical optimization, statistics and simulation techniques. Such approaches are complemented by computer science methods and tools for the processing of data and the design and implementation of information systems. The book also examines recent advances in information technology, which allow big data volumes to be treated and enable real-time predictive and prescriptive business analytics to drive decisions and actions. Further, it includes problems modeled and treated under consideration of uncertainty, risk management, behavioral issues, and strategic decision situations.

The conference aims at forming a unique platform to bring together academicians and practitioners from industrial engineering and management engineering as well as from other disciplines working on production function applying the tools of operational research and production/operational management. Topics treated include: computer aided manufacturing, industry 4.0, big data and analytics, flexible manufacturing systems, fuzzy logic, industrial applications, information technologies in production management, optimization, production economy, production planning and control, productivity and performance management, project management, quality management, risk analysis and management, supply chain management.

This book presents novel and original metaheuristics developed to solve the cost-balanced traveling salesman problem. This problem was taken into account for the Metaheuristics Competition proposed in MESS 2018, Metaheuristics Summer School, and the top 4 methodologies ranked are included in the book, together with a brief introduction to the traveling salesman problem and all its variants. The book is aimed particularly at all researchers in metaheuristics and combinatorial optimization areas. Key uses are metaheuristics; complex problem solving; combinatorial optimization; traveling salesman problem.

An accessible treatment of the modeling and solution of integer programming problems, featuring modern applications and software In order to fully comprehend the algorithms associated with integer programming, it is important to understand not only how algorithms work, but also why they work. Applied Integer Programming features a unique emphasis on this point, focusing on problem modeling and solution using commercial software. Taking an application-oriented approach, this book addresses the art and science of mathematical modeling related to the mixed integer programming (MIP) framework and discusses the algorithms and associated practices that enable those models to be solved most efficiently. The book begins with coverage of successful applications, systematic modeling procedures, typical model types, transformation of non-MIP models, combinatorial optimization problem models, and automatic preprocessing to obtain a better formulation. Subsequent chapters present algebraic and geometric basic concepts of linear programming theory and network flows needed for understanding integer programming. Finally, the book concludes with classical and modern solution approaches as well as the key components for building an integrated software system capable of solving large-scale integer programming and combinatorial optimization problems. Throughout the book, the authors demonstrate essential concepts through numerous examples and figures. Each new concept or algorithm is accompanied by a numerical example, and, where applicable, graphics are used to draw together diverse problems or approaches into a unified whole. In addition, features of solution approaches found in today's commercial software are identified throughout the book. Thoroughly classroom-tested, Applied Integer Programming is an excellent book for integer programming courses at the upper-undergraduate and graduate levels. It also serves as a well-organized reference for professionals, software developers, and analysts who work in the fields of applied mathematics, computer science, operations research, management science, and engineering and use integer-programming techniques to model and solve real-world optimization problems.