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This textbook for students and practitioners presents a practical approach to decomposition techniques in optimization. It provides an appropriate blend of theoretical background and practical applications in engineering and science, which makes the book interesting for practitioners, as well as engineering, operations research and applied economics graduate and postgraduate students. "Decomposition Techniques in Mathematical Programming" is based on clarifying, illustrative and computational examples and applications from electrical, mechanical, energy and civil engineering as well as applied mathematics and economics. It addresses decomposition in linear programming, mixed-integer linear programming, nonlinear programming, and mixed-integer nonlinear programming, and provides rigorous decomposition algorithms as well as heuristic ones. Practical applications are developed up to working algorithms that can be readily used. The theoretical background of the book is deep enough to be of interest to applied mathematicians. It includes end of chapter exercises and the solutions to the even numbered exercises are included as an appendix.

Decomposition (Mathematics) --- Programming (Mathematics) --- Engineering mathematics. --- Engineering --- Engineering analysis --- Mathematical analysis --- Mathematics --- Mathematical programming --- Goal programming --- Algorithms --- Functional equations --- Mathematical optimization --- Operations research --- Probabilities --- Operations research. --- Mathematics. --- Mathematical and Computational Engineering. --- Operations Research, Management Science. --- Operations Research/Decision Theory. --- Applications of Mathematics. --- Math --- Science --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Applied mathematics. --- Management science. --- Decision making. --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Quantitative business analysis --- Statistical decision --- Decision making

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This textbook provides a detailed description of operation problems in power systems, including power system modeling, power system steady-state operations, power system state estimation, and electricity markets. The book provides an appropriate blend of theoretical background and practical applications, which are developed as working algorithms, coded in Octave (or Matlab) and GAMS environments.  This feature strengthens the usefulness of the book for both students and practitioners. Students will gain an insightful understanding of current power system operation problems in engineering, including: (i) the formulation of decision-making models, (ii) the familiarization with efficient solution algorithms for such models, and (iii) insights into these problems through the detailed analysis of numerous illustrative examples. The authors use a modern, “building-block” approach to solving complex problems, making the topic accessible to students with limited background in power systems. Solved examples are used to introduce new concepts and each chapter ends with a set of exercises.

Engineering. --- Energy policy. --- Energy and state. --- Energy systems. --- Power electronics. --- Power Electronics, Electrical Machines and Networks. --- Energy Systems. --- Energy Policy, Economics and Management. --- Production of electric energy or. --- Energy and state --- Power resources --- State and energy --- Industrial policy --- Energy conservation --- Electronics, Power --- Electric power --- Electronics --- Government policy

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This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering and operations research fields.

Methodology of economics --- Numerical methods of optimisation --- Operational research. Game theory --- Engineering sciences. Technology --- Business management --- Business economics --- Computer. Automation --- financieel management --- automatisering --- wiskunde --- ingenieurswetenschappen

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This book covers robust optimization theory and applications in the electricity sector. The advantage of robust optimization with respect to other methodologies for decision making under uncertainty are first discussed. Then, the robust optimization theory is covered in a friendly and tutorial manner. Finally, a number of insightful short- and long-term applications pertaining to the electricity sector are considered. Specifically, the book includes: robust set characterization, robust optimization, adaptive robust optimization, hybrid robust-stochastic optimization, applications to short- and medium-term operations problems in the electricity sector, and applications to long-term investment problems in the electricity sector. Each chapter contains end-of-chapter problems, making it suitable for use as a text. The purpose of the book is to provide a self-contained overview of robust optimization techniques for decision making under uncertainty in the electricity sector. The targeted audience includes industrial and power engineering students and practitioners in energy fields. The young field of robust optimization is reaching maturity in many respects. It is also useful for practitioners, as it provides a number of electricity industry applications described up to working algorithms (in JuliaOpt).

Macroeconomics --- Operational research. Game theory --- Mathematical statistics --- Planning (firm) --- Business management --- management --- mathematische modellen --- macro-economie --- econometrie --- operationeel onderzoek

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This addition to the ISOR series  introduces complementarity models in a straightforward and approachable manner and uses them to carry out an in-depth analysis of energy markets, including formulation issues and solution techniques.   In a nutshell, complementarity models generalize: a. optimization problems via their Karush-Kuhn-Tucker conditions b. non-cooperative games in which each player may be solving a separate but related optimization problem with potentially overall system constraints (e.g., market-clearing conditions) c. economic and engineering problems that aren’t specifically derived from optimization problems (e.g., spatial price equilibria) d. problems in which both primal and dual variables (prices) appear in the original formulation (e.g., The National Energy Modeling System (NEMS) or its precursor, PIES). As such, complementarity models are a very general and flexible modeling format. A natural question is why concentrate on energy markets for this complementarity approach?  As it turns out, energy or other markets that have game theoretic aspects are best modeled by complementarity problems.  The reason is that the traditional perfect competition approach no longer applies due to deregulation and restructuring of these markets and thus the corresponding optimization problems may no longer hold.  Also, in some instances it is important in the original model formulation to involve both primal variables (e.g., production) as well as dual variables (e.g., market prices) for public and private sector energy planning.  Traditional optimization problems can not directly handle this mixing of primal and dual variables but complementarity models can and this makes them all that more effective for decision-makers.

Commodity exchanges -- Computer simulation. --- Electric utilities -- Finance -- Computer simulation. --- Energy resources. --- Power resources. --- Commodity exchanges --- Power resources --- Management --- Finance --- Business & Economics --- Investment & Speculation --- Management Theory --- Computer simulation --- Computer simulation. --- Energy --- Energy resources --- Power supply --- Commodities exchange --- Commodity markets --- Exchanges, Commodity --- Exchanges, Produce --- Produce exchanges --- Business. --- Operations research. --- Decision making. --- Management science. --- Macroeconomics. --- Business and Management. --- Operation Research/Decision Theory. --- Macroeconomics/Monetary Economics//Financial Economics. --- Operations Research, Management Science. --- Natural resources --- Energy harvesting --- Energy industries --- Futures market --- Commercial products --- Produce trade --- Speculation --- Operations Research/Decision Theory. --- Economics --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Economics. --- Finance. --- Quantitative business analysis --- Problem solving --- Operations research --- Statistical decision --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management decisions --- Choice (Psychology) --- Decision making --- Commodity exchanges - Computer simulation