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Books on the topic 'Mixed Integer Linear Problem'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2025

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1

Mansini, Renata, Włodzimierz Ogryczak, and M. Grazia Speranza. Linear and Mixed Integer Programming for Portfolio Optimization. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18482-1.

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2

Iyengar, A. V. K. Optimal location of cement plants: Using mixed integer linear programming model. Institute for Financial Management and Research, 1992.

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3

Martello, Silvano. Knapsack problems: Algorithms and computer implementations. J. Wiley & Sons, 1990.

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4

Bulgakova, Irina, Yuliya Vertakova, Ol'ga Medvedeva, Sergey Medvedev, and Galina Chernyshova. Models and methods of system analysis in operations research. INFRA-M Academic Publishing LLC., 2024. https://doi.org/10.12737/2049708.

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The textbook discusses models and methods of operations research as applied methods for the study of socio-economic processes and phenomena. These methods include those that use the apparatus of mathematical programming, schedule theory, inventory management theory, game theory, etc. The authors consider general issues of system modeling, cite the mathematical apparatus of operations research (linear programming, simplex method, integer linear programming, network models, game theory, etc.), show the areas of practical application of methods of operations research using illustrative examples. The main theoretical material is accompanied by tasks for independent work, which contributes to a faster and deeper study of problem solving methods. Meets the requirements of the federal state educational standards of higher education of the latest generation. It is intended for students studying in the fields of "Applied Mathematics and Computer Science", "Business Informatics", "Management", "Economics", whose curricula include the disciplines "Game Theory and Operations Research", "Models and methods of discrete optimization", "Methods and models in Economics", "System analysis", "Planning and optimization of computing processes". The textbook will also be useful for graduate students, researchers of departments of economics and management, specialists involved in the analysis and modeling of the development of economic and social facilities.

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5

Floudas, Christodoulos A. Nonlinear and Mixed-Integer Optimization. Oxford University Press, 1995. http://dx.doi.org/10.1093/oso/9780195100563.001.0001.

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Filling a void in chemical engineering and optimization literature, this book presents the theory and methods for nonlinear and mixed-integer optimization, and their applications in the important area of process synthesis. Other topics include modeling issues in process synthesis, and optimization-based approaches in the synthesis of heat recovery systems, distillation-based systems, and reactor-based systems. The basics of convex analysis and nonlinear optimization are also covered and the elementary concepts of mixed-integer linear optimization are introduced. All chapters have several illustrations and geometrical interpretations of the material as well as suggested problems. Nonlinear and Mixed-Integer Optimization will prove to be an invaluable source--either as a textbook or a reference--for researchers and graduate students interested in continuous and discrete nonlinear optimization issues in engineering design, process synthesis, process operations, applied mathematics, operations research, industrial management, and systems engineering.

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6

LOPEZ, J. OPTIMIZATION with MATLAB. LINEAR PROGRAMMING and MIXED-INTEGER LINEAR PROGRAMMING. Independently Published, 2019.

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7

Mansini, Renata, M. Grazia Speranza, and Włodzimierz Ogryczak. Linear and Mixed Integer Programming for Portfolio Optimization. Springer London, Limited, 2015.

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8

Ogryczak, Włodzimierz, Renata Mansini, and M. Grazia Speranza. Linear and Mixed Integer Programming for Portfolio Optimization. Springer, 2015.

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9

Ogryczak, Włodzimierz, Renata Mansini, and M. Grazia Speranza. Linear and Mixed Integer Programming for Portfolio Optimization. Springer, 2016.

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10

Little, John D. C. Synchronization of Traffic Signals by Mixed-Integer Linear Programming. Creative Media Partners, LLC, 2015.

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11

Little, John D. C. Synchronization of Traffic Signals by Mixed-Integer Linear Programming. Creative Media Partners, LLC, 2018.

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12

Synchronization of Traffic Signals by Mixed-Integer Linear Programming. Creative Media Partners, LLC, 2022.

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13

Chvilicek, Jerome Leonard. Optimal farm plans for the Palouse using mixed integer linear programming techniques. 1989.

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14

Abichandani, Pramod, Hande Benson, and Moshe Kam. Mathematical Programming Approaches for Multi-Vehicle Motion Planning: Linear, Nonlinear, and Mixed Integer Programming. Now Publishers, 2013.

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15

Tawarmalani, M., and Nikolaos V. Sahinidis. Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming: Theory, Algorithms, Software, and Applications (Nonconvex Optimization and Its Applications). Springer, 2002.

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16

A Mixed Integer Linear Unit Commitment and Economic Dispatch Model for Thermo-Electric and Variable Renewable Energy Generators With Compressed Air Energy Storage. [publisher not identified], 2017.

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17

Bäck, Thomas. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099713.001.0001.

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This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.

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18

Michel, Bierlaire. Optimization: Principles and Algorithms. EPFL Press, 2015. http://dx.doi.org/10.55430/6116v1mb.

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Every engineer and decision scientist must have a good mastery of optimization, an essential element in their toolkit. Thus, this articulate introductory textbook will certainly be welcomed by students and practicing professionals alike. Drawing from his vast teaching experience, the author skillfully leads the reader through a rich choice of topics in a coherent, fluid and tasteful blend of models and methods anchored on the underlying mathematical notions (only prerequisites: first year calculus and linear algebra). Topics range from the classics to some of the most recent developments in smooth unconstrained and constrained optimization, like descent methods, conjugate gradients, Newton and quasi-Newton methods, linear programming and the simplex method, trust region and interior point methods. Furthermore elements of discrete and combinatorial optimization like network optimization, integer programming and heuristic local search methods are also presented. This book presents optimization as a modeling tool that beyond supporting problem formulation plus design and implementation of efficient algorithms, also is a language suited for interdisciplinary human interaction. Readers further become aware that while the roots of mathematical optimization go back to the work of giants like Newton, Lagrange, Cauchy, Euler or Gauss, it did not become a discipline on its own until World War Two. Also that its present momentum really resulted from its symbiosis with modern computers, which made it possible to routinely solve problems with millions of variables and constraints. With his witty, entertaining, yet precise style, Michel Bierlaire captivates his readers and awakens their desire to try out the presented material in a creative mode. One of the outstanding assets of this book is the unified, clear and concise rendering of the various algorithms, which makes them easily readable and translatable into any high level programming language. ''This is an addictive book that I am very pleased to recommend.'' Prof. Thomas M. Liebling

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