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Books on the topic 'Mixed-integer model'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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1

Jeroslow, Robert G. Logic-based decision support: Mixed integer model formulation. North-Holland, 1989.

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2

Glen, John J. A mixed integer programming model for fisheries management. University of Edinburgh, Management School, 1994.

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3

Kirches, Christian. Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8202-8.

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4

Glen, John J. A mixed integer programming model of a single species fishery. Department of Business Studies, University of Edinburgh, 1990.

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5

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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6

Pochet, Yves. Production planning by mixed integer programming. Springer, 2011.

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7

Floudas, Christodoulos A. Nonlinear and mixed-integer optimization: Fundamentals and applications. Oxford University Press, 1995.

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8

McGarvey, David C. Mixed-strategy solutions to integer Prim-Read defense problems. Rand, 1987.

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9

McGarvey, David C. Mixed-strategy solutions to integer Prim-Read defense problems. Rand, 1987.

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10

Greg, Jones J., and Intermountain Research Station (Ogden, Utah), eds. A Heuristic process for solving mixed-integer land management and transportation planning models. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1991.

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11

Greg, Jones J., and Intermountain Research Station (Ogden, Utah), eds. A Heuristic process for solving mixed-integer land management and transportation planning models. U.S. Dept. of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, 1991.

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12

Logic-Based Decision Support - Mixed Integer Model Formulation. Elsevier, 1989. http://dx.doi.org/10.1016/s0167-5060(08)x7019-0.

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13

Jeroslow, R. G. Logic-Based Decision Support: Mixed Integer Model Formulation. Elsevier Science & Technology Books, 1989.

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14

Kirches, Christian. Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Vieweg Verlag, Friedr, & Sohn Verlagsgesellschaft mbH, 2011.

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15

Neibergs, Joseph Shannon. Application of a mixed integer programming model to determine optimal irrigation management strategies under conditions of limited water supplies. 1987.

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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

Geunes, Joseph. Operations Planning: Mixed Integer Optimization Models. Taylor & Francis Group, 2014.

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18

Geunes, Joseph. Operations Planning: Mixed Integer Optimization Models. Taylor & Francis Group, 2017.

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19

Geunes, Joseph. Operations Planning: Mixed Integer Optimization Models. Taylor & Francis Group, 2014.

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20

Operations Planning: Mixed Integer Optimization Models. Taylor & Francis Group, 2014.

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21

Maravelias, Christos T. Chemical Production Scheduling: Mixed-Integer Programming Models and Methods. Cambridge University Press, 2021.

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22

Maravelias, Christos T. Chemical Production Scheduling: Mixed-Integer Programming Models and Methods. University of Cambridge ESOL Examinations, 2021.

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23

Production Planning by Mixed Integer Programming. Springer, 2006.

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24

Floudas, Christodoulos A. Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications. Oxford University Press, 1995.

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25

A Heuristic process for solving mixed-integer land management and transportation planning models. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1991.

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26

Hamler-Dupras, Kevin. A mathematical analysis and critique of activity-based costing using mixed integer programming. 1997.

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27

Wolsey, Laurence A., and Yves Pochet. Production Planning by Mixed Integer Programming (Springer Series in Operations Research and Financial Engineering). Springer, 2006.

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28

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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Abstract:

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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