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Books on the topic 'LINEAR PROGRAMMING PROBLEM (LPP)'

Author: Grafiati
Published: 11 September 2023

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1

Linear complementarity, linear and nonlinear programming. Berlin: Heldermann, 1988.

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2

Jong-Shi, Pang, and Stone Richard E, eds. The linear complementarity problem. Philadelphia: Society for Industrial and Applied Mathematics, 2009.

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3

Linear programming. New York: Birkhäuser, 2009.

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4

service), SpringerLink (Online, ed. Linear Programming and Generalizations: A Problem-based Introduction with Spreadsheets. Boston, MA: Springer Science+Business Media, LLC, 2011.

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5

Kozlov, M. V. Primenenie t︠s︡elochislennogo lineĭnogo programmirovanii︠a︡ s posledovatelʹnym iskli︠u︡cheniem t︠s︡iklov dli︠a︡ reshenii︠a︡ zadachi kommivoi︠a︡zhera. Moskva: Vychislitelʹnyĭ T︠S︡entr im. A.A. Dorodnit︠s︡yna Rossiĭskoĭ akademii nauk, 2012.

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6

Ben-Ayed, Omar. Solving a real world highway network design problem using bilevel linear programming. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1988.

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7

Degeneracy graphs and the neighbourhood problem. Berlin: Springer-Verlag, 1986.

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8

Schübbe, Jochen. Tourenplanung für die Entleerung von Bringsystemen zur Wertstoffsammlung in Stadtgebieten. Münster: Lit, 1992.

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9

Eberhard, Ulrich. Mehr-Depot-Tourenplanung. München: Minerva, 1987.

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10

Goldberg, Andrew V. Combinatorial algorithms for the generalized circulation problem. Stanford, Calif: Dept. of Computer Science, Stanford University, 1988.

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11

Brendel, Thomas. Dialoggestützte Tourenplanung unter besonderer Berücksichtigung von Belieferungszeitintervallen. Frankfurt am Main: P. Lang, 1987.

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12

Instituti i Informatikës dhe Matematikës së Aplikuar (Akademia e Shkencave e RSH), ed. Përmbledhje punimesh në informatikë dhe matematikë të aplikuar. Tiranë: Republika e Shqipërisë Akademia e Shkencave, Instituti i Informatikës dhe Matematikës së Aplikuar, 1995.

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13

Bender, Filmore Edmund. Optimization for profit: A decision maker's guide to linear programming. New York: Haworth Press, 1992.

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14

Nakhla, Afaf Fouad. Update the transportation problem. Cairo: Institute of National Planning, 1985.

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15

Coleman, Thomas F. A quadradically[sic]-convergent algorithm for the linear programming problem with lower and uppper [sic] bounds. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1990.

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16

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

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17

Queueing theory: A linear algebraic approach. 2nd ed. New York: Springer, 2009.

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18

United States. National Aeronautics and Space Administration., ed. Dynamic flow management problems in air transportation. [Washington, DC: National Aeronautics and Space Administration, 1997.

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19

A, Bushenkov V., and Kamenev G. K, eds. Interactive decision maps: Approximation and visualization of Pareto frontier. Boston: Kluwer Academic Publishers, 2004.

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20

Milan, Vlach, ed. Generalized concavity in fuzzy optimization and decision analysis. Boston: Kluwer Academic Publishers, 2002.

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21

Nakov, Svetlin. Fundamentals of Computer Programming with C#: The Bulgarian C# Book. Sofia, Bulgaria: Svetlin Nakov, 2013.

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22

1948-, Langholtz Harvey J., ed. Resource-allocation behavior. Boston: Kluwer Academic Publishers, 2003.

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23

1923-, Thompson Gerald Luther, ed. Optimal control theory: Applications to management science and economics. 2nd ed. Boston: Kluwer Academic Publishers, 2000.

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24

Denardo, Eric V. Linear Programming and Generalizations: A Problem-based Introduction with Spreadsheets. Springer, 2017.

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25

A scalable parallel algorithm for multiple objective linear programs. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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26

A scalable parallel algorithm for multiple objective linear programs. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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27

Bioinspired Algorithms For The Vehicle Routing Problem. Springer, 2008.

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28

Tavares, Jorge, and Francisco Baptista Pereira. Bio-inspired Algorithms for the Vehicle Routing Problem. Springer, 2010.

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29

Tavares, Jorge, and Francisco Baptista Pereira. Bio-Inspired Algorithms for the Vehicle Routing Problem. Springer, 2008.

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30

Kahan, Gerald, Filmore E. Bender, and W. Charles Mylander. Optimization for Profit: A Decision Maker's Guide to Linear Programming. Food Products Press, 1991.

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31

Linear Optimization for Business: Theory and Practical Application. Taylor & Francis Group, 2019.

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32

Singer, Marcos. Linear Optimization for Business: Theory and Practical Application. Taylor & Francis Group, 2019.

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33

Lotov, Alexander V., Vladimir A. Bushenkov, and Georgy K. Kamenev. Interactive Decision Maps: Approximation and Visualization of Pareto Frontier (Applied Optimization). Springer, 2004.

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34

Lotov, Alexander V., Vladimir A. Bushenkov, and Georgy K. Kamenev. Interactive Decision Maps: Approximation and Visualization of Pareto Frontier. Springer London, Limited, 2013.

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35

Vlach, Milan, and Jaroslav Ramík. Generalized Concavity in Fuzzy Optimization and Decision Analysis. Springer, 2001.

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36

Resource-allocation behavior. Boston, MA: Kluwer Academic Publishers, 2004.

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37

Langholtz, Harvey J. Resource-Allocation Behavior. Springer, 2012.

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38

Langholtz, Harvey J., Antoinette T. Marty, Christopher T. Ball, and Eric C. Nolan. Resource-Allocation Behavior. Springer, 2002.

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39

Sethi, Suresh P. Optimal Control Theory: Applications to Management Science and Economics. Springer International Publishing AG, 2022.

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40

Sethi, Suresh P. Optimal Control Theory: Applications to Management Science and Economics. Springer, 2018.

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41

Thompson, Gerald L., and Suresh P. Sethi. Optimal Control Theory - Applications to Management Science and Economics (Second Edition). 2nd ed. Springer, 2000.

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42

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

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Abstract:
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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