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Relevant bibliographies by topics / Operations research, programming (PPN617617589)

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Academic literature on the topic 'Operations research, programming (PPN617617589)'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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Journal articles on the topic "Operations research, programming (PPN617617589)"

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Roehrkasse, R. "Linear programming in operations research." IEEE Potentials 9, no. 4 (1990): 39–40. http://dx.doi.org/10.1109/45.65868.

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Hooker, J. N., and W. J. van Hoeve. "Constraint programming and operations research." Constraints 23, no. 2 (2017): 172–95. http://dx.doi.org/10.1007/s10601-017-9280-3.

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Milano, Michela, and Mark Wallace. "Integrating Operations Research in Constraint Programming." Annals of Operations Research 175, no. 1 (2009): 37–76. http://dx.doi.org/10.1007/s10479-009-0654-9.

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Milano, Michela, and Mark Wallace. "Integrating operations research in constraint programming." 4OR 4, no. 3 (2006): 175–219. http://dx.doi.org/10.1007/s10288-006-0019-z.

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Karabuk, Suleyman, and F. Hank Grant. "A Common Medium for Programming Operations-Research Models." IEEE Software 24, no. 5 (2007): 39–47. http://dx.doi.org/10.1109/ms.2007.125.

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Buckley, James J., Thomas Feuring, and Yoichi Hayashi. "Solving Fuzzy Problems in Operations Research." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (1999): 171–76. http://dx.doi.org/10.20965/jaciii.1999.p0171.

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Fuzzy optimization problems to which traditional methods - calculus and crisp algorithms - are not directly applicable have not been completely solved. We used evolutionary algorithms to produce good approximate solutions to fuzzy optimization problems including fully fuzzified linear programming, nonlinear fuzzy regression, neural net training, and fuzzy hierarchical analysis. We applied our evolutionary algorithm package to generating good approximate solutions to fuzzy optimization problems in operations research including the fuzzy shortest route problem and the fuzzy min-cost capacitated flow problem.

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De Cosmis, Sonia, and Renato De Leone. "The use of grossone in Mathematical Programming and Operations Research." Applied Mathematics and Computation 218, no. 16 (2012): 8029–38. http://dx.doi.org/10.1016/j.amc.2011.07.042.

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Elleuch, Souhir, and Bassem Jarboui. "Improved Memetic Programming algorithm." International Journal of Operational Research 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijor.2021.10032941.

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Lachhwani, Kailash. "On multi-level quadratic fractional programming problem with modified fuzzy goal programming approach." International Journal of Operational Research 37, no. 1 (2020): 135. http://dx.doi.org/10.1504/ijor.2020.10025873.

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Lachhwani, Kailash. "On multi-level quadratic fractional programming problem with modified fuzzy goal programming approach." International Journal of Operational Research 37, no. 1 (2020): 135. http://dx.doi.org/10.1504/ijor.2020.104227.

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Dissertations / Theses on the topic "Operations research, programming (PPN617617589)"

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Fahle, Torsten. "Integrating concepts from constraint programming and operations research algorithms." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=968544851.

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Makaya, Makaya L. "Interactive methods for multiple objective linear programming in decision support." Master's thesis, University of Cape Town, 2005. http://hdl.handle.net/11427/4385.

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Demir, Ramazan. "An approximate dynamic programming approach to discrete optimization." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9137.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2000.<br>Includes bibliographical references (leaves 181-189).<br>We develop Approximate Dynamic Programming (ADP) methods to integer programming problems. We describe and investigate parametric, nonparametric and base-heuristic learning approaches to approximate the value function in order to break the curse of dimensionality. Through an extensive computational study we illustrate that our ADP approach to integer programming competes successfully with existing methodologies including state of art commercial packages like CPLEX. Our benchmarks for comparison are solution quality, running time and robustness (i.e., small deviations in the computational resources such as running time for varying instances of same size). In this thesis, we particularly focus on knapsack problems and the binary integer programming problem. We explore an integrated approach to solve discrete optimization problems by unifying optimization techniques with statistical learning. Overall, this research illustrates that the ADP is a promising technique by providing near-optimal solutions within reasonable amount of computation time especially for large scale problems with thousands of variables and constraints. Thus, Approximate Dynamic Programming can be considered as a new alternative to existing approximate methods for discrete optimization problems.<br>by Ramazan Demir.<br>Ph.D.

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Huchette, Joseph Andrew. "Advanced mixed-integer programming formulations : methodology, computation, and application." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119282.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 193-203).<br>This thesis introduces systematic ways to use mixed-integer programming (MIP) to solve difficult nonconvex optimization problems arising in application areas as varied as operations, robotics, power systems, and machine learning. Our goal is to produce MIP formulations that perform extremely well in practice, requiring us to balance qualities often in opposition: formulation size, strength, and branching behavior. We start by studying a combinatorial framework for building MIP formulations, and present a complete graphical characterization of its expressive power. Our approach allows us to produce strong and small formulations for a variety of structures, including piecewise linear functions, relaxations for multilinear functions, and obstacle avoidance constraints. Second, we present a geometric way to construct MIP formulations, and use it to investigate the potential advantages of general integer (as opposed to binary) MIP formulations. We are able to apply our geometric construction method to piecewise linear functions and annulus constraints, producing small, strong general integer MIP formulations that induce favorable behavior in a branch-and-bound algorithm. Third, we perform an in-depth computational study of MIP formulations for nonconvex piecewise linear functions, showing that the new formulations devised in this thesis outperform existing approaches, often substantially (e.g. solving to optimality in orders of magnitude less time). We also highlight how high-level, easy-to-use computational tools, built on top of the JuMP modeling language, can help make these advanced formulations accessible to practitioners and researchers. Furthermore, we study high-dimensional piecewise linear functions arising in the context of deep learning, and develop a new strong formulation and valid inequalities for this structure. We close the thesis by answering a speculative question: Given a disjunctive constraint, what can we reasonably sacrifice in order to construct MIP formulations with very few integer variables? We show that, if we allow our formulations to introduce spurious "integer holes" in their interior, we can produce strong formulations for any disjunctive constraint with only two integer variables and a linear number of inequalities (and reduce this further to a constant number for specific structures). We provide a framework to encompass these MIP-with-holes formulations, and show how to modify standard MIP algorithmic tools such as branch-and-bound and cutting planes to handle the holes.<br>by Joseph Andrew Huchette.<br>Ph. D.

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Espinoza, Daniel G. "On Linear Programming, Integer Programming and Cutting Planes." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10482.

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In this thesis we address three related topic in the field of Operations Research. Firstly we discuss the problems and limitation of most common solvers for linear programming, precision. We then present a solver that generate rational optimal solutions to linear programming problems by solving a succession of (increasingly more precise) floating point approximations of the original rational problem until the rational optimality conditions are achieved. This method is shown to be (on average) only 20% slower than the common pure floating point approach, while returning true optimal solutions to the problems. Secondly we present an extension of the Local Cut procedure introduced by Applegate et al, 2001, for the Symmetric Traveling Salesman Problem (STSP), to the general setting of MIP problems. This extension also proves finiteness of the separation, facet and tilting procedures in the general MIP setting, and also provides conditions under which the separation procedure is guaranteed to generate cuts that separate the current fractional solution from the convex hull of the mixed-integer polyhedron. We then move on to explore some configurations for local cuts, realizing extensive testing on the instances from MIPLIB. Those results show that this technique may be useful in general MIP problems, while the experience of Applegate et al, shows that the ideas can be successfully applied to structures problems as well. Thirdly we present an extensive computational experiment on the TSP and Domino Parity inequalities as introduced by Letchford, 2000. This work also include a safe-shrinking theorem for domino parity inequalities, heuristics to apply the planar separation algorithm introduced by Letchford to instances where the planarity requirement does not hold, and several practical speed-ups. Our computational experience showed that this class of inequalities effectively improve the lower bounds from the best relaxations obtained with Concorde, which is one of the state of the art solvers for the STSP. As part of these experience, we solved to optimality the (up to now) largest two STSP instances, both of them belong to the TSPLIB set of instances and they have 18,520 and 33,810 cities respectively.

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Sharifi, Mokhtarian Faranak. "Analytic center cutting plane and path-following interior-point methods in convex programming and variational inequalities." Thesis, McGill University, 1997. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=35615.

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Interior-point methods have not only shown their efficiency for linear and some nonlinear programming problems, but also for cutting plane methods and large scale optimization. The analytic center cutting plane method uses the analytic center of the current polyhedral approximation of the feasible region to add a new cutting plane. In this thesis, analytic center cutting plane and path-following interior-point methodologies are used to solve the following problems: (1) convex feasibility problems defined by a deep cut separation oracle; (2) convex optimization problems involving a nonlinear objective and a constraint set defined implicitly by a separation oracle; (3) variational inequalities involving a nonlinear operator and a convex set explicitly defined; (4) variational inequalities involving an affine operator and a constraint set defined implicitly by a deep cut separation oracle; and (5) variational inequalities involving a nonlinear operator and a constraint set defined implicitly by a deep cut separation oracle. Here, the oracle is a routine that takes as input a test point. If the point belongs to the feasible region, it answers "yes", otherwise it answers "no" and returns a cut separating the point from the feasible region. Complexity bounds are established for algorithms developed for Cases 1, 2 and 4. The algorithm developed for Case 3 will be proven to be convergent, whereas, in Case 5, the developed algorithm will be shown to find an approximate solution in finite time.

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Domm, Maryanne. "Mathematical programming in data mining: Models for binary classification with application to collusion detection in online gambling." Diss., The University of Arizona, 2003. http://hdl.handle.net/10150/280270.

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Data mining is a semi-automated technique to discover patterns and trends in large amounts of data and can be used to build statistical models to predict those patterns and trends. One type of prediction model is a classifier, which attempts to predict to which group a particular item belongs. An important binary classifier, the Support Vector Machine classifier, uses non-linear optimization to find a hyperplane separating the two classes of data. This classifier has been reformulated as a linear program and as a pure quadratic program. We propose two modeling extensions to the Support Vector Machine classifier. The first, the Linearized Proximal Support Vector Machine classifier, linearizes the objective function of the pure quadratic version. This reduces the importance the classifier places on outlying data points. The second extension improves the conceptual accuracy of the model. The Integer Support Vector Machine classifier uses binary indicator variables to indicate potential misclassification errors and minimizes these errors directly. Performance of both these new classifiers was evaluated on a simple two dimensional data set as well as on several data sets commonly used in the literature and was compared to the original classifiers. These classifiers were then used to build a model to detect collusion in online gambling. Collusion occurs when two or more players play differently against each other than against the rest of the players. Since their communication cannot be intercepted, collusion is easier for online gamblers. However, collusion can still be identified by examining the playing style of the colluding players. By analyzing the record of play from online poker, a model to predict whether a hand contains colluding players or not can be built. We found that these new classifiers performed about as well as previous classifiers and sometimes worse and sometimes better. We also found that one form of online collusion could be detected, but not perfectly.

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Scott, Joseph D. "Optimally scheduling basic courses at the Defense Language Institute using integer programming." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://handle.dtic.mil/100.2/ADA447035.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, Sept. 2005.<br>Thesis Advisor(s): Robert F. Dell. "September 2005." Includes bibliographical references (p. 37-38). Also available in print.

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Yakowitz, Diana Schadl. "Two-stage stochastic linear programming: Stochastic decomposition approaches." Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185342.

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Stochastic linear programming problems are linear programming problems for which one or more data elements are described by random variables. Two-stage stochastic linear programming problems are problems in which a first stage decision is made before the random variables are observed. A second stage, or recourse decision, which varies with these observations compensates for any deficiencies which result from the earlier decision. Many applications areas including water resources, industrial management, economics and finance lead to two-stage stochastic linear programs with recourse. In this dissertation, two algorithms for solving stochastic linear programming problems with recourse are developed and tested. The first is referred to as Quadratic Stochastic Decomposition (QSD). This algorithm is an enhanced version of the Stochastic Decomposition (SD) algorithm of Higle and Sen (1988). The enhancements were designed to increase the computational efficiency of the SD algorithm by introducing a quadratic proximal term in the master program objective function and altering the manner in which the recourse function approximations are updated. We show that every accumulation point of an easily identifiable subsequence of points generated by the algorithm are optimal solutions to the stochastic program with probability 1. The various combinations of the enhancements are empirically investigated in a computational experiment using operations research problems from the literature. The second algorithm is an SD based algorithm for solving a stochastic linear program in which the recourse problem appears in the constraint set. This algorithm involves the use of an exact penalty function in the master program. We find that under certain conditions every accumulation point of a sequence of points generated by the algorithm is an optimal solution to the recourse constrained stochastic program, with probability 1. This algorithm is tested on several operations research problems.

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Kleinau, Peer Bruno Paul. "Application of genetic programming to finance and operations management /." Berlin : Logos-Verl, 2004. http://www.gbv.de/dms/zbw/377392103.pdf.

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Books on the topic "Operations research, programming (PPN617617589)"

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Operations research: An introduction. 9th ed. Prentice Hall, 2011.

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Rardin, Ronald L. Optimization in operations research. Prentice Hall, 1998.

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Optimization in operations research. Prentice Hall, 1998.

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Operations research: An introduction. 7th ed. Prentice Hall, 2002.

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Taha, Hamdy A. Operations research: An introduction. 5th ed. Macmillan, 1992.

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Operations research: An introduction. 5th ed. Macmillan, 1992.

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Operations research: Deterministic optimization models. Prentice Hall, 1995.

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Or, İlhan. Introduction to stochastic models in operations research. Boğaziçi University, School of Engineering, 1986.

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J, Sobel Matthew, ed. Stochastic models in operations research. Dover Publication, 2004.

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F, Bard Jonathan, ed. Operations research models and methods. Wiley, 2003.

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Book chapters on the topic "Operations research, programming (PPN617617589)"

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Eiselt, H. A., and C. L. Sandblom. "Linear Programming." In Operations Research. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10326-1_2.

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Eiselt, H. A., and C. L. Sandblom. "Multiobjective Programming." In Operations Research. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10326-1_3.

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Eiselt, H. A., and C. L. Sandblom. "Integer Programming." In Operations Research. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10326-1_4.

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Johnson, Ellis L. "Mathematical Programming." In Operations Research Proceedings. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74862-2_3.

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Kasana, Harvir Singh, and Krishna Dev Kumar. "Integer Programming." In Introductory Operations Research. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08011-5_11.

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Kasana, Harvir Singh, and Krishna Dev Kumar. "Dynamic Programming." In Introductory Operations Research. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08011-5_12.

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Kasana, Harvir Singh, and Krishna Dev Kumar. "Nonlinear Programming." In Introductory Operations Research. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08011-5_13.

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Kasana, Harvir Singh, and Krishna Dev Kumar. "Geometric Programming." In Introductory Operations Research. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08011-5_15.

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Kasana, Harvir Singh, and Krishna Dev Kumar. "Goal Programming." In Introductory Operations Research. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08011-5_16.

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Poler, Raúl, Josefa Mula, and Manuel Díaz-Madroñero. "Linear Programming." In Operations Research Problems. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5577-5_1.

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Conference papers on the topic "Operations research, programming (PPN617617589)"

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Beraldi, Patrizia, Antonio Violi, Maria Bruni, and Gianluca Carrozzino. "Dynamic Index Tracking via Stochastic Programming." In 8th International Conference on Operations Research and Enterprise Systems. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007573404430450.

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A. Oliveira, José, João Ferreira, Luís Dias, Manuel Figueiredo, and Guilherme Pereira. "Non Emergency Patients Transport - A Mixed Integer Linear Programming." In International Conference on Operations Research and Enterprise Systems. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005214902620269.

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Adasme, Pablo, Rafael Andrade, Janny Leung, and Abdel Lisser. "On a Traveling Salesman based Bilevel Programming Problem." In 6th International Conference on Operations Research and Enterprise Systems. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006190503290336.

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"On the Use of Copulas in Joint Chance-constrained Programming." In International Conference on Operations Research and Enterprise Systems. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004831500720079.

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"A Distributionally Robust Formulation for Stochastic Quadratic Bi-level Programming." In International Conference on Operations Research and Enterprise Systems. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004207100240031.

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Masmoudi, Meryem, and Fouad Ben Abdelaziz. "Applying the Recourse Goal Programming Approach to the Portfolio Selection Problem." In Annual International Conference on Operations Research and Statistics. Global Science & Technology Forum (GSTF), 2012. http://dx.doi.org/10.5176/2251-1938_ors57.

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Lampoudi, Sotiria, Eric Saunders, and Jason Eastman. "An Integer Linear Programming Solution to the Telescope Network Scheduling Problem." In International Conference on Operations Research and Enterprise Systems. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005207003310337.

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"Building Surgical Team with High Affinities - A Bicriteria Mixed-integer Programming Approach." In International Conference on Operations Research and Enterprise Systems. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004831804170424.

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"Linear Programming Formulation of the Elevator Trip Origin-destination Matrix Estimation Problem." In International Conference on Operations Research and Enterprise Systems. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004338502980303.

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C. González-Araya, Marcela, Carolina A. Urzúa-Bobadilla, and Luis G. Acosta Espejo. "Applying Mathematical Programming to Planning Bin Location in Apple Orchards." In 6th International Conference on Operations Research and Enterprise Systems. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006192203450352.

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