Relevant bibliographies by topics / Linear Programming Problem

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Linear Programming Problem'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "Linear Programming Problem"

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S. Mohan, S. Mohan, and Dr S. Sekar Dr. S. Sekar. "Linear Programming Problem with Homogeneous Constraints." Indian Journal of Applied Research 4, no. 3 (2011): 298–307. http://dx.doi.org/10.15373/2249555x/mar2014/90.

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D, Sempavazhaam, Jothi K, and Kamali S. Nandhini A. Princy Rebekah J. "A Study on Linear Programming Problem." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (2019): 903–4. http://dx.doi.org/10.31142/ijtsrd21539.

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Mahjoub Mohammed Hussein, Elfarazdag. "Application of Linear Programming (Transportation Problem)." International Journal of Science and Research (IJSR) 12, no. 3 (2023): 452–54. http://dx.doi.org/10.21275/sr21222020051.

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Gebreanenya, Haftom. "Integer Linear Programming Problem in Messebo Cement Factory." International journal of Emerging Trends in Science and Technology 03, no. 12 (2016): 4858–65. http://dx.doi.org/10.18535/ijetst/v3i12.10.

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Hussian, Abdel-Nasser, Mai Mohamed, Mohamed Abdel-Baset, and Florentin Smarandache. "Neutrosophic Linear Programming Problem." Mathematical Sciences Letters 6, no. 3 (2017): 319–24. http://dx.doi.org/10.18576/msl/060315.

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Ahuja, R. K. "Minimax linear programming problem." Operations Research Letters 4, no. 3 (1985): 131–34. http://dx.doi.org/10.1016/0167-6377(85)90017-3.

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Princy Flora, M. "Solving Fuzzy Linear Programming as Multi Objective Linear Programming Problem." Asian Journal of Science and Applied Technology 5, no. 1 (2016): 28–32. http://dx.doi.org/10.51983/ajsat-2016.5.1.2545.

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The constraints and the objective function of the fuzzy linear programming problem are converted into the multi-objective optimization problem (i.e.,) into an equivalent crisp linear problem.Finally, the multi-objective linear programming problem is converted into the weighted linear programming problem to show that they are independent of weights and obtained the complete optimal solution.
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Stetsyuk, P., O. Lykhovyd, and A. Suprun. "On Linear and Quadratic Two-Stage Transportation Problem." Cybernetics and Computer Technologies, no. 4 (December 31, 2020): 5–14. http://dx.doi.org/10.34229/2707-451x.20.4.1.

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Introduction. When formulating the classical two-stage transportation problem, it is assumed that the product is transported from suppliers to consumers through intermediate points. Intermediary firms and various kinds of storage facilities (warehouses) can act as intermediate points. The article discusses two mathematical models for two-stage transportation problem (linear programming problem and quadratic programming problem) and a fairly universal way to solve them using modern software. It uses the description of the problem in the modeling language AMPL (A Mathematical Programming Language) and depends on which of the known programs is chosen to solve the problem of linear or quadratic programming. The purpose of the article is to propose the use of AMPL code for solving a linear programming two-stage transportation problem using modern software for linear programming problems, to formulate a mathematical model of a quadratic programming two-stage transportation problem and to investigate its properties. Results. The properties of two variants of a two-stage transportation problem are described: a linear programming problem and a quadratic programming problem. An AMPL code for solving a linear programming two-stage transportation problem using modern software for linear programming problems is given. The results of the calculation using Gurobi program for a linear programming two-stage transportation problem, which has many solutions, are presented and analyzed. A quadratic programming two-stage transportation problem was formulated and conditions were found under which it has unique solution. Conclusions. The developed AMPL-code for a linear programming two-stage transportation problem and its modification for a quadratic programming two-stage transportation problem can be used to solve various logistics transportation problems using modern software for solving mathematical programming problems. The developed AMPL code can be easily adapted to take into account the lower and upper bounds for the quantity of products transported from suppliers to intermediate points and from intermediate points to consumers. Keywords: transportation problem, linear programming problem, AMPL modeling language, Gurobi program, quadratic programming problem.
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Ka, Pratik, S. Suma, and Vishwas B R. "Solving Linear Programming Problems Using AMPL Modeling LanguageSolving Linear Programming Problems Using AMPL Modeling Language." International Journal of Research and Review 9, no. 11 (2022): 66–69. http://dx.doi.org/10.52403/ijrr.20221110.

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Optimization problems arise in many contexts. A modelling language like AMPL makes it easier to experiment with formulations and use the right solvers to address the resultant optimization issues. Variables, objectives, constraints, sets of possible parameters, and notations that resemble well-known mathematical notation can all be stated using AMPL. The AMPL command language enables computation and display of data regarding the specifics of a problem and the solutions provided by solvers. It also enables the modification of problem formulations and the resolution of problem chains. Both continuous and discrete optimization issues are addressed by AMPL. In this paper, AMPL is used to solve different optimization problems such as Wyndor Glass problem, Transportation and Assignment problem and Purchase Planning problem. Keywords: Optimization, AMPL, Wyndor Glass Problem, Transportation and Assignment Problem, Purchase Planning Problem
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ZANGIABADI, M., and H. R. MALEKI. "A METHOD FOR SOLVING LINEAR PROGRAMMING PROBLEMS WITH FUZZY PARAMETERS BASED ON MULTIOBJECTIVE LINEAR PROGRAMMING TECHNIQUE." Asia-Pacific Journal of Operational Research 24, no. 04 (2007): 557–73. http://dx.doi.org/10.1142/s0217595907001395.

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In the real-world optimization problems, coefficients of the objective function are not known precisely and can be interpreted as fuzzy numbers. In this paper we define the concepts of optimality for linear programming problems with fuzzy parameters based on those for multiobjective linear programming problems. Then by using the concept of comparison of fuzzy numbers, we transform a linear programming problem with fuzzy parameters to a multiobjective linear programming problem. To this end, we propose several theorems which are used to obtain optimal solutions of linear programming problems with fuzzy parameters. Finally some examples are given for illustrating the proposed method of solving linear programming problem with fuzzy parameters.
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Dissertations / Theses on the topic "Linear Programming Problem"

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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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Cregger, Michael L. "The general mixed-integer linear programming problem an empirical analysis /." Instructions for remote access. Click here to access this electronic resource. Access available to Kutztown University faculty, staff, and students only, 1993. http://www.kutztown.edu/library/services/remote_access.asp.

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Hocking, Peter M. "Solving the binary integer bi-level linear programming problem /." Electronic version (PDF), 2004. http://dl.uncw.edu/etd/2004/hockingp/peterhocking.pdf.

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Tinti, Laura. "Mixed Integer Linear Programming Models for a Stowage Planning Problem." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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The aim of this thesis is to deepen the Containership Stowage Planning Problem (CSPP). In general terms, this problem consists of finding optimal plans for stowing containers into a containership, satisfying several restrictions. This topic has a lot of variations regarding the objective functions and the constraints required, depending on the situation taken into account. This dissertation is developed by referring to a real case, with its specific objective function and restrictions. In the first part of this thesis, an overview on the different approaches given by the literature is provided. After this outline, a Mixed Integer Linear Programming Model is proposed with the goal of finding feasible solutions for the CSPP. A consistent number of instances is generated to analyze how the model performs depending on the input parameters. The model is then tested by using CPLEX Solver in the mathematical programming and optimization modelling language AMPL. Finally, the importance of the stability of the vessel is underlined. Constraints concerning the ship stability are added to the model and, throughout other tests in AMPL, the computational results and the comparison to the results previously obtained are evaluated.
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DASTMARD, SOHOF. "The Asymmetric Travelling Salesman Problem : A Linear Programming Solution Approach." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157412.

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The travelling salesman problem is a well known optimization problem. The goal is to nd theshortest tour that visits each city in a given list exactly once. Despite the simple problem statementit belongs to the class of NP-complete problems. Its importance arises from a plethora of applicationsas well as a theoretical appeal. The asymmetric TSP is not as well researched as the symmetric TSP,in this paper we focus on a solution approach suitable for the asymmetric case. We demonstrate howa linear programming formulation can be used to solve the problem. We also show the limitationsof this solution approach and provide suggestions for improving it.i
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Sariklis, Dimitrios. "Open Vehicle Routing Problem : description, formulations and heuristic methods." Thesis, London School of Economics and Political Science (University of London), 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265252.

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Tanksley, Latriece Y. "Interior point methods and kernel functions of a linear programming problem." Click here to access thesis, 2009. http://www.georgiasouthern.edu/etd/archive/spring2009/latriece_y_tanksley/tanksley_latriece_y_200901_ms.pdf.

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Thesis (M.S.)--Georgia Southern University, 2009.<br>"A thesis submitted to the Graduate Faculty of Georgia Southern University in partial fulfillment of the requirements for the degree Master of Science." Directed by Goran Lesaja. ETD. Includes bibliographical references (p. 76) and appendices.
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de, Farias Ismael Jr. "A polyhedral approach to combinatorial complementarity programming problems." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/25574.

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Islam, Mohammad Tauhidul, and University of Lethbridge Faculty of Arts and Science. "Approximation algorithms for minimum knapsack problem." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2009, 2009. http://hdl.handle.net/10133/1304.

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Knapsack problem has been widely studied in computer science for years. There exist several variants of the problem, with zero-one maximum knapsack in one dimension being the simplest one. In this thesis we study several existing approximation algorithms for the minimization version of the problem and propose a scaling based fully polynomial time approximation scheme for the minimum knapsack problem. We compare the performance of this algorithm with existing algorithms. Our experiments show that, the proposed algorithm runs fast and has a good performance ratio in practice. We also conduct extensive experiments on the data provided by Canadian Pacific Logistics Solutions during the MITACS internship program. We propose a scaling based e-approximation scheme for the multidimensional (d-dimensional) minimum knapsack problem and compare its performance with a generalization of a greedy algorithm for minimum knapsack in d dimensions. Our experiments show that the e- approximation scheme exhibits good performance ratio in practice.<br>x, 85 leaves ; 29 cm
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Adams, Warren Philip. "The mixed-integer bilinear programming problem with extensions to zero-one quadratic programs." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/74711.

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This research effort is concerned with a class of mathematical programming problems referred to as Mixed-Integer Bilinear Programming Problems. This class of problems, which arises in production, location-allocation, and distribution-application contexts, may be considered as a discrete version of the well-known Bilinear Programming Problem in that one set of decision variables is restricted to be binary valued. The structure of this problem is studied, and special cases wherein it is readily solvable are identified. For the more general case, a new linearization technique is introduced and demonstrated to lead to a tighter linear programming relaxation than obtained through available linearization methods. Based on this linearization, a composite Lagrangian relaxation-implicit enumeration-cutting plane algorithm is developed. Extensive computational experience is provided to test the efficiency of various algorithmic strategies and the effects of problem data on the computational effort of the proposed algorithm. The solution strategy developed for the Mixed-Integer Bilinear Programming Problem may be applied, with suitable modifications,. to other classes of mathematical programming problems: in particular, to the Zero-One Quadratic Programming Problem. In what may be considered as an extension to the work performed on the Mixed-Integer Bilinear Programming Problem, a solution strategy based on an equivalent linear reformulation is developed for the Zero-One Quadratic Programming Problem. The strategy is essentially an implicit enumeration algorithm which employs Lagrangian relaxation, Benders' cutting planes, and local explorations. Computational experience for this problem class is provided to justify the worth of the proposed linear reformulation and algorithm.<br>Ph. D.
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Books on the topic "Linear Programming Problem"

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

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

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

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

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

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

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Brendel, Thomas. Dialoggestützte Tourenplanung unter besonderer Berücksichtigung von Belieferungszeitintervallen. P. Lang, 1987.

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

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

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

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Book chapters on the topic "Linear Programming Problem"

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Shah, Nita H., and Poonam Prakash Mishra. "One-Dimensional Optimization Problem." In Non-Linear Programming. CRC Press, 2020. http://dx.doi.org/10.4324/9781003105213-1.

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Shah, Nita H., and Poonam Prakash Mishra. "One-Dimensional Optimization Problem." In Non-Linear Programming. CRC Press, 2020. http://dx.doi.org/10.1201/9781003105213-1.

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Padberg, Manfred. "The Linear Programming Problem." In Linear Optimization and Extensions. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-12273-0_2.

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Alevras, Dimitres, and Manfred W. Padberg. "The Linear Programming Problem." In Linear Optimization and Extensions. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56628-8_2.

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Maros, István. "The Linear Programming Problem." In Computational Techniques of the Simplex Method. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0257-9_1.

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Luderer, Bernd, Volker Nollau, and Klaus Vetters. "Linear Programming. Transportation Problem." In Mathematical Formulas for Economists. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04079-5_14.

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Borkar, Vivek S., Vladimir Ejov, Jerzy A. Filar, and Giang T. Nguyen. "Linear Programming Based Algorithms." In Hamiltonian Cycle Problem and Markov Chains. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3232-6_7.

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Luderer, Bernd, Volker Nollau, and Klaus Vetters. "Linear Programming and Transportation Problem." In Mathematical Formulas for Economists. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-12431-4_13.

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Ahmad, Firoz, and Ahmad Yusuf Adhami. "Spherical Fuzzy Linear Programming Problem." In Decision Making with Spherical Fuzzy Sets. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45461-6_19.

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Skalna, Iwona. "Parametric Interval Linear Programming Problem." In Parametric Interval Algebraic Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75187-0_6.

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Conference papers on the topic "Linear Programming Problem"

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Wójtowicz, Daniel, Krzysztof Puszyński, and Adam Gałuszka. "PDDL optimal planning as the Linear Programming Problem in a seaport container terminal." In 2024 28th International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, 2024. http://dx.doi.org/10.1109/mmar62187.2024.10680834.

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Davoudi, Niloofar, Farhad Hamidi, and Hassan Mishmast Nehi. "Triangular fuzzy bilevel linear programming problem." In 2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS). IEEE, 2020. http://dx.doi.org/10.1109/cfis49607.2020.9238756.

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Zhou, Weijie, and Xinru Peng. "Linear Programming Method and Diet Problem." In 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI). IEEE, 2022. http://dx.doi.org/10.1109/icetci55101.2022.9832221.

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Arbaiy, Nureize Binti, and Junzo Watada. "Linear Fractional Programming for Fuzzy Random Based Possibilistic Programming Problem." In 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM). IEEE, 2012. http://dx.doi.org/10.1109/cimsim.2012.42.

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El-Sayed, Mohamed E. M., and T. S. Jang. "Large Scale Structural Optimization With Linear Goal Programming." In ASME 1990 Design Technical Conferences. American Society of Mechanical Engineers, 1990. http://dx.doi.org/10.1115/detc1990-0070.

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Abstract This paper presents a method for solving large scale structural optimization problems using linear goal programming techniques. The method can be used as a multicriteria optimization tool since goal programming removes the difficulty of having to define an objective function and constraints. It also has the capacity of handling rank ordered design objectives or goals. The method uses finite element analysis, linear goal programming techniques and successive linearization to obtain the solution for the nonlinear goal optimization problems. The general formulation of the structural optimization problem into a nonlinear goal programming form is presented. The successive linearization method for the nonlinear goal optimization problem is discussed. To demonstrate the validity of the method, as a design tool, the solution of the minimum weight structural optimization problem with stress constraints for 10, 25 and 200 truss problems are included.
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Veeramani, C., and M. Sumathi. "Fuzzy Mathematical programming approach for solving Fuzzy Linear Fractional Programming problem." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622568.

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Suprajitno, Herry, and Ismail bin Mohd. "Transformation method for solving interval linear programming problem." In INTERNATIONAL CONFERENCE ON MATHEMATICS, COMPUTATIONAL SCIENCES AND STATISTICS 2020. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0042592.

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Малаханова, А. Г. "SOLUTION OF A LINEAR PROGRAMMING PROBLEM IN SCILAB." In САПР и моделирование в современной электронике. Брянский государственный технический университет, 2020. http://dx.doi.org/10.51932/9785907271739_387.

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Xie, Xunzi, Shi Zhang, Yizhe Chen, and Yifan Pan. "Linear Programming: A Diet Problem with Methane Emission." In International Conference on Health Big Data and Intelligent Healthcare. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0011361000003438.

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Hoang, Dao Minh, Tran Ngoc Thang, Nguyen Danh Tu, and Nguyen Viet Hoang. "Stochastic Linear Programming Approach for Portfolio Optimization Problem." In 2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT). IEEE, 2021. http://dx.doi.org/10.1109/icmlant53170.2021.9690552.

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Reports on the topic "Linear Programming Problem"

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Khan, Mahreen. Lessons from Adaptive Programming. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/k4d.2022.142.

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The aim of adaptive programming (AP) is to produce adaptive, flexible, iterative, responsive, problem-driven, politically smart, locally led programmes which are effective and efficient and meet donor requirements for accountability. This is a rapid desk review of recent literature on AP including academic and grey sources. Section 2 covers the main challenges and barriers to successful implementation of AP. Key success factors are covered in Section 3. Selecting the appropriate monitoring and evaluation tools such as outcome harvesting or adapted versions of Value for Money to assist in measuring outcomes and embedding learning is key to successful AP, particularly in governance programmes, where results are usually long-term, non-linear and causality can be difficult to specifically trace back to the donor-funded intervention. Section 4 details three case studies from the governance arena as this report was requested to assist in designing adaptive governance programmes. Thus, the State Accountability and Voice Initiative (SAVI) from Nigeria, Chakua Hatua from Tanzania, and Within and Without the State (WWS) from conflict regions are included to show how flexible indicators, donor communication and negotiation, empowering teams and adopting monitoring and evaluation tools assisted in successful AP outcomes in different locations and political contexts. The challenges faced and drawbacks of certain processes were fed into efficient feedback loops fostering cross-communication, adaptation, and modification to ensure procedures and policies were changed accordingly. Sources used are primarily from the previous 5 years, as per K4D norms, unless the work is seminal, such as the ODI Report (2016) Doing Development Differently, which encouraged over 60 countries to sign up for the AP methodology. This review found a substantive body of literature on AP methodology the relative recency of academic attention on AP in the development less evidence is available on case studies of AP in the development sector, as there are not many ongoing projects and even fewer have been completed and results assessed (ICF, 2019). There is also a lack of case studies on how dynamic, empowered, innovative teams successfully apply adaptive programming ideas, particularly providing behavioural insights about such teams (Cooke, 2017) as well as little attention to precipitating and sustaining behaviour change in institutions over the longer term (Power, 2017).
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Ratmanski, Kiril, and Sergey Vecherin. Resilience in distributed sensor networks. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45680.

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With the advent of cheap and available sensors, there is a need for intelligent sensor selection and placement for various purposes. While previous research was focused on the most efficient sensor networks, we present a new mathematical framework for efficient and resilient sensor network installation. Specifically, in this work we formulate and solve a sensor selection and placement problem when network resilience is also a factor in the optimization problem. Our approach is based on the binary linear programming problem. The generic formulation is probabilistic and applicable to any sensor types, line-of-site and non-line-of-site, and any sensor modality. It also incorporates several realistic constraints including finite sensor supply, cost, energy consumption, as well as specified redundancy in coverage areas that require resilience. While the exact solution is computationally prohibitive, we present a fast algorithm that produces a near-optimal solution that can be used in practice. We show how such formulation works on 2D examples, applied to infrared (IR) sensor networks designed to detect and track human presence and movements in a specified coverage area. Analysis of coverage and comparison of sensor placement with and without resilience considerations is also performed.
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Liebchen, Christian. An Integer Programming Model to Assign Train Drivers to Good Positions in Basic Turni. Technische Hochschule Wildau, 2024. http://dx.doi.org/10.15771/1935.

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We are reporting on a project on duty rostering of a train operating company in Germany. There, in the past, rostering took place on a purely individual basis. As the goal of the project, the train drivers should work according to a set of 20 essentially fixed basic turni. These plans took into effect from January 2024 on. In this extended abstract, we are focusing on a specific task that had to be resolved within the transition process. In particular, the fixed turni are intended to repeat periodically, most of them after 17 weeks. In particular, for that one turnus is equipped with train drivers in a balanced way, 17 employees have to be assigned to this turnus, each of them to one of the 17 different weeks of the turnus to start. To cover each day of the week, the weeks of the turnus typically differ in their number of working days. Since the holidays of the train drivers had been already planned in advance, depending on the starting week, during the entire year, a smaller or larger number of working days of the turnus could be erased by the individual holidays. In order to preserve as many working days as necessary, a straightforward mixed-integer linear optimization problem has been designed and solved, to decide which train driver should start to work in which week of the turnus. The solution of this optimization run has been finally applied in practice.
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Boggs, P. T., P. D. Domich, J. E. Rogers, and C. Witzgall. An interior-point method for linear and quadratic programming problems. National Institute of Standards and Technology, 1991. http://dx.doi.org/10.6028/nist.ir.4556.

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Hough, P. D. Stable computation of search directions for near-degenerate linear programming problems. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/477537.

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Ariyawansa, K. A., and Andrew J. Felt. A Collection of Multistage Stochastic Linear Programming Test Problems (Version 1). Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada384441.

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Leichner, S. A., G. B. Dantzig, and J. W. Davis. A strictly improving linear programming alorithm based on a series of Phase 1 problems. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10153260.

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Leichner, S., G. Dantzig, and J. Davis. A strictly improving linear programming alorithm based on a series of Phase 1 problems. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/5159733.

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Gonzalez-Lima, Maria D., Richard A. Tapia, and Robert M. Thrall. On the Construction of Strong Complementarity Slackness Solutions for DEA Linear Programming Problems Using a Primal-Dual Interior-Point Method. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada444967.

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Looi, Chee-Kit, Longkai Wu, Peter Sen Kee Seow, and Wendy Huang. Researching and developing pedagogies using unplugged and computational thinking approaches for teaching computing in the schools. National Institute of Education, Nanyang Technological University, Singapore, 2020. https://doi.org/10.32658/10497/22601.

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INTRODUCTION/BACKGROUND In 2017, Singapore’s Ministry of Education implemented a new GCE ‘O’ Level Computing curriculum. The new curriculum is a distinct shift from the teaching students on the use of software technology to the development of Computational Thinking skills and programming competencies. Computing thinking skills are associated with problem solving, reasoning and logic skills that all students should develop. As Singapore moves to implement a new curriculum with a greater emphasis on the development of computational thinking and programming, the following are some of the challenges that must be addressed: 1. Teachers’ Pedagogical Knowledge in teaching Computing 2. Teachers’ Competency and Knowledge on Computational Thinking STATEMENT OF PROBLEMS This project has a focus on using and integrating the unplugged approach as introductory activities for teaching computing as a pedagogy. It focuses on helping students to understand concepts in Computational Thinking. The approach also fits very well to the teaching and learning environment in a typical secondary school classroom. We worked with the teachers from collaborating schools to design and co-design unplugged activities, observed how they enacted the lessons in the classroom. This would help us to understand how teachers interpret computational thinking and adapt the unplugged approaches with their teaching practice. Also, we would like to study students' learning outcomes as a result of the teaching. The existing practice and research of unplugged teaching has the following problems: 1. There is no systematic integration. Among the many topics in computing, there are not many topics that match unplugged activities. 2. For the first-line teachers, the available public accessible resources do not help much. It can only be used when they encounter related topics. Even if there are corresponding resources on the Internet, many teachers are not keen on adopting unplugged teaching methods, due to the time and effort needed to prepare and to enact the lessons. 3. The existing unplugged teaching resources are designed with the goal of mobilizing students' interest and engagement, and more in-depth practice and research in transiting from teaching with unplugged methods to programming is needed. PURPOSE OF STUDY The purposes of this proposed research study are the following: • Develop and evaluate pedagogies linked to teaching CT. We introduce teaching unplugged as an effective student-centered approach to introducing computing concepts without the use of computers, and then we design follow-up activities and pedagogies that move students forward in the crucial computational experiences. • Assess the effect on teachers. Teachers’ pedagogical content knowledge will be assessed to understand the level they started with, and the level they would have attained after the workshops and teaching in class. Classroom observations will be held to study the teachers’ enactment of computing lessons. We want to understand the territory of teachers’ dispositions for, attitudes toward and stereotypes concerning CT and Computing. • Assess the effect on students. Students’ work will be analysed to assess their level of comprehension and application of computing concepts, and this will be done through prior experience surveys, pre-post computing perceptions survey, pre-post computing tests, quizzes and computing assignments. These are steps towards developing an assessment framework for CT.
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