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Добірка наукової літератури з теми "LINEAR PROGRAMMING PROBLEM (LPP)"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "LINEAR PROGRAMMING PROBLEM (LPP)"

1

Borza, Mojtaba, and Azmin Sham Rambely. "A Linearization to the Sum of Linear Ratios Programming Problem." Mathematics 9, no. 9 (April 29, 2021): 1004. http://dx.doi.org/10.3390/math9091004.

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Optimizing the sum of linear fractional functions over a set of linear inequalities (S-LFP) has been considered by many researchers due to the fact that there are a number of real-world problems which are modelled mathematically as S-LFP problems. Solving the S-LFP is not easy in practice since the problem may have several local optimal solutions which makes the structure complex. To our knowledge, existing methods dealing with S-LFP are iterative algorithms that are based on branch and bound algorithms. Using these methods requires high computational cost and time. In this paper, we present a non-iterative and straightforward method with less computational expenses to deal with S-LFP. In the method, a new S-LFP is constructed based on the membership functions of the objectives multiplied by suitable weights. This new problem is then changed into a linear programming problem (LPP) using variable transformations. It was proven that the optimal solution of the LPP becomes the global optimal solution for the S-LFP. Numerical examples are given to illustrate the method.
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2

Mustafa, Rebaz B., and Nejmaddin A. Sulaiman. "A New Approach to Solving Linear Fractional Programming Problem with Rough Interval Coefficients in the Objective Function." Ibn AL- Haitham Journal For Pure and Applied Sciences 35, no. 2 (April 20, 2022): 70–83. http://dx.doi.org/10.30526/35.2.2809.

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This paper presents a linear fractional programming problem (LFPP) with rough interval coefficients (RICs) in the objective function. It shows that the LFPP with RICs in the objective function can be converted into a linear programming problem (LPP) with RICs by using the variable transformations. To solve this problem, we will make two LPP with interval coefficients (ICs). Next, those four LPPs can be constructed under these assumptions; the LPPs can be solved by the classical simplex method and used with MS Excel Solver. There is also argumentation about solving this type of linear fractional optimization programming problem. The derived theory can be applied to several numerical examples with its details, but we show only two examples for promising.
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3

Kucukbay, Fusun, and Ceyhun Araz. "Portfolio selection problem: a comparison of fuzzy goal programming and linear physical programming." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 6, no. 2 (April 20, 2016): 121–28. http://dx.doi.org/10.11121/ijocta.01.2016.00284.

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Investors have limited budget and they try to maximize their return with minimum risk. Therefore this study aims to deal with the portfolio selection problem. In the study two criteria are considered which are expected return, and risk. In this respect, linear physical programming (LPP) technique is applied on Bist 100 stocks to be able to find out the optimum portfolio. The analysis covers the period April 2009- March 2015. This period is divided into two; April 2009-March 2014 and April 2014 – March 2015. April 2009-March 2014 period is used as data to find an optimal solution. April 2014-March 2015 period is used to test the real performance of portfolios. The performance of the obtained portfolio is compared with that obtained from fuzzy goal programming (FGP). Then the performances of both method, LPP and FGP are compared with BIST 100 in terms of their Sharpe Indexes. The findings reveal that LPP for portfolio selection problem is a good alternative to FGP.
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4

Bharati, S. K., and S. R. Singh. "Interval-Valued Intuitionistic Fuzzy Linear Programming Problem." New Mathematics and Natural Computation 16, no. 01 (March 2020): 53–71. http://dx.doi.org/10.1142/s1793005720500040.

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In many existing methods of linear programming problem (LPP), precise values of parameters have been used but parameters of LPP are imprecise and ambiguous due to incomplete information. Several approaches and theories have been developed for dealing LPP based on fuzzy set (FS), intuitionistic fuzzy set (IFS) which are characterized by membership degree, membership and non-membership degrees, respectively. It’s interesting to note that single membership and non-membership degrees do not deal properly the state of uncertainty and hesitation. Further, we face a kind of uncertainty occurs a kind of uncertainty. Interval-valued intuitionistic fuzzy sets (IV-IFS) is a perfect key for handling uncertainty and hesitation than FS and IFS. In this paper, we define an interval-valued intuitionistic fuzzy number (IV-IFN) and its expected interval and expected values. We also introduce the concept of interval-valued intuitionistic fuzzy linear programming problem (IV-IFLPP). Further, we find the solutions of IV-IFLPP and compare the obtained optimal solutions with existing methods [D. Dubey and A. Mehra, Linear programming with Triangular Intuitionistic Fuzzy Numbers, in Proc. of the 7th Conf. and of the European Society for Fuzzy Logic and Technology (EUSFLAT-LFA 2011), R. Parvathi and C. Malathi, Intuitionistic fuzzy linear optimization, Notes on Intuitionistic Fuzzy Sets 18 (2012) 48–56]. Proposed technique may be used successfully in various areas in the formulation of our country’s five year plans, these include transportation, food-grain storage, urban development, national, state and district level plans, etc., The Indian Railways may use IV-IFLPP technique for linking different railway zones in more realistic way. Agricultural research institutes may use proposed technique for crop rotation mix of cash crops, food crops and fertilizer mix. Airlines can apply IV-IFLPP in the selection of routes and allocation of aircrafts to different routes. Private and public sector oil refineries may use IV-IFLPP for blending of oil ingredients to produce finished petroleum products.
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5

Mitlif, Rasha Jalal. "An Application Model for Linear Programming with an Evolutionary Ranking Function." Ibn AL-Haitham Journal For Pure and Applied Sciences 35, no. 3 (July 20, 2022): 146–54. http://dx.doi.org/10.30526/35.3.2817.

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One of the most important methodologies in operations research (OR) is the linear programming problem (LPP). Many real-world problems can be turned into linear programming models (LPM), making this model an essential tool for today's financial, hotel, and industrial applications, among others. Fuzzy linear programming (FLP) issues are important in fuzzy modeling because they can express uncertainty in the real world. There are several ways to tackle fuzzy linear programming problems now available. An efficient method for FLP has been proposed in this research to find the best answer. This method is simple in structure and is based on crisp linear programming. To solve the fuzzy linear programming problem (FLPP), a new ranking function (RF) with the trapezoidal fuzzy number (TFN) is devised in this study. The fuzzy quantities are de-fuzzified by applying the proposed ranking function (RF) transformation to crisp value linear programming problems (LPP) in the objective function (OF). Then the simplex method (SM) is used to determine the best solution (BS). To demonstrate our findings, we provide a numerical example (NE).
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6

Khalifa, Hamiden Abd El-Wahed, Majed Alharbi, and Pavan Kumar. "A new method for solving quadratic fractional programming problem in neutrosophic environment." Open Engineering 11, no. 1 (January 1, 2021): 880–86. http://dx.doi.org/10.1515/eng-2021-0088.

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Abstract In the current study, a neutrosophic quadratic fractional programming (NQFP) problem is investigated using a new method. The NQFP problem is converted into the corresponding quadratic fractional programming (QFP) problem. The QFP is formulated by using the score function and hence it is converted to the linear programming problem (LPP) using the Taylor series, which can be solved by LPP techniques or software (e.g., Lingo). Finally, an example is given for illustration.
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7

Junaid Basha, M., and S. Nandhini. "A fuzzy based solution to multiple objective LPP." AIMS Mathematics 8, no. 4 (2023): 7714–30. http://dx.doi.org/10.3934/math.2023387.

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<abstract><p>This study presents a Fuzzy Multiple Objective Linear Programming Problem (FMOLPP) method to solve the Linear Programming Problem (LPP). Initially Multiple Objective Linear Programming Problem (MOLPP) is solved using Chandra Sen's approach along with various types of mean approaches. Furthermore, FMOLPP is solved using Chandra Sen's approach and various categories of fuzzy mean techniques. The simplex form is used to solve the LPP, where the three-tuple symmetric triangular fuzzy number with the constraints of the fuzzy objective function is considered. We have presented a comparative study of optimum values of MOLPP with optimum values of FMOLPP, to show the significance of our proposed method.</p></abstract>
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8

Mitlif, Rasha Jalal, Raghad I. Sabri, and Eman Hassan Ouda. "A Novel Algorithm to Find the Best Solution for Pentagonal Fuzzy Numbers with Linear Programming Problems." Ibn AL-Haitham Journal For Pure and Applied Sciences 36, no. 2 (April 20, 2023): 301–5. http://dx.doi.org/10.30526/36.2.2957.

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Fuzzy numbers are used in various fields such as fuzzy process methods, decision control theory, problems involving decision making, and systematic reasoning. Fuzzy systems, including fuzzy set theory. In this paper, pentagonal fuzzy variables (PFV) are used to formulate linear programming problems (LPP). Here, we will concentrate on an approach to addressing these issues that uses the simplex technique (SM). Linear programming problems (LPP) and linear programming problems (LPP) with pentagonal fuzzy numbers (PFN) are the two basic categories into which we divide these issues. The focus of this paper is to find the optimal solution (OS) for LPP with PFN on the objective function (OF) and right-hand side. New ranking function (RF) approaches for solving fuzzy linear programming problems (FLPP) with a pentagonal fuzzy number (PFN) have been proposed, based on new ranking functions (N RF). The simplex method (SM) is very easy to understand. Finally, numerical examples (NE) are used to demonstrate the suggested approach's computing process.
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9

Carutasu, Vasile. "Considerations on Cycling in the Case of Linear Programming Problem (Lpp)." International conference KNOWLEDGE-BASED ORGANIZATION 24, no. 3 (June 1, 2018): 14–19. http://dx.doi.org/10.1515/kbo-2018-0130.

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Abstract Ever since the onset of algorithms for determining the optimal solution or solutions for a linear programming problem (LPP), the question of the possibility of occurrence of cycling when one or other of these algorithms are applied was born. Thus, the fundamental question regarding this issue is under what conditions the cyclic phenomenon appears for a problem of linear programming and how to construct examples in which to do so, and as a continuation of it, which methods can be developed to avoid this phenomenon. In this study we will present some aspects regarding this issue starting from the primal simplex algorithm, by highlighting some general aspects that occur when this phenomenon happens
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10

Güzel, Nuran. "A Proposal to the Solution of Multiobjective Linear Fractional Programming Problem." Abstract and Applied Analysis 2013 (2013): 1–4. http://dx.doi.org/10.1155/2013/435030.

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Анотація:
We have proposed a new solution to the Multiobjective Linear Fractional Programming Problem (MOLFPP). The proposed solution is based on a theorem that deals with nonlinear fractional programming with single objective function and studied in the work by Dinkelbach, 1967. As a new contribution, we have proposed that is an efficient solution of MOLFPP if is an optimal solution of problem , where is for all . Hence, MOLFPP is simply reduced to linear programming problem (LPP). Some numerical examples are provided in order to illustrate the applications of the proposed method. The optimization software package, namely, WinQSB (Chang, 2001), has been employed in the computations.
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Дисертації з теми "LINEAR PROGRAMMING PROBLEM (LPP)"

1

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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2

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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3

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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4

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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5

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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6

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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7

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.
"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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8

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.
x, 85 leaves ; 29 cm
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9

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.
Ph. D.
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10

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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Книги з теми "LINEAR PROGRAMMING PROBLEM (LPP)"

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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Частини книг з теми "LINEAR PROGRAMMING PROBLEM (LPP)"

1

Bajalinov, Erik B. "Special LFP Problems." In Linear-Fractional Programming Theory, Methods, Applications and Software, 245–86. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9174-4_9.

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2

Shah, Nita H., and Poonam Prakash Mishra. "One-Dimensional Optimization Problem." In Non-Linear Programming, 1–14. First edition. | Boca Raton, FL: CRC Press, an imprint of Taylor & Francis Group, LLC, 2021. | Series: Mathematical engineering, manufacturing, and management sciences: CRC Press, 2020. http://dx.doi.org/10.4324/9781003105213-1.

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3

Shah, Nita H., and Poonam Prakash Mishra. "One-Dimensional Optimization Problem." In Non-Linear Programming, 1–14. First edition. | Boca Raton, FL: CRC Press, an imprint of Taylor & Francis Group, LLC, 2021. | Series: Mathematical engineering, manufacturing, and management sciences: CRC Press, 2020. http://dx.doi.org/10.1201/9781003105213-1.

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4

Maros, István. "The Linear Programming Problem." In Computational Techniques of the Simplex Method, 3–18. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0257-9_1.

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5

Padberg, Manfred. "The Linear Programming Problem." In Linear Optimization and Extensions, 25–32. Berlin, Heidelberg: 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, 39–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56628-8_2.

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Luderer, Bernd, Volker Nollau, and Klaus Vetters. "Linear Programming. Transportation Problem." In Mathematical Formulas for Economists, 129–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04079-5_14.

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8

Borkar, Vivek S., Vladimir Ejov, Jerzy A. Filar, and Giang T. Nguyen. "Linear Programming Based Algorithms." In Hamiltonian Cycle Problem and Markov Chains, 113–42. New York, NY: 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, 127–36. Berlin, Heidelberg: 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, 455–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45461-6_19.

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Тези доповідей конференцій з теми "LINEAR PROGRAMMING PROBLEM (LPP)"

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Prasad, Enagandula. "Divergent solutions of linear programming problem (LPP) and system of linear equations (SLE) for a particular coefficient matrix." In 1ST INTERNATIONAL CONFERENCE ON MATHEMATICAL TECHNIQUES AND APPLICATIONS: ICMTA2020. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0026264.

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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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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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De, Moumita Deb P. K. "Study of possibility programming in stochastic fuzzy multiobjective linear fractional programming problem." In 2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2014. http://dx.doi.org/10.1109/isco.2014.7103970.

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Звіти організацій з теми "LINEAR PROGRAMMING PROBLEM (LPP)"

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

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Анотація:
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.), October 2022. http://dx.doi.org/10.21079/11681/45680.

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Анотація:
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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