linear programming methods and applications pdf

Linear Programming Methods And Applications Pdf

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Linear programming LP , also called linear optimization is a method to achieve the best outcome such as maximum profit or lowest cost in a mathematical model whose requirements are represented by linear relationships. Linear programming is a special case of mathematical programming also known as mathematical optimization.

Optimization Problems And Solutions Pdf

The Journal of Applied Research and Technology JART is a bimonthly open access journal that publishes papers on innovative applications, development of new technologies and efficient solutions in engineering, computing and scientific research. JART publishes manuscripts describing original research, with significant results based on experimental, theoretical and numerical work.

The journal does not charge for submission, processing, publication of manuscripts or for color reproduction of photographs. JART classifies research into the following main fields: Material Science Biomaterials, carbon, ceramics, composite, metals, polymers, thin films, functional materials and semiconductors.

Computer Science Computer graphics and visualization, programming, human-computer interaction, neural networks, image processing and software engineering. Industrial Engineering Operations research, systems engineering, management science, complex systems and cybernetics applications and information technologies Electronic Engineering Solid-state physics, radio engineering, telecommunications, control systems, signal processing, power electronics, electronic devices and circuits and automation.

Instrumentation engineering and science Measurement devices pressure, temperature, flow, voltage, frequency etc. SRJ is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and qualitative measure of the journal's impact.

SNIP measures contextual citation impact by wighting citations based on the total number of citations in a subject field. In this paper, it is pointed out that the existing general form of such fully fuzzy linear programming problems in which all the parameters are represented by such flat fuzzy numbers for which is valid only if there is not a negative sign. However, if there is a negative sign, then the existing general form of fully fuzzy linear programming problems is not valid.

Thus, a new general form is proposed.. Linear programming is one of the most frequently applied operation research techniques. Although it has been investigated and expanded for more than six decades by many researchers and from various points of view, it is still useful to develop new approaches in order to fit better real-world problems within the framework of linear programming. In conventional approach, parameters of linear programming models must be well defined and precise.

However, in a real-world environment, this is not a realistic assumption. Usually, the value of many parameters of a linear programming model is estimated by experts. Clearly, it cannot be assumed that the knowledge of experts is precise enough. Bellman and Zadeh [2] proposed the concept of decision making in fuzzy environments. After that, a number of researchers have exhibited their interest to solve the fuzzy linear programming problems [ 1 , 5—30 ]. In this paper, the shortcomings of existing general form of fully fuzzy linear programming problems are pointed out and a new general form of fully fuzzy linear programming problems is proposed.

This paper is organized as follows: In Section 2, some basic definitions and arithmetic operations are presented. In Section 3, the shortcomings of existing general form of fully fuzzy linear programming problems are pointed out. In Section 4, a new general form of fully fuzzy linear programming problems is proposed and the advantages of the proposed form over the existing form are discussed. Conclusions are discussed in Section 5. In this section, some basic definitions and arithmetic operations are presented [20].

Definition 2. In this section, the arithmetic operations between LR flat fuzzy numbers are presented [4]. Subject to. However, if all the parameters of P 2 are represented by LR flat fuzzy numbers then it is not genuine to use the fully fuzzy linear programming problem P 2 to find the fuzzy optimal solution of real life problems due to the following reasons:.

Example 3. A market research conducted recently according to consumer preferences demonstrated that the assortments shown in Table 1 are to be in demand. Most preferred assortments of biscuits.

For the biscuits, the fuzzy manufacturing capacity and fuzzy costs are shown in Table 2. Fuzzy manufacturing capacity and fuzzy cost for biscuits. Formulate a model to find the production schedule which maximizes the fuzzy profit by assuming that there are no market restrictions. Using the existing general form of fully fuzzy linear programming problems P 2 , the chosen problem can be formulated into the following fully fuzzy linear programming problem.

To the best of our knowledge, only the existing method [1] can be used to find the fuzzy optimal value of such fully fuzzy linear programming problems in which the parameters are represented by LR flat fuzzy numbers. In this section, to show the shortcomings of the existing general form of fully fuzzy linear programming problems P 2 , the fuzzy optimal value of fully fuzzy linear programming problem P 3 obtained by using the existing method [1] is shown in Table 3.

Results of the chosen problem using existing formulation P 3. In the objective function of fully fuzzy linear programming formulation P 3 of the problem, chosen in Example 3. Remark 1. The shortcomings, pointed out in Section 3, will also occur in the existing general form of fuzzy linear programming problems [ 5—11 , 18—30 ].

In this section, to solve the shortcomings of the existing general form of fully fuzzy linear programming problems, pointed out in Section 3, a new general form of the fully fuzzy linear programming problems is proposed. Replacing all the crisp parameters of the crisp linear programming problems P 4 by fuzzy parameters general form of the fully fuzzy linear programming problems P 5 can be written as.

Because in the proposed general form P 5 the subtraction of LR flat fuzzy numbers is not occurring, hence using the proposed general form the shortcomings of the existing general form pointed out in Section 3 are solved. To show the advantage of the proposed general form over the existing general form, it was demonstrated that if the problem chosen in Example 3.

Using the proposed general form of fully fuzzy linear programming problems P 5 , the problem, chosen in Example 3. Fuzzy optimal value of the formulated problem P 6 by using the existing method [1] is shown in Table 4. Results of the chosen problem using proposed formulation P 6. Therefore, by using the proposed general form of fully fuzzy linear programming problems P 5 all the shortcomings, pointed out in Section 3, are solved. Based on the present study it can be concluded that it is better to use the proposed general form of fully fuzzy linear programming problems as it was compared to the existing general form of fully fuzzy linear programming problems.

I, Dr. Amit Kumar, want to acknowledge the adolescent inner blessings of Mehar. I believe that Mehar is an angel for me and without Mehar's blessing it would not be possible to develop the idea proposed in this paper.

Mehar is the lovely daughter of Parmpreet Kaur Research Scholar under my supervision. Inicio Journal of Applied Research and Technology. Previous article Next article. Issue 5. Pages October Download PDF. Amit Kumar. Jagdeep Kaur. This item has received.

Under a Creative Commons license. Article information. Table 1. Most preferred assortments of biscuits.. Table 2. Fuzzy manufacturing capacity and fuzzy cost for biscuits..

Table 3. Results of the chosen problem using existing formulation P Table 4. Results of the chosen problem using proposed formulation P Show more Show less. Thus, a new general form is proposed. LR flat fuzzy numbers. Allahviranloo, F. Lotfi, M. Kiasary, N. Kiani, L. Solving fully fuzzy linear programming problem by the ranking functhion. Applied Matematical Sciences, 2 , pp. Bellman, L. Management Science, 17 , pp. Dehghan, B. Hashemi, M. Computational methods for solving fully fuzzy linear systems.

Applied Mathematics and Computation, , pp. Dubois, H. Ebrahimnejad, S. A dual simplex method for bounded linear programmes with fuzzy numbers. International Journal of Mathematics in Operational Research, 2 , pp. Nasseri, F. Bounded linear programs with trapezoidal fuzzy numbers. A primal-dual method for linear programming problems with fuzzy variables. European Journal of Industrial Engineering, 4 , pp. Nasseri, S.

Linear programming: methods and applications

In real-world problems related to finance, business, and management, mathematicians and economists frequently encounter optimization problems. In this classic book, George Dantzig looks at a wealth of examples and develops linear programming methods for their solutions. He begins by introducing the basic theory of linear inequalities and describes the powerful simplex method used to solve them. Treatments of the price concept, the transportation problem, and matrix methods are also given, and key mathematical concepts such as the properties of convex sets and linear vector spaces are covered. George Dantzig is properly acclaimed as the "father of linear programming. It can be used to minimize traffic congestion or to maximize the scheduling of airline flights. He formulated its basic theoretical model and discovered its underlying computational algorithm, the "simplex method," in a pathbreaking memorandum published by the United States Air Force in early

This book introduces the latest theory and applications in optimization. It emphasizes constrained optimization, beginning with a substantial treatment of linear programming and then proceeding to convex analysis, network flows, integer programming, quadratic programming, and convex optimization. Readers will discover a host of practical business applications as well as non-business applications. Topics are clearly developed with many numerical examples worked out in detail. Specific examples and concrete algorithms precede more abstract topics. With its focus on solving practical problems, the book features free C programs to implement the major algorithms covered, including the two-phase simplex method, primal-dual simplex method, path-following interior-point method, and homogeneous self-dual methods.

The Journal of Applied Research and Technology JART is a bimonthly open access journal that publishes papers on innovative applications, development of new technologies and efficient solutions in engineering, computing and scientific research. JART publishes manuscripts describing original research, with significant results based on experimental, theoretical and numerical work. The journal does not charge for submission, processing, publication of manuscripts or for color reproduction of photographs. JART classifies research into the following main fields: Material Science Biomaterials, carbon, ceramics, composite, metals, polymers, thin films, functional materials and semiconductors. Computer Science Computer graphics and visualization, programming, human-computer interaction, neural networks, image processing and software engineering. Industrial Engineering Operations research, systems engineering, management science, complex systems and cybernetics applications and information technologies Electronic Engineering Solid-state physics, radio engineering, telecommunications, control systems, signal processing, power electronics, electronic devices and circuits and automation.

Grey linear programming: a survey on solving approaches and applications

Optimization models are used extensively in almost all areas of decision-making, such as engineering design and financial portfolio selection. This site presents a focused and structured process for optimization problem formulation, design of optimal strategy, and quality-control tools that include validation, verification, and post-solution activities. Enter a word or phrase in the dialogue box, e. In deterministic models good decisions bring about good outcomes.

Linear Programming and Extensions

Papers in Honor of Saul Gass’ 80th Birthday

The book is an edited volume from leading research scholars in the field of Operations Research, focusing on future perspectives in OR. Each of the contributors offers their perspective looking forward to the further development of the field. The theme will provide pivotal interest in the book because of prominence of the contributors and Saul Gass' position as one of the founders of OR and his involvement in writing about the history of OR. The history of operations research is of considerable interest and this book takes a pivotal perspective of OR's history by examining current trends and the future of the field. Skip to main content Skip to table of contents.

Most users should sign in with their email address. If you originally registered with a username please use that to sign in. To purchase short term access, please sign in to your Oxford Academic account above. Don't already have an Oxford Academic account? Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Shenoy Published Computer Science. A straighforward introduction to the concepts of linear programming and their applications. Includes several applications of linear programming to real-life problems in management and numerous exercises. Save to Library. Create Alert.

In this section, you will learn about real world applications of linear programming and related methods. In practice, linear programs can contain thousands of variables and constraints. However, in order to make the problems practical for learning purposes, our problems will still have only several variables. Now that we understand the main concepts behind linear programming, we can also consider how linear programming is currently used in large scale real-world applications.

Perspectives in Operations Research

The purpose of this paper is to survey and express the advantages and disadvantages of the existing approaches for solving grey linear programming in decision-making problems. After presenting the concepts of grey systems and grey numbers, this paper surveys existing approaches for solving grey linear programming problems and applications.

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