Solving optimization problems in MS Running title: Solving optimization problems with Excel's Solver add-in (original) (raw)
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Solving Linear Programming Problems with the “Solver” in MS Excel
Linear Programming technique is one of the important decision making tools in business operations that is used to optimize a solution within the limitations of resources available. Graphical Method and Simplex Method are generally used to solve LP problems. Though various computer applications are now available for solving LP problems, as a pack in handy, MS Excel is useful to solve LP problems with its "SOLVER" application. This paper illustrates solving a simple LP problem with the Solver.
On the Use of Integer Programming versus Evolutionary Solver in Spreadsheet Optimization
INFORMS Transactions on Education, 2005
T he introduction of the evolutionary solver in Frontline Systems' Premium Solver for Education allows students to use any functions in Excel for modeling optimization problems. As a result, instructors teaching optimization now face a dilemma of how much emphasis to place on "traditional" integer programming versus the unrestricted but heuristic approach of the evolutionary solver. Our goal in this work is to shed some light on the tradeoffs in these two modeling approaches. We discuss some experimental results comparing the two approaches for a number of well-known problem types. We also report some observations of student performance with these two different approaches.
Optimization using spreadsheets on a microcomputer
Annals of Operations Research, 1985
This paper discusses the advantages of using spreadsheets for problem specification and report generation in optimization projects. It summarizes some of the mathematical programming software which is compatible with popular spreadsheets. A small production planning problem is used to illustrate the steps in input and processing of the results. Two programs are compared in detail.
Creative OR modeling using Excel to solve combinatorial programming problems
Spreadsheets have grown up and became very powerful and easy to use tools in applying analytical techniques for solving business problems. Operations managers, production managers, planners and schedulers can work with them in developing solid and practical Do-It-Yourself Decision Support Systems. Small and Medium size organizations, can apply OR methodologies without the presence of specialized software and trained personnel, which in many cases cannot afford anyway. This paper examines an efficient approach in solving combinatorial programming problems with the use of spreadsheets. A practical application, which demonstrates the approach, concerns the development of a spreadsheet-based DSS for the Multi Item Procurement Problem with Fixed Vendor Cost. The DSS has been build using exclusively standard spreadsheet feature and can solve real problems of substantial size. The benefits and limitations of the approach are also discussed.
Optimization is a process that aims to find the best, most favorable, or most optimized solution for a given problem. This process includes the use of mathematical techniques, algorithms and specialized methods to identify the values of the variables that minimize, or maximize a certain function, which is called an objective function. The use of these methods helps to find valid and efficient solutions for optimization problems, bringing significant benefits in many areas of life and industry. The main goal of optimization is to identify the best or most favorable solution in the context of a given problem. Optimization problems are present in many fields of life and sciences such as: engineering and design, transportation and logistics, artificial intelligence and machine learning, energy and natural resources, network management and telecommunications, sciences and environment, robotics and automation, economics , informatics, biology, statistics, finance, social sciences, genetic algorithms, metaheuristics, etc. To solve optimization problems, specialized optimization algorithms and methods are used, including browsing algorithms, linear programming, genetic algorithms, clustering methods and many others. etc. These algorithms help in finding valid and efficient solutions for various optimization problems. In this paper, we will deal with some different problems from the real daily life of society. In solving such practical problems, the most important problem is often converting them into mathematical optimization problems by constructing a function that must be maximized or minimized.
A Real Life Application of Linear Programming
2019
Linear programming is heavily used in microeconomics and company management, such as<br> planning, production, transportation, technology and other issues, either to maximize the income<br> or minimize the costs of a production scheme. In the real world the problem is to find the<br> maximum profit for a certain production. In "real life", linear programming is part of a very<br> important area of mathematics called "optimization techniques". In this paper, it is to be<br> investigated two different solving graphical methods for some real life problems. Then we may<br> introduce a new program for linear programming which is my own invention software. This<br> system is computerized system using Microsoft Visual Basic Programming Software. This<br> software may be helpful to solve the linear programming problems to get quickly and easily<br> optimal solutions for any user.<br>
An Approach to Aid Decision-Making by Solving Complex Optimization Problems Using SQL Queries
Applied Sciences
In combinatorial optimization, the more complex a problem is, the more challenging it becomes, usually causing most research to focus on creating solvers for larger cases. However, real-life situations also contain small-sized instances that deserve a researcher’s attention. For example, within a web development context, a developer might face small combinatorial optimization cases that fall in the following situations to solve them: (1) the development of an ad hoc specialized strategy is not justified; (2) the developer could lack the time, or skills, to create the solution; (3) the efficiency of naive brute force strategies might be compromised due to the programming paradigm use. Similar situations in this context, combined with a recent increasing interest in optimization information from databases, open a research area to develop easy-to-implement strategies that compete with those naive approaches and do not require specialized knowledge. Therefore, this work revises Structur...
Métodos quantitativos - programação linear com o apoio da planilha Excel
Revista Ciencia E Tecnologia, 2010
Accounting is an important instrument of information for company managers decision making. The information should agree with the users decision pattern and needs to be available in a quicker and more accurate way. In order to meet the linear programming, its application in optimization troubleshooting and what it actually represents, this work was structured, in the beginning, with a review of concepts in, cost problems after that, an approach about the bases of linear programming and its use. The last part shows a cost problem in which linear programming was used in order to get an optimum combination of resources, focused on the best potential result. The method of linear programming was applied through the troubleshooting tool from the excel spreadsheet, spread over the companies, but with little usage for this purpose. Owning this solution, the information that can be gotten from the analysis pattern, from the solution and its elements is shown: function-object, coefficients, variables and restrictions.
Modeling optimization problems in the unstructured world of spreadsheets
Omega, 1997
Electronic spreadsheets are the most common software tool managers use to analyze data and model quantitative problems. Increasingly, these software packages are being used in introductory OR/MS courses to introduce students to a variety of quantitative modeling tools. Because spreadsheets are inherently free-form, they impose no particular guidelines or structure on the way problems may be modeled. Thus, academics and practitioners accustomed to solving problems using very structured, dedicated OR[MS software packages are facing the challenge of dealing with these problems in the unstructured spreadsheet environment where there is often a variety of ways to implement and solve the same problem. This challenge is particularly acute in the case of optimization problems. Some are responding to this challenge by devising rules for implementing models that impose an artificial structure on spreadsheets, sometimes resembling the operation of dedicated OR[MS optimization packages. This paper offers a critique of this approach and provides some guidelines we believe to be more helpful in creating effective spreadsheet models for optimization problems.