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Journal articles on the topic 'Excel Solver'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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1

Greco, Marco Antonio. "SOLVER como suplemento do Excel." Revista Dissertar 1, no. 26 e 27 (2017): 33–42. http://dx.doi.org/10.24119/16760867ed11321.

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A ferramenta SOLVER é extremamente útil para todos os profissionais que necessitam resolver problemas de recursos limitados e usá-los da maneira mais eficiente possível; normalmente procurando Maximização de Lucros e Minimização de Custos. A otimização matemática conduz a determinação de objetivos, considerando-se as restrições para todas as variáveis de decisão. Com isso, a cessão do SOLVER pela Frontline Systems à Microsoft, faz deste aplicativo um excelente solucionador de problemas.
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2

Pachamanova, Dessislava. "Introducing Integer Modeling with Excel Solver." INFORMS Transactions on Education 7, no. 1 (2006): 88–98. http://dx.doi.org/10.1287/ited.7.1.88.

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3

Howard, Enda, and John Cassidy. "Analysis with Microelectrodes Using Microsoft Excel Solver." Journal of Chemical Education 77, no. 3 (2000): 409. http://dx.doi.org/10.1021/ed077p409.

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4

Dasgupta, Purnendu K. "Chromatographic peak resolution using Microsoft Excel Solver." Journal of Chromatography A 1213, no. 1 (2008): 50–55. http://dx.doi.org/10.1016/j.chroma.2008.08.108.

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5

Duan, C. J., Jiayu Hu, and Stephen C. Garrott. "Using Excel Solver to solve braydon farms' truck routing problem: A case study." South Asian Journal of Management Sciences 10, no. 1 (2016): 38–47. http://dx.doi.org/10.21621/sajms.2016101.04.

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6

Walsh, S. "Non-linear curve fitting using microsoft excel solver." Talanta 42, no. 4 (1995): 561–72. http://dx.doi.org/10.1016/0039-9140(95)01446-i.

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7

Fylstra, Daniel, Leon Lasdon, John Watson, and Allan Waren. "Design and Use of the Microsoft Excel Solver." Interfaces 28, no. 5 (1998): 29–55. http://dx.doi.org/10.1287/inte.28.5.29.

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8

Pinder, Jonathan P. "An Excel Solver Exercise to Introduce Nonlinear Regression." Decision Sciences Journal of Innovative Education 11, no. 3 (2013): 263–78. http://dx.doi.org/10.1111/dsji.12009.

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9

Prather, Larry, Han-Sheng Chen, and Ying-Chou Lin. "CREATING OPTIMAL MUTUAL FUND PORTFOLIOS USING EXCEL SOLVER." Journal of International Finance and Economics 15, no. 3 (2015): 57–64. http://dx.doi.org/10.18374/jife-15-3.7.

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10

Li, Kunpeng, Bin Shao, and Pamela Zelbst. "Project Crashing Using Excel Solver: A Simple AON Network Approach." International Journal of Management & Information Systems (IJMIS) 16, no. 2 (2012): 177. http://dx.doi.org/10.19030/ijmis.v16i2.6917.

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This paper introduces a new and efficient AON-Solver approach to solve project crashing problems. The new approach uses conventional critical path concepts to determine the project completion time and to define network structures in Excel. It is simple and straightforward, with less decision variables and constraints than the existing AOA-Solver approach, which relies on tracking event times of the nodes in the AOA network. Also, there is no requirement to deal with dummy variables, which are often problematic, but frequently required in many AOA networks. In addition, most of the students and business practitioners are more familiar and comfortable with AON network. It would be more appealing and accessible for them to learn and to implement a computerized approach to solve project crashing problems, and to better understand the cost-time tradeoffs in project crashing.
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11

Harris, Daniel C. "Nonlinear Least-Squares Curve Fitting with Microsoft Excel Solver." Journal of Chemical Education 75, no. 1 (1998): 119. http://dx.doi.org/10.1021/ed075p119.

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12

Denton, Philip. "Analysis of First-Order Kinetics Using Microsoft Excel Solver." Journal of Chemical Education 77, no. 11 (2000): 1524. http://dx.doi.org/10.1021/ed077p1524.

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13

Thiruchelvam, Loshini, and Sabri Ahmad. "Excel solver as the software to construct MUSA model." Global Journal of Mathematical Analysis 2, no. 4 (2014): 304. http://dx.doi.org/10.14419/gjma.v2i4.3419.

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14

Delgado-Aguilar, Miriam, Lucia Valverde-Som, and Luis Cuadros-Rodríguez. "Solver, an Excel application to solve the difficulty in applying different univariate linear regression methods." Chemometrics and Intelligent Laboratory Systems 178 (July 2018): 39–46. http://dx.doi.org/10.1016/j.chemolab.2018.04.018.

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15

YOSHIMURA, Norio, Masanori OKAZAKI, and Naoya NAKAGAWA. "Determination of Dissociation Constants and Concentration by Microsoft Excel Solver." Journal of Chemical Software 7, no. 4 (2001): 191–95. http://dx.doi.org/10.2477/jchemsoft.7.191.

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16

Lázaro, Mario, and José Luis Pérez Aparicio. "Modelización no lineal de secciones aeronaúticas con EXCEL-SOLVER©." Modelling in Science Education and Learning 6 (June 2, 2013): 123. http://dx.doi.org/10.4995/msel.2013.1845.

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17

Luo, Zhi Yang, Hong Xia Zhao, Xin Yuan, and Yuan Li. "Optimization Problem of Excel Programming Calculation for Mayonnaise Prescription." Advanced Materials Research 304 (July 2011): 304–9. http://dx.doi.org/10.4028/www.scientific.net/amr.304.304.

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The method combining linear programming theory and computer technology, affords the possibility for the Optimization problem of food prescription in engineering design. Excel's Solver function may be used to solve problems with up to 200 decision variables, enough to calculate the most problems about food prescription. Combined design requirement of mayonnaise prescription, the decision variables, constraint condition and mathematics model of cost are established. The optimization prescription of minimum cost is gotten through the function of Excel programming calculation. It may provide valuable reference for the application of computer in prescription design.
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18

Filho, Oscar S. Silva, Wagner Cezarino, and João Ratto. "Aggregate Production Planning: Modeling and Solution via Excel Spreadsheet and Solver." IFAC Proceedings Volumes 43, no. 17 (2010): 89–94. http://dx.doi.org/10.3182/20100908-3-pt-3007.00020.

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19

Iannone, Mark. "Using Excel Solver: An Addendum to the HCl Infrared Spectrum Experiment." Journal of Chemical Education 75, no. 9 (1998): 1188. http://dx.doi.org/10.1021/ed075p1188.

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20

ARIF, Chusnul, Budi Indra SETIAWAN, Hanhan Ahmad SOFIYUDDIN, Lolly Martina MARTIEF, Masaru MIZOGUCHI, and Ryoichi DOI. "Estimating Crop Coefficient in Intermittent Irrigation Paddy Fields Using Excel Solver." Rice Science 19, no. 2 (2012): 143–52. http://dx.doi.org/10.1016/s1672-6308(12)60033-x.

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21

Chen, Wen, Lin Guo Chen, Jin Lu, Zhuan Liu, Zhong-hai Wan, and Wen Cai. "Application of EXCEL Solver Function in Solving Steam Turbine Thermal Equations." IOP Conference Series: Earth and Environmental Science 295 (July 25, 2019): 042093. http://dx.doi.org/10.1088/1755-1315/295/4/042093.

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22

Kang, Ling, and Liwei Zhou. "Parameter estimation of variable-parameter nonlinear Muskingum model using excel solver." IOP Conference Series: Earth and Environmental Science 121 (February 2018): 052047. http://dx.doi.org/10.1088/1755-1315/121/5/052047.

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23

Carrilho Fernades Barella, Rosilene, Celso Correia de Souza, José Francisco Dos Reis Neto, Sidney Maldonado, and Wesley Osvaldo Pradella Rodrigues. "Otimização da Produção de Queijos Usando a Ferramenta Solver do Excel." UNICIÊNCIAS 24, no. 2 (2021): 124–29. http://dx.doi.org/10.17921/1415-5141.2020v24n2p124-129.

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Nos últimos anos, o processo de gestão das empresas tem sido modificado na tentativa de responder mais rapidamente às mudanças ocorridas nas relações empresariais e nos cenários de negócios. A dinâmica é bastante complexa, e requer que os gestores controlem e planejem suas ações na busca pelo sucesso e manutenção de suas empresas no mercado. Este artigo técnico emprega a técnica de Programação Linear (PL) na otimização da produção de queijos em uma empresa de laticínios, em Jaraguari (MS), empregando a ferramenta Solver - Excel para a otimização do lucro. Os dados secundários foram obtidos junto a empresa objeto da pesquisa, a qual apresentava dificuldades na melhoria da sua produção de queijos. Os dados coletados foram organizados para a entrada do Solver, insumos e custos, considerando as restrições produtivas da empresa, para quatro tipos de queijos. Como resultado se demonstrou que o uso da PL na produção de queijos promoveu uma ampliação do lucro à empresa de 30,98%. Com a realização da análise de sensibilidade, sendo recomendada a fabricação de apenas dois tipos de queijos, considerando os seus custos e preços de venda ao mercado. Este procedimento de tomada de decisão pode ser aplicado em outras atividades de otimização em ambientes da agroindústria e produção sustentável.
 
 Palavras-chave: Agronegócio. Derivados do Leite. Pesquisa Operacional. Queijo Coalho.
 
 Abstract
 In recent years, the companies management process has been modified in an attempt to respond more quickly to changes in business relationships and business scenarios. The dynamics are quite complex and require managers to control and plan their actions in the search for the success and maintenance of their companies in the market. This technical article uses the Linear Programming (PL) technique to optimize the production of various types of cheese in a dairy company in Jaraguari (MS), using the Solver - Excel tool to optimize profit. Secondary data were obtained from the research company, which had difficulties in improving its cheese production. The data collected were organized for the entry of the Solver, inputs and costs, considering the company's production restrictions, for four types of cheese. As a result, it is shown that the use of PL in the cheese production promoted an increase in profit of 30.98% to the company. With the performance of the sensitivity analysis, it was recommended to manufacture only two types of cheese, considering their costs and selling prices to the market. This decision-making procedure can be applied to other optimization activities in agro-industrial and sustainable production environments.
 
 Keywords: Agribusiness. Milk Derivatives. Operational Research. Rennet Cheese.
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24

Ghaddar, Chahid. "Rapid Solution of Optimal Control Problems by a Functional Spreadsheet Paradigm: A Practical Method for the Non-Programmer." Mathematical and Computational Applications 23, no. 4 (2018): 54. http://dx.doi.org/10.3390/mca23040054.

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We devise a practical and systematic spreadsheet solution paradigm for general optimal control problems. The paradigm is based on an adaptation of a partial-parametrization direct solution method which preserves the original mathematical optimization statement, but transforms it into a simplified nonlinear programming problem (NLP) suitable for Excel NLP solver. A rapid solution strategy is implemented by a tiered arrangement of pure elementary calculus functions in conjunction with Excel NLP solver. With the aid of the calculus functions, a cost index and constraints are represented by equivalent formulas that fully encapsulate an underlining parametrized dynamical system. Excel NLP solver is then employed to minimize (or maximize) the cost index formula, by varying decision parameters, subject to the constraints formulas. The paradigm is demonstrated for several fixed and free-time nonlinear optimal control problems involving integral and implicit dynamic constraints with direct comparison to published results obtained by fundamentally different methods. Practically, applying the paradigm involves no more than defining a few formulas using basic Excel spreadsheet skills.
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25

Sil, Briti Sundar, Angana Borah, Shubrajyoti Deb, and Biplab Das. "DEVELOPMENT OF RIVER FLOOD ROUTING MODEL USING NON-LINEAR MUSKINGUM EQUATION AND EXCEL TOOL 'GANetXL'." Journal of Urban and Environmental Engineering 10, no. 2 (2017): 214–20. http://dx.doi.org/10.4090/juee.2016.v10n2.214-220.

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Flood routing is of utmost importance to water resources engineers and hydrologist. Muskingum model is one of the popular methods for river flood routing which often require a huge computational work. To solve the routing parameters, most of the established methods require knowledge about different computer programmes and sophisticated models. So, it is beneficial to have a tool which is comfortable to users having more knowledge about everyday decision making problems rather than the development of computational models as the programmes. The use of micro-soft excel and its relevant tool like solver by the practicing engineers for normal modeling tasks has become common over the last few decades. In excel environment, tools are based on graphical user interface which are very comfortable for the users for handling database, modeling, data analysis and programming. GANetXL is an add-in for Microsoft Excel, a leading commercial spreadsheet application for Windows and MAC operating systems. GANetXL is a program that uses a Genetic Algorithm to solve a wide range of single and multi-objective problems. In this study, non-linear Muskingum routing parameters are solved using GANetXL. Statistical Model performances are compared with the earlier results and found satisfactory.
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26

Sil, Briti Sundar, Angana Borah, Shubrajyoti Deb, and Biplab Das. "DEVELOPMENT OF RIVER FLOOD ROUTING MODEL USING NON-LINEAR MUSKINGUM EQUATION AND EXCEL TOOL 'GANetXL'." Journal of Urban and Environmental Engineering 10, no. 2 (2017): 214–20. http://dx.doi.org/10.4090/juee.2016.v10n2.214220.

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Flood routing is of utmost importance to water resources engineers and hydrologist. Muskingum model is one of the popular methods for river flood routing which often require a huge computational work. To solve the routing parameters, most of the established methods require knowledge about different computer programmes and sophisticated models. So, it is beneficial to have a tool which is comfortable to users having more knowledge about everyday decision making problems rather than the development of computational models as the programmes. The use of micro-soft excel and its relevant tool like solver by the practicing engineers for normal modeling tasks has become common over the last few decades. In excel environment, tools are based on graphical user interface which are very comfortable for the users for handling database, modeling, data analysis and programming. GANetXL is an add-in for Microsoft Excel, a leading commercial spreadsheet application for Windows and MAC operating systems. GANetXL is a program that uses a Genetic Algorithm to solve a wide range of single and multi-objective problems. In this study, non-linear Muskingum routing parameters are solved using GANetXL. Statistical Model performances are compared with the earlier results and found satisfactory.
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27

Fan, Meng Hua. "Using Excel to Evaluate Shear Strength Parameters of Soil." Advanced Materials Research 709 (June 2013): 579–82. http://dx.doi.org/10.4028/www.scientific.net/amr.709.579.

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It is difficult to determine the Mohr-Coulomb failure envelope visually, and it is strongly influenced by abnormal test data evaluating the shear strength parameters of soil via trend line and unable to adjust the scope of permissible error. So it is recommended to evaluate shear strength parameters of soil using Solver of Excel for direct shear tests and triaxial tests and you can control the allowable deviation. The mathematics model of nonlinear programming was established to evaluate shear strength parameters of soil from the results of direct shear test and triaxial shear test. The related Excel worksheet was created and the optimum results of the objective function were obtained by setting the Solver parameters dialog box accurately. The method is simple, inexpensive and rapid.
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28

Al-Ashhab, M. S., and Hassan Fadag. "MULTI-PRODUCT MASTER PRODUCTION SCHEDULING OPTIMIZATION MODELLING USING MIXED INTEGER LINEAR PROGRAMMING AND GENETIC ALGORITHMS." International Journal of Research -GRANTHAALAYAH 6, no. 5 (2018): 78–92. http://dx.doi.org/10.29121/granthaalayah.v6.i5.2018.1429.

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The objective of this research is to develop a Master Production Scheduling (MPS) model to maximize the total profit using Mixed Integer Linear Programming (MILP). The model is solved using both MILP with the Xpress software and genetic algorithms with the Evolver solver. The model is built for Evolver in MS Excel. Results of both solving tools are compared to analyze the performance of each of them. The accuracy and capability of the model to solve the MPS problems have been verified through the discussion of its results logicality for different cases of different patterns.
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Adekunbi, E. A., J. O. Babajide, H. O. Oloyede, J. S. Amoko, O. A. Obijole, and I. A. Oke. "Evaluation of Microsoft excel solver as a tool for adsorption kinetics determination." Ife Journal of Science 21, no. 3 (2020): 169. http://dx.doi.org/10.4314/ijs.v21i3.14.

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30

Patterson, Mike C., and Bob Harmel. "An Algorithm for Using Excel Solver© for the Traveling Salesman Problem." Journal of Education for Business 78, no. 6 (2003): 341–46. http://dx.doi.org/10.1080/08832320309598624.

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31

Barati, Reza. "Application of excel solver for parameter estimation of the nonlinear Muskingum models." KSCE Journal of Civil Engineering 17, no. 5 (2013): 1139–48. http://dx.doi.org/10.1007/s12205-013-0037-2.

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32

Alzahrani, Jaber. "JOB-SHOP SCHEDULING OPTIMIZATION WITH STOCHASTIC PROCESSING TIMES." International Journal of Engineering Technologies and Management Research 6, no. 1 (2020): 73–83. http://dx.doi.org/10.29121/ijetmr.v6.i1.2019.347.

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In this study, a job shop scheduling optimization model under risk has been developed to minimize the make span. This model has been built using Microsoft Excel spreadsheets and solved using @Risk solver. A set of experiments have been also conducted to examine the accuracy of the model and its effectiveness has been proven.
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33

Amlie, Thomas T. "Constrained Optimization Problems In Cost And Managerial Accounting Spreadsheet Tools." American Journal of Business Education (AJBE) 2, no. 6 (2009): 11–22. http://dx.doi.org/10.19030/ajbe.v2i6.4083.

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A common problem addressed in Managerial and Cost Accounting classes is that of selecting an optimal production mix given scarce resources. That is, if a firm produces a number of different products, and is faced with scarce resources (e.g., limitations on labor, materials, or machine time), what combination of products yields the greatest profit to the firm? Solver, an optimization package included within Microsoft Excel (or Optimizer in Quattro Pro), is an ideal vehicle by which to analyze these problems. In most cost or managerial accounting texts, students are asked to address this type of question when there is only one scarce resource (e.g., Material X); such problems can be readily solved by hand. In the case of two or more scarce resources, students are usually referred to their management science classes and Linear Programming packages such as LINDO for further enlightenment, with the comment that such matters are beyond the scope of an accounting text. The purpose of this paper is to illustrate how the Solver package in Microsoft Excel can be easily used to solve optimization problems in management accounting. Although not as powerful or flexible as stand-alone packages such as LINDO, Solvers presence within a universally available spreadsheet package makes it an extraordinarily powerful teaching tool. Instead of parameters being entered into the optimization problem as constants, they can be expressed as functions of other spreadsheet cells. This interactive structure allows an instructor (or student) to create complex production environments where it can be illustrated how minor changes in one aspect of the production environment can flow through and have a profound impact on optimal production schedules.
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Fogués Zornoza, P., and E. Jiménez Fernández. "Modelo de selección de cartera con Solver." Modelling in Science Education and Learning 5 (April 5, 2012): 57. http://dx.doi.org/10.4995/msel.2012.2133.

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<p>En este trabajo se presenta un ejemplo de cómo contextualizar la optimización lineal en cursos avanzados de grados de económicas o administración y dirección de empresas. Mostramos técnicas que permiten al alumno profundizar e investigar con problemas reales que posteriormente modelizan utilizando la plataforma Excel. El modelo que aquí se muestra, es el trabajo desarrollado por un alumno de estos cursos y que consiste en minimizar las desviaciones absolutas respecto a la rentabilidad media esperada de una cartera de valores bursátiles, utilizando la herramienta Solver que nos proporciona la hoja de cálculo.</p>
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SWITALA-ZELIAZKOW, MARIA. "The use of Solver in Excel to solve copolymer composition equation in terminal and penultimate models of copolymerization." Polimery 47, no. 10 (2002): 700–705. http://dx.doi.org/10.14314/polimery.2002.700.

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36

Hisada, Kimikazu. "Thermodynamic calculations using the Excel Solver tool: Application to barycentric formula-based calculations." Journal of the Geological Society of Japan 125, no. 9 (2019): 635–54. http://dx.doi.org/10.5575/geosoc.2019.0018.

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37

YOSHIMURA, Norio, Masanori OKAZAKI, and Naoya NAKAGAWA. "Application of Linear Programming Method by Microsoft Excel Solver to Titration Curve Analysis." Journal of Computer Chemistry, Japan 2, no. 2 (2003): 49–56. http://dx.doi.org/10.2477/jccj.2.49.

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38

Evans, James R. "Teaching Note—Some Practical Issues with Excel Solver: Lessons for Students and Instructors." INFORMS Transactions on Education 8, no. 2 (2008): 89–95. http://dx.doi.org/10.1287/ited.1070.0006.

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39

Oke, I. A., A. Ismail, S. Lukman, S. O. Ojo, O. O. Adeosun, and M. O. Nwude. "Economics and Statistical Evaluations of Using Microsoft Excel Solver in Pipe Network Analysis." Journal of Pipeline Systems Engineering and Practice 7, no. 4 (2016): 06016002. http://dx.doi.org/10.1061/(asce)ps.1949-1204.0000240.

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40

Sujono, S., A. K. Dewi, and T. S. Soegiarto. "Evaluating and optimizing performance of shell and tube heat exchanger using excel-solver." IOP Conference Series: Materials Science and Engineering 830 (May 19, 2020): 042029. http://dx.doi.org/10.1088/1757-899x/830/4/042029.

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41

Sanchez, Luis Carlos, and Jorge Herrera. "Solution to the multiple products transportation problem: linear programming optimization with Excel Solver." IEEE Latin America Transactions 14, no. 2 (2016): 1018–23. http://dx.doi.org/10.1109/tla.2016.7437253.

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42

Němec, Petr, Petr Stodola, Miroslav Pecina, Jiří Neubauer, and Martin Blaha. "Optimization of the Weighted Multi-Facility Location Problem Using MS Excel." Algorithms 14, no. 7 (2021): 191. http://dx.doi.org/10.3390/a14070191.

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This article presents the possibilities in solving the Weighted Multi-Facility Location Problem and its related optimization tasks using a widely available office software—MS Excel with the Solver add-in. To verify the proposed technique, a set of benchmark instances with various point topologies (regular, combination of regular and random, and random) was designed. The optimization results are compared with results achieved by a metaheuristic algorithm based on simulated annealing principles. The influence of the hardware configuration on the performance achieved by MS Excel Solver is also examined and discussed from both the execution time and accuracy perspectives. The experiments showed that this widely available office software is practical for solving even relatively complex optimization tasks (Weighted Multi-Facility Location Problem with 100 points and 20 centers, which consists of 40 continuous optimization variables in two-dimensional space) with sufficient quality for many real-world applications. The method used is described in detail and step-by-step using an example.
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43

Chang, Kian Boon, Sai Hin Lai, and Othman Faridah. "RainIDF: automated derivation of rainfall intensity–duration–frequency relationship from annual maxima and partial duration series." Journal of Hydroinformatics 15, no. 4 (2013): 1224–33. http://dx.doi.org/10.2166/hydro.2013.192.

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RainIDF, a software tool for derivation of rainfall intensity–duration–frequency (IDF) relationship is developed as an Excel add-in by using Visual Basics for Applications (VBA). The tool is integrated with two of the most widely used statistical distributions for determination of IDF relationship: the generalized extreme value (GEV) distribution for annual maxima series, and the generalized Pareto (GPA) distribution for partial duration series (PDS). It provides automated distribution fitting for rainfall data in the form of annual maxima or PDS for multiple intervals, solving and plotting of rainfall IDF curves. RainIDF uses the Solver add-in function in Excel to solve the coefficients of the empirical IDF formula in one step. The methodology built into RainIDF is discussed and rainfall IDF relationships for several stations in Peninsular Malaysia are derived and compared. RainIDF is available for download on GitHub (http://github.com/kbchang/rainidf) as an Excel add-in.
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Murthy, Sree Rama. "Excel Based Financial Modeling for Making Portfolio Management Decisions." Information Management and Business Review 11, no. 2(I) (2019): 35–41. http://dx.doi.org/10.22610/imbr.v11i2(i).2881.

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The Excel based financial model proposed in this paper provides a very simple but powerful method for portfolio selection. Apart from a simple and powerful tool for making portfolio management decisions, the paper also proposes an easy to use technique for calculating portfolio standard deviation without using correlation coefficients. The model uses “Excel Solver Add-In” to create an optimum portfolio by maximizing the Sharpe ratio. Benefits of Sharpe style optimization are demonstrated using data on monthly returns from 1999 to 2010 covering 30 stocks.
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Amindoust, Atefeh, Ahmed Shamsuddin, and Ali Saghafinia. "Learning Improvement of DEA Technique in Decision Making for Manufacturing Applications Using DEA Excel-Solver." Advanced Materials Research 903 (February 2014): 425–30. http://dx.doi.org/10.4028/www.scientific.net/amr.903.425.

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DEA (Data Envelopment Analysis) is the optimization method of mathematical programming to measure the relative efficiencies of decision making units (DMUs). Due to its wide applicability, the DEA has been studied extensively for the last 30 years to solve decision making problems. Since, there are a lot of selection decisions in manufacturing, DEA as an appropriate tool to be necessary-especially for engineers-to improve learning for decision making. In this paper, the DEA method is applied in decision making process through DEA Excel-Solver software and the required processes are explained step by step to help academics and practitioners to get appropriate results in making decision.
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Tetik Küçükelçi, Didem, and Atıf Evren. "A Comparison Between Mıcrosoft Excel Solver and Ncss, Spss Routines for Nonlinear Regression Models." Journal of Engineering Technology and Applied Sciences 2, no. 2 (2017): 91–100. http://dx.doi.org/10.30931/jetas.336853.

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Dias, Albino A., Paula A. Pinto, Irene Fraga, and Rui M. F. Bezerra. "Diagnosis of Enzyme Inhibition Using Excel Solver: A Combined Dry and Wet Laboratory Exercise." Journal of Chemical Education 91, no. 7 (2014): 1017–21. http://dx.doi.org/10.1021/ed3006677.

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Hozak, Kurt. "Teaching decomposition forecasting models using an Excel-based spreadsheet application with Solver and randomisation." International Journal of Innovation and Learning 26, no. 4 (2019): 407. http://dx.doi.org/10.1504/ijil.2019.10024009.

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Hozak, Kurt. "Teaching decomposition forecasting models using an Excel-based spreadsheet application with Solver and randomisation." International Journal of Innovation and Learning 26, no. 4 (2019): 407. http://dx.doi.org/10.1504/ijil.2019.102905.

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Briones, Laura, Victoria Morales, José Iglesias, Gabriel Morales, and Jose M. Escola. "Application of the microsoft excel solver tool in the optimization of distillation sequences problems." Computer Applications in Engineering Education 28, no. 2 (2019): 304–13. http://dx.doi.org/10.1002/cae.22193.

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