scholarly journals Optimalisasi Pendapatan Integrasi Sawit dengan Sapi Menggunakan Metode Monte Carlo

KOMTEKINFO ◽  
2021 ◽  
pp. 225-231
Author(s):  
Hermanto
Keyword(s):  
Monte Carlo ◽  
Microsoft Excel

Hidup manusia sangat dipengaruhi oleh perkembangan lmu pengetahuan dan teknologi inforrmasi dalam penunjang kehidupan, salah satu nya dalam sektor pertanian. Teknologi informasi dalam sektor pertanian yang tepat waktu dan relevan memberikan informasi yang tepat guna kepada rumah tangga usaha pertanian untuk pengambilan keputusan dalam berusaha tani, sehingga efektif dalam meningkatkan produktivitas,dan pendapatan. Di Kabupaten Sijunjung petani yang merapkan sistem integrasi kelapa sawit dengan sapi hanya beberapa petani yang menerapkan hal itu, di karenakan keterbatasan informasi dan menyebabkan pendapatan dari petani yang menerapkan metode ini tidak menentu. Maka dari itu teknologi di harapkan dapat membantu mengatasi permasalahan tersebut. Metode yang di gunakan dalam penelitian ini adalah metode Monte Carlo dengan mengunakan data pendapatan petani kelapa sawit yang mengunakan metode integrasi kelapa sawit dengan sapi yang berada di kabupaten sijunjung tepatnya di kecamatan Kamang Baru, data di peroleh dengan cara wawancara secara langsung kepada petani di mana mempunyai lahan 1,5 hektar kebun kelapa sawit dengan 8 ekor sapi, dan di peroleh lah data pendapatan petani dari tahun 2018, 2019 dan 2020 dimulai dari bulan januari sampai bulan desember. Variabel yang digunakan dalam penelitian ini adalah jumlah pendapatan petani perbulannya. Data jumlah pendapatan petani tersebut akan di olah menggunakan metode Monte Carlo dibantu dengan Microsoft Excel untuk pencarian manualnya. Data jumlah pendapatan petani tahun 2018 digunakan sebagai data uji coba untuk memprediksi jumlah pendapatan petani pada tahun 2019, data tahun 2019 di gunakan untuk memprediksi jumlah pendapatan petani tahun 2020, dan data tahun 2020 untuk memprediksi pendapatan tahun 2021

scholarly journals PSM: A New Approach to Probabilistic Scheduling

2021 ◽  
Author(s):  
Benyamin Tedjakusuma
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Critical Path ◽  
Project Schedule ◽  
Microsoft Excel ◽  
New Approach ◽  
Probabilistic Scheduling ◽  
Scheduling Method ◽  
Duration Estimation ◽  
Ongoing Project

A new scheduling method, where probability values can be assigned to activity durations, is proposed in this thesis. Probabilistic Scheduling Method (PSM) accepts activity durations tagged with probability or confidence intervals. Tests were carried out using examples of 3,7, and 9 activities to evaluate PSM's practical capability. The comparisons of PSM to Critical Path Method (CPM), Performance Evaluation and Review Technique (PERT), and Monte Carlo application to PERT (MC PERT) conclude that PSM results in similar most probable duration estimation. Further tests were implemented to evaluate PSM's capability to project schedule revision on an ongoing project. A microsoft Excel application was used to organize tests data and calculations. PSM computations are more industry friendly. They allow for a range of duration associated with a range of probabilites. PSM provides flexibility and simplicity, and also dependency information that will benefit its user in decision making

ANÁLISE DE VIABILIDADE EM PROJETOS: COMPARAÇÃO ENTRE OS MÉTODOS DETERMINÍSTICOS E PROBABILÍSTICOS

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Luciano Maldonado Felipe ◽  
Edison Luiz Leismann
Keyword(s):  
Monte Carlo ◽  
Microsoft Excel ◽  
Software Risk

Organizações e investidores desejam maiores retornos sobre seu capital ou patrimônio, quando investem seus recursos em novos projetos ou negócios em substituição às aplicações mais conservadoras. As habilidades e conhecimentos necessários para análise de viabilidade econômica/financeira de projetos ou negócios, quando somados ao uso de técnicas adequadas, permitem ampliar as taxas de sucesso dos investimentos realizados. Assim, por meio de pesquisa exploratória, descritiva e com uso de aplicação por meio de um estudo de caso (simulação de análise de investimento), este artigo objetiva apresentar os métodos determinísticos e probabilísticos, suas aplicações, vantagens e desvantagens, como formas de cálculo e demonstração dos resultados projetados para os investimentos em análise, com simulação de cenários integrado ao método probabilístico, mediante adoção do método de simulação de Monte Carlo, com uso do software @Risk, um add-in para o Microsoft Excel. Explora ainda, a análise de opções reais e árvore de decisão, como técnicas para ampliar o detalhamento da análise de investimentos a serem realizados em projetos. Ao final, por meio dos resultados e simulações geradas em relação ao estudo de caso pelos métodos determinístico e probabilístico, propicia-se melhor compreensão do resultado final apresentado de cada método, permitindo as organizações e investidores aperfeiçoar suas análises.

scholarly journals Application of the Monte Carlo method in the calculation procedure of the internal combustion engine

2019 ◽  
Vol 178 (3) ◽  
pp. 24-26
Author(s):  
Lech SITNIK ◽  
Dominik ZADWORNY
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Internal Combustion Engine ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Calculation Procedure ◽  
High Complexity ◽  
Microsoft Excel ◽  
The Monte Carlo Method ◽  
Random Manner

The aim of the work is to check the possibility of applying the Monte Carlo method to the calculation procedure of internal combus-tion engines. This has been accomplished by modifying existing algorithms in such a way that the variables responsible for the main parameters of the engine are selected in a random manner using Solver written in Microsoft Excel. It turns out that this method can actually be implied, however, it has some limitations related to the high complexity of calculations.

scholarly journals PSM: A New Approach to Probabilistic Scheduling

2021 ◽  
Author(s):  
Benyamin Tedjakusuma
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Critical Path ◽  
Project Schedule ◽  
Microsoft Excel ◽  
New Approach ◽  
Probabilistic Scheduling ◽  
Scheduling Method ◽  
Duration Estimation ◽  
Ongoing Project

A new scheduling method, where probability values can be assigned to activity durations, is proposed in this thesis. Probabilistic Scheduling Method (PSM) accepts activity durations tagged with probability or confidence intervals. Tests were carried out using examples of 3,7, and 9 activities to evaluate PSM's practical capability. The comparisons of PSM to Critical Path Method (CPM), Performance Evaluation and Review Technique (PERT), and Monte Carlo application to PERT (MC PERT) conclude that PSM results in similar most probable duration estimation. Further tests were implemented to evaluate PSM's capability to project schedule revision on an ongoing project. A microsoft Excel application was used to organize tests data and calculations. PSM computations are more industry friendly. They allow for a range of duration associated with a range of probabilites. PSM provides flexibility and simplicity, and also dependency information that will benefit its user in decision making

Practical reliability analysis of slope stability by advanced Monte Carlo simulations in a spreadsheet

2011 ◽  
Vol 48 (1) ◽  
pp. 162-172 ◽  
Author(s):  
Yu Wang ◽  
Zijun Cao ◽  
Siu-Kui Au
Keyword(s):  
Monte Carlo ◽  
Slope Stability ◽  
Spatial Variability ◽  
Soil Properties ◽  
Reliability Analysis ◽  
Slip Surface ◽  
Probability Of Failure ◽  
Critical Slip Surface ◽  
Microsoft Excel ◽  
Mcs Method

This paper develops a Monte Carlo simulation (MCS)-based reliability analysis approach for slope stability problems and utilizes an advanced MCS method called “subset simulation” for improving efficiency and resolution of the MCS at relatively small probability levels. Reliability analysis is operationally decoupled from deterministic slope stability analysis and implemented using a commonly available spreadsheet software, Microsoft Excel. The reliability analysis spreadsheet package is validated through comparison with other reliability analysis methods and commercial software. The spreadsheet package is then used to explore the effect of spatial variability of the soil properties and critical slip surface. It is found that, when spatial variability of soil properties is ignored by assuming perfect correlation, the variance of the factor of safety (FS) is overestimated, which may result in either over (conservative) or under (unconservative) estimation of the probability of failure (Pf = P(FS < 1)). When the spatial variability of soil properties is considered, the critical slip surface varies spatially and such spatial variability should be properly accounted for. Otherwise, the probability of failure can be significantly underestimated and unconservative.

scholarly journals ANÁLISE DA RELAÇÃO ENTRE QUANTIDADES E CUSTOS TOTAIS DE PRODUÇÃO DE FRANGO NO ESTADO DO CEARÁ

ABCustos ◽  
2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Jucimar Casimiro de Andrade ◽  
Fernando Salvino Da Silva ◽  
Marcela Rebecca Pereira ◽  
Robson José Silva Santana
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Microsoft Excel

Diante do atual cenário econômico que ora vivenciam os mercados mundiais e considerando a necessidade empresarial de mensurar e controlar melhor seus custos produtivos, principalmente no segmento de agronegócio da carne de frango de corte, o presente artigo teve como propósito analisar como os custos de produção de frango no estado do Ceará relacionam-se com as quantidades produzidas em unidades/lotes de frango para abate. Para isso, utilizaram-se os dados mensais dos custos de produção de frango disponíveis no site da Central de Inteligência de Aves e Suínos (CIAS), referentes ao estado do Ceará, entre os meses de janeiro de 2010 a setembro de 2015. Para os cálculos, utilizou-se uma adaptação do modelo desenvolvido por Carmo, Lima, Oliveira e Oliveira (2011) e as planilhas do Microsoft Excel®, em conjunto com o pacote econométrico Gretl® (Gnu Regression, Econometrics and Time-series Library). Portanto, no que se refere ao modelo estimado, em que a variável y1 representa os custos totais de produção (fixos e variáveis) e x1 representa as quantidades produzidas, constatou-se que o modelo apresentou um erro de estimação em torno de -6,59% para os dados de 2014; e, em relação às simulações nas planilhas feitas por meio da Técnica de Monte Carlo, observou-se que o modelo é satisfatório e serve como apoio à tomada de decisão sobre as projeções de custos/lotes para uma estimativa de produção de frangos para abate entre 1.000 e 5.000 cabeças, desde que se assuma uma margem de erro de em torno de ± 1,11%.

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