ANALYSIS OF BEEF INVENTORY PLANNING WITH APPROACH MONTE CARLO METHOD IN CV. PUTRA SURYA

Research with the title Analysis of Planning Beef Inventory with Monte Carlo Method Approach in CV. Putra Surya was held for 3 months, from September to November 2019. The data used in this research is secondary data obtained from the owner of CV. Putra Surya. Based on the results of the analysis that hasbeen done with Monte Carlo Simulation, CV. Putra Surya can plan beef supply for 2020 of 35,388 kg of beef with an average demand in one week of 707.76 kg. For expected requests or expected values of 704.96. The average demand that has been processed by the Monte Carlo Simulation method of 707.76 kg per week is not too different from the expected demand of 704.96 kg per week. There is a difference of 2.8 kg between the simulation value and the expected value and this difference can be influenced by the number of tests or experiments conducted. Keywords:Inventory, Supplay,Demand,Monte Carlo

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Application of Quasi-Monte Carlo Method Based on Good Point Set in Tolerance Analysis

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4032909 ◽

2016 ◽

Vol 16 (2) ◽

Cited By ~ 3

Author(s):

Yanlong Cao ◽

Huiwen Yan ◽

Ting Liu ◽

Jiangxin Yang

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Monte Carlo Method ◽

Tolerance Analysis ◽

Simulation Method ◽

Analysis Tool ◽

Monte Carlo Simulation Method ◽

Worst Case ◽

Quasi Monte Carlo ◽

Good Point

Tolerance analysis is increasingly becoming an important tool for mechanical design, process planning, manufacturing, and inspection. It provides a quantitative analysis tool for evaluating the effects of manufacturing variations on performance and overall cost of the final assembly. It boosts concurrent engineering by bringing engineering design requirements and manufacturing capabilities together in a common model. It can be either worst-case or statistical. It may involve linear or nonlinear behavior. Monte Carlo simulation is the simplest and the most popular method for nonlinear statistical tolerance analysis. Monte Carlo simulation offers a powerful analytical method for predicting the effects of manufacturing variations on design performance and production cost. However, the main drawbacks of this method are that it is necessary to generate very large samples to assure calculation accuracy, and that the results of analysis contain errors of probability. In this paper, a quasi-Monte Carlo method based on good point (GP) set is proposed. The difference between the method proposed and Monte Carlo simulation lies in that the quasi-random numbers generated by Monte Carlo simulation method are replaced by ones generated by the method proposed in this paper. Compared with Monte Carlo simulation method, the proposed method provides analysis results with less calculation amount and higher precision.

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Analysis of Uncertainty in Standard Metal Tank Volume Verification with Monte Carlo Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.712-715.1974 ◽

2013 ◽

Vol 712-715 ◽

pp. 1974-1978

Author(s):

Jin Hai Li ◽

Ru Song Tong ◽

Suo Sheng Cao

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Monte Carlo Method ◽

Evaluation Method ◽

Simulation Method ◽

Comparison Method ◽

Monte Carlo Simulation Method ◽

Analog Simulation ◽

Volume Comparison ◽

Evaluation Result

The uncertainty in standard metal tank volume value verification is evaluated by using Monte Carlo Method. For second standard metal tank, verification is conducted with volume comparison method. A mathematical model of second standard metal tank volume measurement is established by using first standard metal tank to measure second standard metal tank. An analog simulation is conducted for verification value by using Monte Carlo simulation method, and hence the uncertainty in volume verification is obtained. Through comparison between evaluation result and traditional uncertainty propagation evaluation method, the result indicates that the difference cannot be ignored and the evaluation result by using Monte Carlo Simulation Method is more reliable.

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The Structure Optimization Design of Cutting Disc of Defibrator Based on Monte Carlo Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.472-475.2064 ◽

2012 ◽

Vol 472-475 ◽

pp. 2064-2067

Author(s):

Hong Bin Ma ◽

Yan Ma ◽

Guang Yu Tan ◽

Xiu Rong Guo

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Monte Carlo Method ◽

Optimization Design ◽

Structure Optimization ◽

Simulation Method ◽

Design Parameters ◽

Monte Carlo Simulation Method ◽

Performance Parameters ◽

Disc Size

This paper aims at the factors that the design needed to be considered. There are diameter and structural of defibrator millstone and the rotational speed and torque of millstone and so on. To optimize the cutting disc size and subarea size and the number of cutting disc fan block etc. which will influence the indicator of performance parameters of cutting disc and to arrive at a more reasonable design parameters via the Monte Carlo simulation method.

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Research on Scheduling Model of Maintenance Support Force Based on Monte Carlo Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.452-453.838 ◽

2012 ◽

Vol 452-453 ◽

pp. 838-841

Author(s):

Xiang Li ◽

Liu Zhang ◽

Feng Hua Lv

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Monte Carlo Method ◽

Mathematical Description ◽

Simulation Method ◽

Evaluation Index ◽

Monte Carlo Simulation Method ◽

Scheduling Model ◽

Support Force ◽

Force Unit

The priority evaluation index of maintenance support force is established,and the mathematical description is developed. The priority of each maintenance support force unit is calculated with Monte Carlo simulation method, and the scheduling model is built on the basis of priority. Practical example shows that the model is accurate and effective.

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Uncertainty Cost Functions in Climate-Dependent Controllable Loads in Commercial Environments

Energies ◽

10.3390/en14102885 ◽

2021 ◽

Vol 14 (10) ◽

pp. 2885

Author(s):

Daniel Losada ◽

Ameena Al-Sumaiti ◽

Sergio Rivera

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Simulation Method ◽

Electricity Sector ◽

Cost Functions ◽

Density Estimator ◽

Monte Carlo Simulation Method ◽

Commercial Building ◽

Complementary Method ◽

The Cost

This article presents the development, simulation and validation of the uncertainty cost functions for a commercial building with climate-dependent controllable loads, located in Florida, USA. For its development, statistical data on the energy consumption of the building in 2016 were used, along with the deployment of kernel density estimator to characterize its probabilistic behavior. For validation of the uncertainty cost functions, the Monte-Carlo simulation method was used to make comparisons between the analytical results and the results obtained by the method. The cost functions found differential errors of less than 1%, compared to the Monte-Carlo simulation method. With this, there is an analytical approach to the uncertainty costs of the building that can be used in the development of optimal energy dispatches, as well as a complementary method for the probabilistic characterization of the stochastic behavior of agents in the electricity sector.

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