scholarly journals Financial Assessment on Designing Inventory Policy by Considering Demand, Lead Time, and Defective Product Uncertainties: A Monte Carlo Simulation

2021 ◽  
Vol 3 ◽  
pp. 36-42
Author(s):  
Zakka Ugih Rizqi ◽  
Adinda Khairunisa ◽  
Aniya Maulani
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Tests ◽  
Monte Carlo Model ◽  
Net Income ◽  
Order Quantity ◽  
Inventory Policy ◽  
Financial Assessment ◽  
Financial Expenditure ◽  
Main Components

Inventory is one of the main components in supply chain. However, it is not easy to design inventory policy under uncertainties. The frequent occurrence of overstocks increases the company's financial expenditure. Otherwise, stockout decreases customer satisfaction and damage the company's image. This study aims to provide monte carlo model to design inventory policy with the aim of maximizing net income with a variety of uncertainties, one of the uncertainties is defective product because of the travel from suppliers. To handle the complexity and uncertainty of problem, a Monte Carlo simulation is used with spreadsheet-based representation. To test the reliability of the model, guitar company is used as relevant use case with uncertainty adhered ‘the greater number of order quantity, the greater likely the defective guitar will be’. The verification & validation process, experimental design, and alternative selection are also done with statistical tests. Based on the simulation result, it is known that changing the reorder point to 80 and the order quantity to 90 gives the best result which can increase net income by 0.44% compared to the initial net income. In addition, the number of stockouts has decreased.

Modeling Energy Deposition in Very Thin Layered Media by Monte Carlo Simulation

2007 ◽  
Author(s):  
Reginald Eze ◽  
Anisur Rahman ◽  
Sunil Kumar
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Thin Layer ◽  
Layer Thickness ◽  
Path Length ◽  
Energy Deposition ◽  
Radiation Transport ◽  
Monte Carlo Model ◽  
Layered Media ◽  
Thin Layers

A Monte Carlo model with special features for modeling of radiation transport through very thin layers has been presented. Over the decades traditional Monte Carlo model has been used to model highly scattering thin layers in skin and may inaccurately capture the effect of thin layers since their interfaces are not perfectly planar and thicknesses non-uniform. If the Monte Carlo model is implemented without special features then the results of the simulation would show no effect of the outer thin layer since the path length of most photons would be significantly larger than the layer thickness and the resulting predicted photon travel would simply not notice the presence of the layer. Examples of multi-layered media are considered where the effect of a very thin absorbing layers is systematically examined using both the traditional Monte Carlo and that with new features incorporated. The results have profound implications in the diagnostic and therapeutic applications of laser in biomedicine and surgery.

scholarly journals Ab initiokinetic Monte Carlo simulation of seeded emulsion polymerizations of styrene

2018 ◽  
Vol 20 (16) ◽  
pp. 10796-10805 ◽  
Author(s):  
Marco Drache ◽  
Katrin Brandl ◽  
Rebecca Reinhardt ◽  
Sabine Beuermann
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Kinetic Monte Carlo ◽  
Monte Carlo Model ◽  
Radical Transfer ◽  
Emulsion Polymerizations

A kinetic Monte Carlo model for emulsion polymerizations based on elemental reactions and radical transfer into particles is introduced.

scholarly journals Monte Carlo Simulation of Pathogen Behavior during the Sprout Production Process

2005 ◽  
Vol 71 (2) ◽  
pp. 746-753 ◽  
Author(s):  
Rebecca Montville ◽  
Donald Schaffner
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Detection Limit ◽  
Production Process ◽  
Irrigation Water ◽  
Pathogen Detection ◽  
Monte Carlo Model ◽  
Disease Outbreaks ◽  
Seed Disinfection ◽  
Reduction Strategies

ABSTRACT Food-borne disease outbreaks linked to the consumption of raw sprouts have become a concern over the past decade. A Monte Carlo simulation model of the sprout production process was created to determine the most-effective points for pathogen control. Published literature was reviewed, and relevant data were compiled. Appropriate statistical distributions were determined and used to create the Monte Carlo model with Analytica software. Factors modeled included initial pathogen concentration and prevalence, seed disinfection effectiveness, and sampling of seeds prior to sprouting, sampling of irrigation water, or sampling of the finished product. Pathogen concentration and uniformity of seed contamination had a large effect on the fraction of contaminated batches predicted by the simulation. The model predicted that sprout sampling and irrigation water sampling at the end of the sprouting process would be more effective in pathogen detection than seed sampling prior to production. Day of sampling and type of sample (sprout or water) taken had a minimal effect on rate of detection. Seed disinfection reduced the proportion of contaminated batches, but in some cases it also reduced the ability to detect the pathogen when it was present, because cell numbers were reduced below the detection limit. Both the amount sampled and the pathogen detection limit were shown to be important variables in determining sampling effectiveness. This simulation can also be used to guide further research and compare the levels of effectiveness of different risk reduction strategies.

scholarly journals ANALYSIS OF RANDOM NUMBER GENERATORS USING MONTE CARLO SIMULATION

1994 ◽  
Vol 05 (03) ◽  
pp. 547-560 ◽  
Author(s):  
P.D. CODDINGTON
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ising Model ◽  
High Precision ◽  
Random Number ◽  
Statistical Tests ◽  
Exact Results ◽  
Random Number Generators ◽  
Monte Carlo Algorithms

Monte Carlo simulation is one of the main applications involving the use of random number generators. It is also one of the best methods of testing the randomness properties of such generators, by comparing results of simulations using different generators with each other, or with analytic results. Here we compare the performance of some popular random number generators by high precision Monte Carlo simulation of the 2-d Ising model, for which exact results are known, using the Metropolis, Swendsen-Wang, and Wolff Monte Carlo algorithms. Many widely used generators that perform well in standard statistical tests are shown to fail these Monte Carlo tests.

A Mathematical Model for Tire/Wheel Assembly Balance

1993 ◽  
Vol 21 (4) ◽  
pp. 220-231
Author(s):  
E. J. Ni
Keyword(s):  
Mathematical Model ◽  
Monte Carlo Simulation ◽  
Finite Element ◽  
Monte Carlo ◽  
Monte Carlo Model ◽  
Element Model ◽  
Equation Of Motion ◽  
Rigid Body Dynamics ◽  
Production Distribution ◽  
Body Dynamics

Abstract A mathematical model is developed to calculate the weight required on a tire/wheel assembly to balance wheel nonuniformity effects such as the lateral runout. A finite element model of a tire mounted on a rigid wheel is used to simulate the free spinning about a skewed axis. The result showed that Euler's equation of motion in rigid body dynamics can be used to calculate the imbalance caused by wheel lateral runout. This equation is then used in a Monte Carlo model to simulate a production distribution. The model can be used to define tire and wheel specification limits, and to predict the number of assemblies that will have unacceptable imbalances. The verification of the model and results of the Monte Carlo simulation are presented.

Procurement Risk Management of Engineering Materials Based on Uncertain Rising Prices

2012 ◽  
Vol 174-177 ◽  
pp. 3219-3222
Author(s):  
Hui Chen
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Calculation Method ◽  
Economic Order Quantity ◽  
Purchasing Management ◽  
Order Quantity ◽  
Management Method ◽  
Engineering Materials ◽  
Procurement Risk

Practical risk purchasing management method of engineering materials is put forward by using Monte Carlo Simulation. First, the calculation method of economic order quantity is put forward based on rising price. Second, appropriate insurance inventory is calculated when material requirements is variable.

Monte Carlo Simulation of Diffusion with Uniform Sinks and Sources for Vacancies

2005 ◽  
Vol 237-240 ◽  
pp. 1168-1173 ◽  
Author(s):  
Jaroslav Ženíšek ◽  
Jiří Svoboda ◽  
Franz Dieter Fischer
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Model ◽  
Kirkendall Effect ◽  
The Self ◽  
The Other ◽  
Other Hand ◽  
Self Consistent

A new concept of generation and annihilation of vacancies at uniform sinks and sources for vacancies is incorporated into the standard Monte Carlo model for vacancy mediated diffusion. This model enables to treat the vacancy wind as well as the deformation of the specimen and the shift of the Kirkendall plane. The Monte Carlo model is used for the testing of the recent phenomenological theories of diffusion by Darken, Manning and Moleko. The agreement with the self-consistent Moleko theory is excellent. On the other hand the agreement with the classical Darken theory used very often for the explanation of the Kirkendall effect is rather poor.

scholarly journals Monte Carlo Simulation Approach to Determine the Optimal Solution of Probabilistic Supply Cost

2020 ◽  
Vol 2 (1) ◽  
pp. 1-6
Author(s):  
Helmi Ramadan ◽  
Prana Ugiana Gio ◽  
Elly Rosmaini
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Process ◽  
Historical Data ◽  
Optimal Solution ◽  
Economic Order Quantity ◽  
Simulation Approach ◽  
Order Quantity ◽  
Probabilistic Simulation ◽  
The Future

Monte Carlo simulation is a probabilistic simulation where the solution of problem is given based on random process. The random process involves a probabilitydistribution from data variable collected based on historical data. The used model is probabilistic Economic Order Quantity Model (EOQ). This model then assumed use Monte Carlo simulation, so that obtained the total of optimal supply cost in the future. Based on data processing, the result of probabilistic EOQ is $486128,19. After simulation using Monte Carlo simulation where the demand data follows normal distribution and it is obtained the total of supply cost is $46116,05 in 23 months later. Whereas the demand data uses Weibull distribution is obtained the total of supply stock is $482301,76. So that, Monte Carlo simulation can calculate the total of optimal supply in the future based on historical demand data.

Monte Carlo Simulation of Backscatter Signals Through Thin Scattering Layers for Biomedical Applications

2014 ◽  
Author(s):  
R. Eze ◽  
Y. Hassebo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Path Length ◽  
Biomedical Applications ◽  
Radiation Transport ◽  
Monte Carlo Model ◽  
Thin Layers ◽  
Radiative Properties ◽  
Noninvasive Methods ◽  
Space Applications

Monte Carlo simulation of photon transport is formulated to solve transient radiative transfer equation through thin multilayered scattering-absorbing media with inhomogeneous properties. Though thin layers might seem to be geometrically insignificant, contribution of their radiative properties is relevant in predicting the behavior of most bioengineering, biomedical and space applications. Most traditional Monte Carlo models often fail to capture the presence of thin layers and account for its radiative properties. If the Monte Carlo model is implemented without unique features then the results of the simulation would show incorrect effect of thin layers since the path length of most photons would be significantly larger than the layer thickness and the evaluated photon travel path length would simply not feel the existence of the layer. Numerical and algorithmic features for computation of radiation transport through thin scattering and absorbing layers using the traditional Monte Carlo and an enhanced Monte Carlo model with features specifically developed for thin layers is presented and implemented for the analysis of backscattered radiation. It is observed that while Monte Carlo without special features defines the radiative effect of the layers, the refined technique indicates that layers have a great impact on the backscattered light, especially if the layer properties are distinctly different from those of the contiguous layers. The results have significant implications in the study of diagnostic applications of laser in biomedical applications since backscattered light is one of the non-invasive techniques available for detection of diseases and complements other known methods. Analyses of backscattered signals have also found use in the noninvasive methods of medical use especially in skin diagnostics.

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