this paper investigates the radiative absorptivity of a spherical cavity by using monte carlo techniques. several parameters such as the angle of incoming radiation which inters the cavity for both diffuse wall cavity and specular wall cavity has been  investigated. the absorptivity of a spherical cavity has been calculated and compared with the exact values when the angle of incoming radiation...

Background: Palladium-103 (103Pd) is a brachytherapy source for cancer treatment. The Monte Carlo codes are usually applied for dose distribution and effect of shieldings. Monte Carlo calculation of dose distribution in water phantom due to a MED3633 103Pd source is presented in this work. Materials and Methods: The dose distribution around the 103Pd Model MED3633 located in the center of 30×...

Since supply chains highly impact the financial performance of companies, it is important to optimize and analyze their Key Performance Indicators (KPI). The synergistic combination of Particle Swarm Optimization (PSO) and Monte Carlo simulation is applied to determine the optimal reorder point of warehouses in supply chains. The goal of the optimization is the minimization of the objective fun...

Real-world distributed systems and networks are often unreliable and subject to random failures of its components. Such a stochastic behavior affects adversely the complexity of optimization tasks performed routinely upon such systems. In this work we investigate Monte Carlo solutions for a class of two-stage optimization problems in stochastic networks in which the expected value of resources ...

Real-world distributed systems and networks are often unreliable and subject to random failures of its components. Such a stochastic behavior affects adversely the complexity of optimization tasks performed routinely upon such systems. In this work we investigate Monte Carlo solutions for a class of two-stage optimization problems in stochastic networks in which the expected value of resources ...

A new approach for estimating the aggregate hierarchical logit model is presented. Though usually derived from random utility theory assuming correlated stochastic errors, the model can also be derived as a solution to a maximum entropy problem. Under the latter approach, the Lagrange multipliers of the optimization problem can be understood as parameter estimators of the model. Based on theore...

This paper presents an optimization-based method to aid capacity planning decisions by quantifying the trade-off between the capital and operating costs of a production facility and the quality of finished products. Given forecasted market demands during multiple production periods, multi-objective optimization selects the quantity and the types of production machines to be purchased during eac...

In this chapter we discuss parallel algorithms for solving some classes of global optimization problems. We present an introductory survey of parallel algorithms that have been used to solve structured problems (partially separable, and large-scale block structured problems), algorithms based on parallel local searches, Monte Carlo approaches and parallel algorithms for some location problems.

Motivated by the numerical resolution of stochastic optimization problems subject to measurability constraints, we focus upon the issue of how to discretize the components arising in the problem formulation. By means of a counterexample based on Monte Carlo approximation, we emphasize the importance of independent discretization of, on the one side, the random variable modelling uncertainties (...

In today’s competitive market, businesses are adopting new practices like Design for Six Sigma (DFSS), a customer driven, structured methodology for faster-to-market, higher quality, and less costly new products and services. Monte Carlo simulation and stochastic optimization can help DFSS practitioners understand the variation inherent in a new technology, process, or product, and can be used ...