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Showing 8 results for Monte Carlo Simulation

Kamran Shahanaghi, Hamid Babaei , Arash Bakhsha,
Volume 20, Issue 1 (5-2009)
Abstract

In this paper we focus on a continuously deteriorating two units series equipment which its failure can not be measured by cost criterion. For these types of systems avoiding failure during the actual operation of the system is extremely important. In this paper we determine inspection periods and maintenance policy in such a way that failure probability is limited to a pre-specified value and then optimum policy and inspection period are obtained to minimize long-run cost per time unit. The inspection periods and maintenance policy are found in two phases. Failure probability is limited to a pre-specified value In the first phase, and in the second phase optimum maintenance thresholds and inspection periods are obtained in such a way that minimize long-run expected.
M. Ebrahimi, R. Farnoosh,
Volume 20, Issue 4 (4-2010)
Abstract

This paper is intended to provide a numerical algorithm based on random sampling for solving the linear Volterra integral equations of the second kind. This method is a Monte Carlo (MC) method based on the simulation of a continuous Markov chain. To illustrate the usefulness of this technique we apply it to a test problem. Numerical results are performed in order to show the efficiency and accuracy of the present method.
Rasoul Haji, Mohammadmohsen Moarefdoost, Seyed Babak Ebrahimi,
Volume 21, Issue 4 (12-2010)
Abstract

  This paper aims to evaluate inventory cost of a Two-echelon serial supply chain system under vendor managed inventory program with stochastic demand, and examine the effect of environmental factors on the cost of overall system. For this purpose, we consider a two-echelon serial supply chain with a manufacturer and a retailer. Under Vendor managed inventory program, the decision on inventory levels are made by manufacturer centrally. In this paper, we assume that the manufacturer monitors inventory levels at the retailer location and replenishes retailer's stock under (r, n, q) policy moreover, the manufacturer follows make-to-order strategy in order to respond retailer's orders. In the other word, when the inventory position at the retailer reaches reorder point, r, the manufacturer initiates production of Q=nq units with finite production rate, p. The manufacturer replenishes the retailer's stock with replenishment frequency n, and the complete batch of q units to the retailer during the production time. We develop a renewal reward model for the case of Poisson demand, and drive the mathematical formula of the long run average total inventory cost of system under VMI. Then, by using Monte Carlo simulation, we examine the effect of environmental factors on the cost of overall system under VMI .


Masoud Mahootchi, Taher Ahmadi, Kumaraswamy Ponnambalam,
Volume 23, Issue 4 (11-2012)
Abstract

This paper presents a new formulation for warehouse inventory management in a stochastic situation. The primary source of this formulation is derived from FP model, which has been proposed by Fletcher and Ponnambalam for reservoir management. The new proposed mathematical model is based on the first and the second moments of storage as a stochastic variable. Using this model, the expected value of storage, the variance of storage, and the optimal ordering policies are determined. Moreover, the probability of within containment, surplus, and shortage are computable without adding any new variables. To validate the optimization model, a Monte Carlo simulation is used. Furthermore, to evaluate the performance of the optimal FP policy, It is compared to (s*,S*) policy, as a very popular policy used in the literature, in terms of the expected total annual cost and the service level. It is also demonstrated that the FP policy has a superior performances than (s*,S*) policy.
M. Reza Peyghami, Abdollah Aghaie, Hadi Mokhtari,
Volume 24, Issue 3 (9-2013)
Abstract

In this paper, we consider a stochastic Time-Cost Tradeoff Problem (TCTP) in PERT networks for project management, in which all activities are subjected to a linear cost function and assumed to be exponentially distributed. The aim of this problem is to maximize the project completion probability with a pre-known deadline to a predefined probability such that the required additional cost is minimized. A single path TCTP is constructed as an optimization problem with decision variables of activity mean durations. We then reformulate the single path TCTP as a cone quadratic program in order to apply polynomial time interior point methods to solve the reformulation. Finally, we develop an iterative algorithm based on Monte Carlo simulation technique and conic optimization to solve general TCTP. The proposed approach has been tested on some randomly generated test problems. The results illustrate the good performance of our new approach.
Rassoul Noorossana, Mahnam Najafi,
Volume 28, Issue 4 (11-2017)
Abstract

Change point estimation is as an effective method for identifying the time of a change in production and service processes. In most of the statistical quality control literature, it is usually assumed that the quality characteristic of interest is independently and identically distributed over time. It is obvious that this assumption could be easily violated in practice. In this paper, we use maximum likelihood estimation method to estimate when a step change has occurred in a high yield process by allowing a serial correlation between observations. Monte Carlo simulation is used as a vehicle to evaluate performance of the proposed method. Results indicate satisfactory performance for the proposed method.


Mahdieh Akhbari,
Volume 29, Issue 2 (6-2018)
Abstract

The aim of this study is to present a new method to predict project time and cost under uncertainty. Assuming that what happens in projects implementation which is expressed in the form of Earned Value Management (EVM) indicators is primarily related to the nature of randomness or unreliability, in this study, by using Monte Carlo simulation, and assuming a specific distribution for the time and cost of project activities, a significant number of predicting scenarios will be simulated. According to the data, an artificial neural network is used as efficient data mining methods to estimate the project time and cost at completion.
Arezoo Jahani, Parastoo Mohammadi, Hamid Mashreghi,
Volume 29, Issue 2 (6-2018)
Abstract

Innovation & Prosperity Fund (IPfund) in Iran as a governmental organization aims to develop new technology-based firms (NTBF) by its available resources through financing these firms. The innovative projects which refer to IPfund for financing are in a stage which can receive both fixed rate facilities and partnership in the projects, i.e. profit loss sharing (PLS). Since this fund must protect its initial and real value of its capital against inflation rate, therefore, this study aims to examine the suitable financing methods with considering risk. For this purpose we study on risk assessment models to see how to use risk adjusted net present value for knowledge based projects. On this basis, the NPV of a project has been analyzed by taking into account the risk variables (sales revenue and the cost of fixed investment) and using Monte Carlo simulation. The results indicate that in most cases for a project, the risk adjusted NPV in partnership scenario is more than the other scenario. In addition to, partnership in projects which demand for industrial production facilities is preferable for the IPfund than projects calling for working capital.

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