Showing 6 results for Monte Carlo Simulation
Saeed Dehnavi-Arani, Hadi Mokhtari,
Volume 0, Issue 0 (10-2024)
Abstract
The selection of material handling equipment is crucial for companies as it significantly impacts productivity in manufacturing and service operations. This decision-making process involves multiple criteria that are often conflicting and cannot be easily compared. To address this complexity, a multi-criteria decision-making framework is employed, where experts' preferences and criteria weights are expressed using fuzzy numbers, such as trapezoidal or triangular fuzzy numbers. The fuzzy VIKOR methodology is then utilized to rank the alternatives based on the aggregate fuzzy values of ratings and weights. A Monte Carlo simulation and a centroid method are employed to derive a suitable shape and obtain a precise value. This additional step enhances the robustness and accuracy of the decision-making process. To demonstrate the effectiveness of this approach, a case study is conducted at R.S-Arvin, a manufacturing company. By applying the proposed methodology to a real-world scenario, the study showcases how it can be used to make informed decisions in practical settings. The results obtained from this case study highlight the benefits of incorporating fuzzy logic and simulation techniques in material handling equipment selection processes. Overall, this research contributes to advancing decision-making practices in companies by providing a systematic and comprehensive approach that considers multiple criteria and uncertainties inherent in such complex systems. The integration of fuzzy logic and defuzzification methods (simulation and centroid method) offers a practical solution for addressing real-world challenges related to equipment selection and optimization in manufacturing environments.
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. 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.
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.
Abolfazl Khatti Dizabadi, Abdollah Arasteh, Mohammad Mahdi Paydar,
Volume 33, Issue 4 (12-2022)
Abstract
Supply chain management is one of the requirements for achieving economic growth in any supply chain. If managers' decisions are optimally allocated, it will be possible for companies and industries with a competitive and profitable advantage to grow and develop. The main desire of any company for survival is to minimize costs and maximize profitability. Due to the increasing complexity and dynamics of the situation, decision-making in this area requires more advanced analytical methods. Accordingly, the Real options theory has emerged, which introduces a new way of thinking about investing, especially in conditions of uncertainty. In this paper, a multi-period model is considered that examines the demand uncertainty in each period and also uses the Real options theory to seek the optimal strategy for investors in conditions of uncertainty and the effect of investors’ discretion on it. Using a decision tree to estimate the probable demand in each period and using Monte Carlo simulations to identify the lowest cost scenario in each period, the model has been solved in this research. In the case of the uncertainty parameter, sensitivity analysis is performed, and under different values of this parameter, the obtained result is evaluated and validated. And the extension of outsourcing will increase the company’s profitability and meet higher demand and lower costs.
