monte carlo simulation

The present study examines the application of deep learning in forecasting option pricing under the stochastic interest rate and stochastic volatility model. In the stochastic interest rate model, the interest rate of financial products is randomly determined at different times. This randomization causes volatility in prices and interest rates. In this research, characteristic function, Monte Carlo simulation and conditional Monte Carlo simulation are used to generate data. Monte Carlo simulation is used as a random and probabilistic method to obtain statistical results and predictions related to option pricing by generating data that belong to the characteristic function. since the financial markets contain a lot of data, in order to be able to manage this data as a financial expert and make decisions based on it, one must master the science of deep learning. The tools available in deep learning and finance affairs are also used to determine and predict the option price under the stochastic volatility model and stochastic interest rate model. Results show that the efficiency of deep learning models, in terms of time and accuracy, compared to traditional methods such as Monte Carlo method is more. The results of this research can be used as a practical tool for investment decisions and risk management.

If important details such as sustainability and resilience are not taken into account in supply chains, the risk of potential threats increases. One of the crucial factors in increasing supply chain resilience is the management of cash flows, especially the management of payable accounts. Managing this factor will lead to better use of financial resources. This study creates a model to optimize the repayment schedules of payable accounts.

To optimize the model, a genetic algorithm was used and simulation was applied to calculate the suitability function. The objective function included the costs of stopping production lines, the costs of producing defective products, and the costs of borrowing for suppliers. The produced output determined the suppliers and the amount of payment to them in each period.

Investigating the feasibility of applying the proposed method was the main finding of this study, and the obtained results proved the model as efficient.

The model used real data from the automotive industry and the results showed that the proposed model has the ability to reduce the costs up to 44.7%.

Given the growing importance of project management and control issues and the benefits of choosing the right management method according to the specific type of project, The purpose of this study is to provide an appropriate method for planning and managing research and development projects. Uncertainty of activities and time required to complete each project activity, requires the project manager to use techniques appropriate to the specific nature of the project under review. In this paper, by describing the probabilistic project of constructing a model airplane according to the probabilistic conditions, a suitable algorithm based on Gert method and Monte Carlo simulation is presented in such a way that as a result the project manager can analyze the basic components from the output of the algorithm and estimate random variables such as the expected time of project completion and the average cost of completing project activities and extract the desired results to better manage and control the project. In order to provide a better understanding of the proposed algorithm, the results of using the algorithm to plan and manage the model aircraft construction project will be reviewed and explained and As a result, the expected time and cost of the project is extracted within a 95% confidence interval.

Today, the need to use efficiency measurement systems in the modern banking industry has become increasingly apparent. Therefore, the efficiency of banks needs to be forecasted so that future economic growth can be monitored in future decisions. This paper designs a new integrated model based on the Stochastic Data Envelopment Analysis (SDEA) model and the Principal Component Analysis (PCA) method in a dynamic environment to forecast the efficiency of branches in the modern banking industry by considering variable returns to scale for them. In order to deal with the uncertainty in efficiency forecasting, the inputs and outputs of the branches are designed as triangular fuzzy stochastic variables with normal distribution. In this study, Monte Carlo (MC) simulation and meta-heuristic algorithms have been used to solve the proposed model. Finally, in order to evaluate the performance and accuracy of the proposed integrated model, a case study based on modern banking indicators has been presented to forecast the efficiency of the future financial period of the branches and the results have been analyzed.

Today, due to the breadth, diversity and multiplicity of investment projects, decision makers and investors are faced with the issue of choosing the most profitable project. The economic rate of return (ERR) index is one of the proper indices that has been presented to determine the profitability of the projects. However, the ERR index has important shortcomings that have led to serious limitations in its use. In this regard, this paper introduces a new measure called the reliable economic rate of return (RERR), which has originated from ERR measure, but it has resolved the drawbacks of ERR index. On the other hand, this paper proposes a new solution by considering the values of the cash flow in terms of fuzzy numbers. After calculating the fuzzy economic rate of return, the profitability potentiality degree of the investment projects can be determined as well as their reliability based on the net present value (NPV) method.

  This paper proves that if we equate the initial cash flow of investment projects with the sum of the negative values ​​of the relevant cash flow stream, then the RERR value would be greater than ‘-1’. In this regard, this paper proposes a solution to modify the initial and final values ​​of the investment cash flow stream, merely without changing middle values. Thereby, the NPV of the modified cash flow does not change. On the other hand, it is sometimes difficult to estimate the values of cash flows as a crisp number, which may lead to a wrong decision in accepting or rejecting projects. For this purpose, this paper considers the values of cash flow stream as fuzzy numbers and describes a solution for calculating the fuzzy RERR using the α-cut method and the RERR index. The proposed solution is compatible with the NPV method and also has fewer calculations than the other methods. This paper also proposes a method that calculates the profitability potentiality degree of projects and provides more comprehensive information to the decision makers by examining the intervals obtained from cutting the fuzzy values of the projects and presenting a new and reliable solution.

Using the @RISK software, it was found that the estimation of fuzzy return rate using RERR index and α-cut method is very close to the simulation result. It confirms the appropriate accuracy of the proposed solution. Also, the results obtained from the software in determining the profitability of projects indicated that the proposed solution is more accurate than the methods of ‘comparison of the expected intervals’ and ‘strict exceedance possibility’. It is also fully compatible with the NPV method.

In this research the market rate is assumed constant, while, in practice it can vary over the periods. Defining the market rate as an inconstant parameter, using the robust optimization approach and investigating the subject of this paper under other uncertainty environments are future study suggestions to both researchers and practitioners.

One of the most important applications of this study is in risky projects wherein estimating the values of cash flow stream as a crisp number is difficult. The results of this paper help decision makers in reducing risk and determining the project profitability. It is worth mentioning that the solution proposed in this paper can be used for all projects with any cash flow stream value.

 Failure to make the correct decision regarding the acceptance or rejection of the projects could have major negative consequences for the companies, organizations and even society. This paper helps decision makers, investors and organizations to determine the projects profitability that could benefit organizations and society.

This paper proposes the RERR measure as a profitability index such with several favorable properties: (ⅰ) it exists and is unique, (ⅱ) it is NPV-consistent in accepting/rejecting decisions, (ⅲ) it provides values greater than ‘-1’, (ⅳ) it takes into account all of the project’s outflows which may be regarded by some practitioners as an appropriate indicator of the overall investment. Furthermore, this paper proposes an appropriate method for calculating the fuzzy RERR and a new method for determining the profitability potentiality degree of the investment projects based on the NPV method.

One of the methods used to measure and evaluate employees is the assessment center. Assessment centers usually have good validity at the instrumental level, but have weaknesses at the summation level. This research seeks to provide a fuzzy system for estimating AC final scores based on competency, intelligence and personality scores by modeling mentality of experts in this field. For this purpose, the data of the assessment center and expert’s opinion have collected and finally a model has been presented to combine these three categories of concepts. The developed model has three sets of trapezoidal fuzzy numbers and 27 rules. In order to validate this model, the data of 116 managers of the country and the simulation of Monte Carlo were used, and it is clear that competence, intelligence and personality are the most important parameters, respectively, in the final score of the evaluation center

: pre-storm proactive recovery planning is an effective tool for reduceing the time and cost of  electrical outage and resiliency enhancement of medium voltage (MV) distribution networks. This paper presents a new framework for setting proactive medium voltage feeders recovery priorities. In this method, the proactive recovery decision making and MV feeders priority setting are considered based on the comparison between different criteria including technical isuue, criticality of loads, economic and accessibility conditions. In this regard, the MV poles fragility curve and Monte Carlo simulation have been employed to predict and estimate the damage of the network. To eliminate the problem of the lack of accurate data and the uncertainties, fuzzy analytical hierarchy process is used to determine proactive recovery prioritization. In the rest of the paper, the proposed approach is applied to a test network. The results demonstrate the suitable performance of this method for determining  feeders recovery priorities especially in the less developed countries which have been facing with the lack of access to the adequate data.

Earned Duration Management (EDM) method has been developed to enhance Earned Value Management and to solve some of its problems, such as separation of measuring time and cost dimensions. By providing performance indicators, this method makes it possible for managers to accurately measure the progress of a project and to calculate the time and cost required to complete it. Nevertheless, the accepted rate of progress indexes of a project is not specified in this method, which brings about ambiguity. Intending to improve earned duration management performance, statistical quality control charts were used, which had previously been applied in Earned Value Management to determine the acceptable cut-off points of indexes of project management. Using Monte Carlo simulation, these charts were calculated at the time of project planning, but not during project implementation. Such calculations give project managers prospective view; in addition, they provide a basis for evaluating and comparing the performance of projects during their progress.

Passenger transferring between origins to destinations, regarding the matter of the variety of services are the main goal in a public transportation (air and ground systems). So, considering the different types of vehicles and increasing different levels of services are the essential issues in hierarchical hub location problems. As well as the effect of disruption (Deliberately and unintentionally), changing the scope of the connectivity in a distribution systems, locations, re–allocations and designing the sustainable network in the presence of disruption are the problems which are less reviewed in a hierarchical hub location field. In this paper a linear model used for designing a sustainable capacitated hierarchical hub location problems considering multi modes of transportation based on two scenarios, with or without any disruption. This model has been solved with GAMS 23.4.3 optimization software and Monte Carlo simulation method (MATLAB R2016a). Based on the results of the network without any disruption, the disruption probability obtained to be used in a network under disruption. The results show that, the model and the proposed solution method, can satisfy the demands in the presence of disruption, changing the location of non–central hub nodes and the re–allocation in each levels of hierarchy. Increasing the discount factors, disruptions and the number of non-central hub nodes increases the total costs of the hierarchical hub network. Also increasing the levels of services, considering the different paths and vehicles can expand the decision making ability in a network.

The purpose of this study is primarily to provide a performance-based budgeting model (PBB) in the Institute for Management and Planning Studies (IMPS). So the one hand allocating budgets to programs is based on programs’ importance and on the other hand, allocating budgets to groups is based on per capita students that were assigned by Ministry of Sciences, Researches and Technology. Considering various criteria in this institute, the PPB Goal programming model is designed. An important point in designing the PBB model is the use of efficiency coefficient calculated based on the Data Envelopment Analysis (DEA) to determine important coefficient of each educational group for budget allocation. In addition, importance coefficient of each program and the goals of the problem are determined based on paired comparison by experts. Given the uncertainty in the parameters of the PBB model, using robust and fuzzy logic, some adopted scenarios from nominal model are designed including robust, fuzzy, and fuzzy-robust models. Finally, the obtained solutions from these models are compared and the strengths and weaknesses of each of them are examined. In addition, in order to evaluate the effectiveness and efficiency of the proposed models, Monte Carlo simulation is used. The modeling results and simulations provide useful information to decision makers for making a reasonable decision by balancing the decision risk levels (possibility of violating constraints) and conservation level.

The growth of two important technologies in solar energy throughout the world, including North America, i.e. the photovoltaic (PV) and the solar thermal (ST), has been dependent on government supports and financial incentives for investment in these technologies. In this paper, huge sets of autoregressive (AR) and vector AR models are developed as candidates for analysis of growth trends of both PV and ST technologies in the US. Then the best models are selected via a systematic model selection method. For the lack of exact data, the effects of incentives on these technologies are estimated by the Monte-Carlo simulation technique. Both real data and simulation results reveal the probability of a hype (herd) behavior in the diffusion of the technologies, both in past and in the present. Moreover, ST technology has already lost its growth speed because of drops in the substituting energy, i.e. natural gas prices. It is shown that the financial incentives had no more influence on ST growth after 2008. Although PV diffusion is accelerating fast, the simulation shows it also may partly lose its current tremendous growth rate, if the incentives are removed. This can be a valuable lesson for the development of renewable energies in

In this paper, the impact of productivity shocks on GDP using advanced algorithm approach and the Monte Carlo simulation in the Iranian economy has been surveyed. After reviewing the theoretical and experimental studies, the variables of inflation, unemployment, potential production and productivity shocks are selected as the variables explaining the variable of gross domestic product. Using three algorithms of fireflies, cuckoo, and particle swarms optimization, the coefficients of each of the independent variables were estimated. After estimating the coefficients and given the uncertainty of the estimated coefficients by the advanced algorithms, Monte Carlo method was used to simulate the equations. Comparing the findings obtained from the simulation and findings of the estimation indicate the high accuracy of the findings obtained from estimates. Given the obtained findings, productivity shocks had very little impact on GDP and the potential production was introduced as the most influential variable.

Planning and decision making are the basic duties of a project manager which without them it is almost impossible to reach the project goals. In planning and decision making process, firstly, risk factors should be identified and after that their impacts and probabilities should be measured. Therefore, risk management is a critical part of the project management and, ignorance of risk factors is one of the primary causes for the project to be failed. The purpose of this paper is to measure risk factors of IT projects. In this research, 13 main factors are identified, then, based on literature and experts opinion these factors are categorized in 3 groups (project factors, software factors, and internal and external organizational factors). Finally, using Monte Carlo simulation, risk associated with each factor is quantified. As the results of this research show “recourse” is the main factor for IT project. To be effective, managers need to consider suitable strategies in order to decrease the dangers related to the possible risks in the resources.Planning and decision making are the basic duties of a project manager which without them it is almost impossible to reach the project goals. In planning and decision making process, firstly, risk factors should be identified and after that their impacts and probabilities should be measured. Therefore, risk management is a critical part of the project management and, ignorance of risk factors is one of the primary causes for the project to be failed. The purpose of this paper is to measure risk factors of IT projects. In this research, 13 main factors are identified, then, based on literature and experts opinion these factors are categorized in 3 groups (project factors, software factors, and internal and external organizational factors). Finally, using Monte Carlo simulation, risk associated with each factor is quantified. As the results of this research show “recourse” is the main factor for IT project. To be effective, managers need to consider suitable strategies in order to decrease the dangers related to the possible risks in the resources.

Making decision to choose the appropriate target market is one of the key decisions in the success of firms، which has direct effect in the amount of their profits. The aim of this paper is to introduce and use of new hybrid method of AHP، Monte Carlo simulation and PROMETHEE to prioritize cities to establish retailers، considering different indices. The problem of this study is related to a factory، constructing premade pieces of buildings، that to introduce and distribute its new products is searching the new retailers in different cities. To prioritize cities، with the interview with experts and the studying of the previous works the indices have been determined and the hierarchy pattern has been made. Then using the hybrid method of AHP and Monte Carlo simulation the weights of the indices have been determined and then using PROMETHEE method the best city has been chosen and the other ones have been prioritized. From the benefits of the new introduced hybrid method with respect to other ways of selecting target markets is decreasing the risk and increasing the power of decision making.