Showing 31 results for Simulation
Zahra Karimi Ezmareh, Gholam Hossein Yari,
Volume 30, Issue 2 (6-2019)
Abstract
In this paper, a new distribution that is highly applicable in the fields of reliability and economics is introduced. Also the parameters of this distribution is estimated using two methods of Maximum Likelihood and Bayes with two prior distributions Weibull and Uniform, and these two methods are compared using Monte-Carlo simulation. Finally, this new model is fit on the real data(with the failure time of 84 aircraft) and some of comparative criteria are calculated to confirm superiority of the proposed model compared to other models.
Mohmmad Anvar Adibhesami, Ahmad Ekhlassi, Ali Mohammad Mosadeghrad, Amirhossein Mohebifar,
Volume 30, Issue 2 (6-2019)
Abstract
The CPM (critical path method) technique is to search out the longest path to try and do activities, so as to compress and cut back the time it takes for a project, which finally ends up inside the creation of an identical and intensive network of activities inside the targeted work. This formal random simulation study has been recognized as a remedy for the shortcomings that are inherent to the classic critical path technique (CPM) project analysis. Considering the importance of time, the cost of activities within the network, and rising the calculation of the critical path during this study, Critical Path technique has been applied to improve critical routing intelligence. This study, by simulating and analyzing dragonfly's splotched and regular patterns, has obtained the precise algorithm of attainable paths with the smallest amount cost and time for every activity. This has been done to put down the restrictions and enhance the computing potency of classic CPM analysis. The simulation results of using Dragonfly Algorithm (DA) in CPM, show the longest path in shortest time with the lowest cost. This new answer to CPM network analysis can provide project management with a convenient tool.
Mohammad Sarvar Masouleh, Amir Azizi,
Volume 30, Issue 4 (12-2019)
Abstract
The present research aims at investigating feasible improvements by determining optimal number of stations and required workforce using a simulation system, with the ultimate goal of reaching optimal throughput while respecting the problem constraints in an attempt to achieve maximum feasible performance in terms of production rate. For this purpose, similar research works were investigated by reviewing the relevant pieces of the literature on simulation model, car signoff station, and techniques for optimizing the station, and the model of the car signoff unit was designed using data gathering tools, existing documents, and actual observations. Subsequently, the model was validated by means of descriptive statistics and analysis of variance (ANOVA). Furthermore, available data was analyzed using ARENA and SPSS software tools. In a next step, potential improvements of the unit were identified and the model was evaluated accordingly. The results indicated that some 80% of the existing problems could be addressed by appropriately planning for human resources, on-time provision of the required material at reworking units, and minimization of transportation at the stations that contributed the most to the working queue. Thus, the waiting time per station could be minimized while increasing the production rate.
Dr V.k. Chawla, A.k. Chanda, Surjit Angra,
Volume 31, Issue 1 (3-2020)
Abstract
The selection of an appropriate cutting tool for the production of different jobs in a flexible manufacturing system (FMS) can play a pivotal role in the efficient utilization of the FMS. The selection procedure of a cutting tool for different production operations becomes more significant with the availability of similar types of tools in the FMS. In order to select and allocate appropriate tool for various production operations in the FMS, the tool selection rules are commonly used. The application of tool selection rules is also observed to be beneficial when a system demands two or more tools for the production operations at different work centers at the same time in the FMS. In this paper, investigations are carried out to evaluate the performance of different tool selection rules in the FMS. The performance of the tool selection rules is evaluated by simulation with respect to different performance parameters in the FMS namely makespan, mean work center utilization (%) and mean automatic tool transporter (ATT) utilization (%).
Rezvan Rezaei, Gholam Hossein Yari, Zahra Karimi Ezmareh,
Volume 31, Issue 3 (9-2020)
Abstract
In this paper, a new five-parameter distribution is proposed that is called MarshallOlkin Gompertz Makeham distribution(MOGM). This new model is applicable in analysis lifetime data, engineering and actuarial. In this research, some properties of the new model such as mode, moment, Reyni entropy, Tsallis entropy, quantile function and the hazard rate function which is decreasing and unimodal, are studied. The unknown parameters of the MOGM distribution are estimated using the maximum likelihood and Bayes methods. Then these methods are compared using Monte Carlo simulation and the best estimator is proposed. Finally, applications of the proposed model are illustrated to show its usefulness.
Parham Azimi, Shahed Sholekar,
Volume 32, Issue 1 (1-2021)
Abstract
According to the real projects’ data, activity durations are affected by numerous parameters. In this research, we have developed a multi-resource multi objective multi-mode resource constrained scheduling problem with stochastic durations where the mean and the standard deviation of activity durations are related to the mode in which each activity is performed. The objective functions of model were to minimize the net present value and makespan of the project. A simulation-based optimization approach was used to handle the problem with several stochastic events. This feature helped us to find several solutions quickly while there was no need to take simplification assumptions. To test the efficiency of the proposed algorithm, several test problems were taken from the PSPLIB directory and solved. The results show the efficiency of the proposed algorithm both in quality of the solutions and the speed.
Mohsen Khezeli, Esmaeil Najafi, Mohammad Haji Molana, Masoud Seidi,
Volume 32, Issue 2 (6-2021)
Abstract
One of the most important fields of logistic network is transportation network design that has an important effect on strategic decisions in supply chain management. It has recently attracted the attention of many researchers. In this paper, a multi-stage and multi-product logistic network design is considered.
This paper presents a hybrid approach based on simulation and optimization (Simulation based optimization), the model is formulated and presented in three stages. At first, the practical production capacity of each product is calculated using the Overall Equipment Effectiveness (OEE) index, in the second stage, the optimization of loading schedules is simulated. The layout of the loading equipment, the number of equipment per line, the time of each step of the loading process, the resources used by each equipment were simulated, and the output of the model determines the maximum number of loaded vehicles in each period. Finally, a multi-objective model is presented to optimize the transportation time and cost of products. A mixed integer nonlinear programming (MINLP) model is formulated in such a way as to minimize transportation costs and maximize the use of time on the planning horizon. We have used Arena simulation software to solve the second stage of the problem, the results of which will be explained. It is also used GAMS software to solve the final stage of the model and optimize the transporting cost and find the optimal solutions. Several test problems were generated and it showed that the proposed algorithm could find good solutions in reasonable time spans.
Zahra Karimi Ezmareh, Gholamhossein Yari,
Volume 33, Issue 4 (12-2022)
Abstract
Recently, generalized distributions have received much attention due to their high applicability and flexibility. This paper introduces a new five-parameter distribution called Kumaraswamy-G generalized Gompertz distribution, which is widely used in the field of survival and lifetime data. In introducing a new distribution, it is important to study the statistical properties and the estimation of its parameters. Therefore, this paper studies the statistical properties of this new distribution. In addition, the parameters of this distribution are estimated by three methods. Finally, using a real dataset, the performance of the introduced distribution is investigated.
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.
Hamza Samouche, Abdellah El Barkany, Ahmed Elkhalfi,
Volume 34, Issue 2 (6-2023)
Abstract
Sales and operations planning (S&OP) is considered as an important tool at the planning strategic level. Its models vary depending on industries. The Asian model is known to be very developed. Having several parameters, the Asian model proves to be an effective tool, precisely for the study of capacity. However, after several searches made in various databases, we did not find any concrete model actually used in industry and whose parameters are presented and which defines the analysis logic to better align supply and demand. In this article, we will carry out various simulations on the basis of the data of a model of sales and operations planning used in a wire harnesses factory, in order to explain the decision-making process during S&OP meetings. The parameters of the model and the various constraints that were facing the sales and operations planning team are presented and discussed as well as the financial consequences of certain decisions. As a result of this study, we can notice that S&OP is indeed a powerful tool that makes it possible to detect in advance the various constraints whose resolution concludes in an optimal alignment between customer demand and factory capacity.
Saeed Dehnavi-Arani, Hadi Mokhtari,
Volume 36, Issue 2 (6-2025)
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.
