Showing 11 results for Rabi
S. Rastegari, Z. Salehpour , Bakhshi , H. Arabi,
Volume 19, Issue 5 (IJES 2008)
Abstract
Formation mechanism of silicon modified aluminide coating applied on a nickel base super alloy IN-738 LC by pack cementation process was the subject of investigation in this research. Study of the microstructure and compositions of the coating was carried out, using optical and scanning electron microscopes, EDS and X-ray diffraction (XRD) techniques. The results showed that due to low partial pressure of silicon halide in Pack process, the amount of soluble silicon in the coating can not exceed 1 wt % of the total coating composition, although the Si content of the particles present within the outer coating sub-layer could reach as far as 5 wt%. Thus, the small amount of soluble Si within the coating could not provide the necessary conditions for formation of any intermetallic and it seems that the formation and growth behavior of various sub-layers in Si-modified aluminide coating is similar to that of simple aluminide coating. Three sub-layers were detected in the coating structure after being subjected to diffusion heat treatment. They were an outer Ni-rich NiAl sub-layer a middle Ni-rich NiAl and an inter diffusion sub-layers. The details of formations and growth mechanism of these sub-layers has been discussed in this research.
H. Arabi, M.t Salehi, B. Mirzakhani, M.r. Aboutalebi , S.h. Seyedein , S. Khoddam,
Volume 19, Issue 5 (IJES 2008)
Abstract
Hot torsion test (HTT) has extensively been used to analysis and physically model flow behavior and microstructure evolution of materials and alloys during hot deformation processes. In this test, the specimen geometry has a great influence in obtaining reliable test results. In this paper, the interaction of thermal-mechanical conditions and geometry of the HTT specimen was studied. The commercial finite element package ANSYS was utilized for prediction of temperature distribution during reheating treatment and a thermo-rigid viscoplastic FE code, THORAX.FOR, was used to predict thermo-mechanical parameters during the test for API-X70 micro alloyed steel. Simulation results show that no proper geometry and dimension selection result in non uniform temperature within specimen and predicted to have effects on the consequence assessment of material behavior during hot deformation. Recommendations on finding proper specimen geometry for reducing temperature gradient along the gauge part of specimen will be given to create homogeneous temperature as much as possible in order to avoid uncertainty in consequent results of HTT.
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Volume 20, Issue 1 (IJIEPR 2009)
Abstract
 The problem of lot sizing, sequencing and scheduling multiple products in flow line production systems has been studied by several authors. Almost all of the researches in this area assumed that setup times and costs are sequence –independent even though sequence dependent setups are common in practice. In this paper we present a new mixed integer non linear program (MINLP) and a heuristic method to solve the problem in sequence dependent case. Furthermore, a genetic algorithm has been developed which applies this constructive heuristic to generate initial population. These two proposed solution methods are compared on randomly generated problems. Computational results show a clear superiority of our proposed GA for majority of the test problems.
Ali Yahyatabar Arabi, Abdolhamid Eshraghnia Jahromi, Mohammad Shabannataj,
Volume 24, Issue 2 (IJIEPR 2013)
Abstract
Redundancy technique is known as a way to enhance the reliability and availability of non-reparable systems, but for repairable systems, another factor is getting prominent called as the number of maintenance resources. In this study, availability optimization of series-parallel systems is modelled by using Markovian process by which the number of maintenance resources is located into the objective model under constraints such as cost, weight, and volume. Due to complexity of the model as nonlinear programming , solving the model by commercial softwares is not possible, and a simple heuristic method called as simulated annealing is applied. Our main contribution in this study is related to the development of a new availability model considering a new decision variable called as the number of maintenance resources. A numerical simulation is solved and the results are shown to demonstrate the effecienct of the method.
Mohammad Mahdi Nasiri, Nafiseh Shamsi Gamchi, Seyed Ali Torabi,
Volume 27, Issue 4 (IJIEPR 2016)
Abstract
Hubs are critical elements of transportation networks. Location of hubs and allocation of demands to them are of high importance in the network design. The most important purpose of these models is to minimize the cost, but path reliability is also another important factor which can influence the location of hubs. In this paper, we propose a P-center hub location model with full interconnection among hubs while there are different paths between origins and destinations. The purpose of the model is to determine the reliable path with lower cost. Unlike the prior studies, the number of hubs in the path is not limited to two hubs. The presented model in this paper is bi-objective and includes cost and reliability to determine the best locations for hubs, allocation of the demands to hubs and the best path. In order to illustrate our model, a numerical example is presented and solved using the Cuckoo Optimization Algorithm.
- S. Ali Torabi, - Abtin Boostani,
Volume 29, Issue 1 (IJIEPR 2018)
Abstract
This paper addresses supplier selection and order allocation problem while considering the losses arising from the risk of sanction in Iran’s Oil & Gas Drilling Industry. In the proposed study, two general classes of items and two different classes of suppliers are considered. AHP is first used to rank the potential suppliers. Then, a multi-objective linear programming model is proposed to determine the best suppliers and their allocated orders. A numerical example is presented to demonstrate the applicability of the proposed model.
Mostafa Ekhtiari, Mostafa Zandieh, Akbar Alem-Tabriz, Masood Rabieh,
Volume 29, Issue 1 (IJIEPR 2018)
Abstract
Supplier selection is one of the influential decisions for effectiveness of purchasing and manufacturing policies under competitive conditions of the market. Regarding the fact that decision makers (DMs) consider conflicting criteria for selecting suppliers, multiple-criteria programming is a promising approach to solve the problem. This paper develops a nadir compromise programming (NCP) model for decision-making under uncertainty on the selection of suppliers within the framework of binary programming. Depending on the condition of uncertainty, three statuses are taken into consideration and a solution approach is proposed for each status. A pure deterministic NCP model is presented for solving the problem in white condition (certainty of data) and a solution approach resulted from combination of NCP and stochastic programming is introduced to solve the model in black (uncertainty of data) situation. The paper also proposes a NCP model under certainty and uncertainty for solving problem under grey (a combination of certainty and uncertainty of data) conditions. The proposed approaches are illustrated for a real problem in steel industry with multiple objectives. Also, a simulation approach has been designed in order to examine the results obtained and also verifies capabilities of the proposed model.
Nima Hamta, Samira Rabiee,
Volume 32, Issue 3 (IJIEPR 2021)
Abstract
One of the challenging issues in today’s competitive world for servicing companies is uncertainty in some factors or parameters that they often derive from fluctuations of market price and other reasons. With regard to this subject, it would be essential to provide robust solutions in uncertain situations. This paper addresses an open vehicle routing problem with demand uncertainty and cost of vehicle uncertainty. Bertsimas and Sim’s method has been applied to deal with uncertainty in this paper. In addition, a deterministic model of open vehicle routing problem is developed to present a robust counterpart model. The deterministic and the robust model is solved by GAMS software. Then, the mean and standard deviations of obtained solutions were compared in different uncertainty levels in numerous numerical examples to investigate the performance of the developed robust model and deterministic model. The computational results show that the robust model has a better performance than the solutions obtained by the deterministic model.
Rouhollah Sohrabi,
Volume 33, Issue 2 (IJIEPR 2022)
Abstract
Nowadays, major challenges in the cold chain of perishable products, such as dairy products, are that these products do not reach customers on time. Answering the question of how to make the cold supply chain of perishable products more agile, the possibility of more control over this issue can be increased. This study tries to investigate the factors affecting the agility of the cold supply chain and after identifying the effective factors, rank them using the GRAY-DEMATEL-AHP. To data gathering, the literature of the subject and the opinions of experts and stakeholders who have sufficient experience in the cold chain have been used and the identified factors have been confirmed after several revisions by the Delphi through snowball sampling. Also, in order to take advantage of both the GRAY and DEMATEL approaches, this paper uses a combination of these two methods to examine causal relationships among the factors affecting the agility of the cold supply chain. The results show that Among the sourcing sub-factor, government decision-making and policies with a weight of 0.212 has gained the first rank and in the sub-factor of distribution, loading time and speed of action in distribution, with a weight of 0.188, has gained the first rank. Also, among the sub-factor of production, accurate planning and speed of action in order production, with a weight of 0.342, has gained the first rank. This paper adds valuable knowledge to the study of the dairy industry cold supply chain agility.
Rabie Mosaad Rabie, Hegazy Zaher, Naglaa Ragaa Saied, Heba Sayed,
Volume 35, Issue 1 (IJIEPR 2024)
Abstract
Harris Hawks Optimization (HHO) algorithm, which is a new metaheuristic algorithm that has shown promising results in comparison to other optimization methods. The surprise pounce is a cooperative behavior and chasing style exhibited by Harris' Hawks in nature. To address the limitations of HHO, specifically its susceptibility to local optima and lack of population diversity, a modified version called Modified Harris Hawks Optimization (MHHO) is proposed for solving global optimization problems. A mutation-selection approach is utilized in the proposed Modified Harris Hawks Optimization (MHHO) algorithm. Through systematic experiments conducted on 23 benchmark functions, the results have demonstrated that the MHHO algorithm offers a more reliable solution compared to other established algorithms. The MHHO algorithm exhibits superior performance to the basic HHO, as evidenced by its superior average values and standard deviations. Additionally, it achieves the smallest average values among other algorithms while also improving the convergence speed. The experiments demonstrate competitive results compared to other meta-heuristic algorithms, which provide evidence that MHHO outperforms others in terms of optimization performance.
Khamiss Cheikh, El Mostapha Boudi, Hamza Mokhliss, Rabi Rabi,
Volume 35, Issue 3 (IJIEPR- In Progress 2024)
Abstract
Maintenance plan efficacy traditionally prioritizes long-term predicted maintenance cost rates, emphasizing performance-centric approaches. However, such criteria often neglect the fluctuation in maintenance costs over renewal cycles, posing challenges from a risk management perspective. This study challenges conventional solutions by integrating both performance and robustness considerations to offer more suitable maintenance options.
The study evaluates two representative maintenance approaches: a block replacement strategy and a periodic inspection and replacement strategy. It introduces novel metrics to assess these approaches, including long-term expected maintenance cost rate as a performance metric and variance of maintenance cost per renewal cycle as a robustness metric.
Mathematical models based on the homogeneous Gamma degradation process and probability theory are employed to quantify these strategies. Comparative analysis reveals that while higher-performing strategies may demonstrate cost efficiency over the long term, they also entail greater risk due to potential cost variability across renewal cycles.
The study underscores the necessity for a comprehensive evaluation that balances performance and resilience in maintenance decision-making. By leveraging the Monte Carlo Method, this research offers a critical appraisal of maintenance strategies, aiming to enhance decision-making frameworks with insights that integrate performance and robustness considerations.
