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Showing 5 results for Salehi

R. Tavakkoli-Moghaddam, M. Aryanezhad, H. Kazemipoor , A. Salehipour ,
Volume 19, Issue 1 (International Journal of Engineering 2008)
Abstract

Abstract : A tandem automated guided vehicle (AGV) system deals with grouping workstations into some non-overlapping zones , and assigning exactly one AGV to each zone. This paper presents a new non-linear integer mathematical model to group n machines into N loops that minimizes both inter and intra-loop flows simultaneously. Due to computational difficulties of exact methods in solving our proposed model, a threshold accepting (TA) algorithm is proposed. To show its efficiency, a number of instances generated randomly are solved by this proposed TA and then compared with the LINGO solver package employing the branch-and-bound (B/B) method. The related computational results show that our proposed TA dominates the exact algorithm when the size of instances grows.

  


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.


Mir Saber Salehi Mir, Javad Rezaeian,
Volume 27, Issue 1 (IJIEPR 2016)
Abstract

This paper considers identical parallel machines scheduling problem with past-sequence-dependent setup times, deteriorating jobs and learning effects, in which the actual processing time of a job on each machine is given as a function of the processing times of the jobs already processed and its scheduled position on the corresponding machine. In addition, the setup time of a job on each machine is proportional to the actual processing time of the already processed jobs on the corresponding machine, i.e., the setup time of a job is past- sequence-dependent (p-s-d). The objective is to determine jointly the jobs assigned to each machine and the order of jobs such that the total completion time (called TC) is minimized. Since that the problem is NP-hard, optimal solution for the instances of realistic size cannot be obtained within a reasonable amount of computational time using exact solution approaches. Hence, an efficient method based on ant colony optimization algorithm (ACO) is proposed to solve the given problem. The performance of the presented model and the proposed algorithm is verified by a number of numerical experiments. The related results show that ant colony optimization algorithm is effective and viable approache to generate optimal⁄near optimal solutions within a reasonable amount of computational time.


Mojtaba Salehi,
Volume 28, Issue 3 (IJIEPR 2017)
Abstract

Due to the multiplicity of standards and complex rules; maintenance, repair and servicing of machinery could be done only by the fully qualified and proficient experts. Since the knowledge of such experts is not available all times, using expert systems can help to improve the maintenance process. To address this need and the uncertainty of the maintenance process indicators, this research proposed a Fuzzy Expert Systems (FES) for decision making on the type of service. Since all indicators identified in the literature aren’t important adequately, more influential indicators in the service type selection are chosen using inferential statistical analysis firstly. Then, the fuzzy rules of the knowledge based were designed by these selected indicators. Finally, Inference engine has been designed based on Mamdani model to detect the service type of equipment. This research selected Shemsh Sazan Zanjan Company as a case study to implement the proposed expert system. According to our experiments, the proposed system increases the reliability by suggesting effective ideas that lead to decrease production line breakdowns. The main contribution of this paper is providing a new approach for designing maintenance dynamic FES based on Maintenance Indicators for service type selection that can decrease production line breakdowns.



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