American Journal of Industrial Engineering

ISSN (Print): 2377-4320

ISSN (Online): 2377-4339

Editor-in-Chief: Ajay Verma

Website: http://www.sciepub.com/journal/AJIE

   

Article

Machine Selection by AHP and TOPSIS Methods

1Department of Industrial and Production Engineering, Jessore University of Science and Technology, Bangladesh

2Department of Industrial and Production Engineering, Jessore University of Science & Technology, Jessore, Bangladesh


American Journal of Industrial Engineering. 2016, 4(1), 7-13
doi: 10.12691/ajie-4-1-2
Copyright © 2016 Science and Education Publishing

Cite this paper:
Rubayet Karim, C. L Karmaker. Machine Selection by AHP and TOPSIS Methods. American Journal of Industrial Engineering. 2016; 4(1):7-13. doi: 10.12691/ajie-4-1-2.

Correspondence to: Rubayet  Karim, Department of Industrial and Production Engineering, Jessore University of Science and Technology, Bangladesh. Email: rubayet26@gmail.com

Abstract

Selection of the most suitable machine is very crucial in the modern economy to prompt production level as well as revenue generation. In order to endure in the global business scenario, companies must find out the proper way that leads to the successful production environment. Machine selection has become challenging as the number of alternatives and conflicting criteria increase. A decision support system has been developed in this research in machine evaluation process. This framework will act as a guide for decision makers to select the suitable machine via an integrated approach of AHP & TOPSIS. The anticipated methods in this research consist of two steps at its core. In the first step, the criteria of the existing problem are inspected and identified and then the weights of the sector and sub-sector are determined that have come to light by using AHP. In the second step, eligible alternatives are ranked by using TOPSIS. A demonstration of the application of these methodologies in a real life problem is presented.

Keywords

References

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Article

The Accuracy Degree of CFD Turbulence Models for Butterfly Valve Flow Coefficient Prediction

1GUPCO - Gulf of Suez Petroleum Co., 270 Palestine St. 4th Sector, New Maadi, Cairo, Egypt

2Mechanical Power Engineering Department, Faculty of Engineering, Mansoura University, El-Mansoura 35516, Egypt


American Journal of Industrial Engineering. 2016, 4(1), 14-20
doi: 10.12691/ajie-4-1-3
Copyright © 2016 Science and Education Publishing

Cite this paper:
Mohammed M. Said, Hossam S.S. AbdelMeguid, Lotfy H. Rabie. The Accuracy Degree of CFD Turbulence Models for Butterfly Valve Flow Coefficient Prediction. American Journal of Industrial Engineering. 2016; 4(1):14-20. doi: 10.12691/ajie-4-1-3.

Correspondence to: Mohammed  M. Said, GUPCO - Gulf of Suez Petroleum Co., 270 Palestine St. 4th Sector, New Maadi, Cairo, Egypt. Email: saidmm@gupco.net

Abstract

Although engineers are mainly interested in the prediction of mean flow behavior, the turbulence cannot be ignored, because the fluctuations give rise to the extra Reynolds stresses on the mean flow. These extra stresses must be modeled in commercial CFD by selecting convenient turbulence model. The flow inside the control valve is complex and the control valves performance is precisely evaluated by determining the valve coefficient named, flow coefficient. Hence, aim of the present study is to investigate the effect of turbulence model type on the solution accuracy for the valve disk angles 40° and 60° as well as to implement the degree of agreement between experimental and numerical results. The numerical verification has been investigated by FLUENT 6.3 and the valve is meshed by GAMBIT 2. The mesh independent test has been carried out only by standard k-ε to evaluate the mesh effectiveness and attain the best accuracy. Among from these several turbulence models which have been studied here are standard k-ε, realized k-ε, k-ω, and RSM. Butterfly valve, STC model and (DN 50) diameter is chosen to be the test specimen in this research. The results showed that, there is no general turbulent model that can deal successfully with all cases. Numerical and experimental results are in general in good agreement, however are different in details, and showed that, RSM model is the most efficient numerical solver when applied to butterfly valve flow coefficient evaluation. For the future, a significant amount of work still needs to be undertaken in experimental unsteady butterfly valve flow analysis with RSM numerical model.

Keywords

References

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Article

Integrated Decision Making in Production Department inside Internal Supply Chain

1Department of statistical, College of Administration and Economic, University of Sumer, Al-Refaee, Thi-qar, Iraq

2Branch of Industrial Engineering, Department of Production Engineering and Metallurgy, University of Technology, Baghdad, Iraq


American Journal of Industrial Engineering. 2016, 4(1), 21-28
doi: 10.12691/ajie-4-1-4
Copyright © 2016 Science and Education Publishing

Cite this paper:
Watheq H. Laith, Sawsan S.Abed Ali, Mahmoud A. Mahmoud. Integrated Decision Making in Production Department inside Internal Supply Chain. American Journal of Industrial Engineering. 2016; 4(1):21-28. doi: 10.12691/ajie-4-1-4.

Correspondence to: Watheq  H. Laith, Department of statistical, College of Administration and Economic, University of Sumer, Al-Refaee, Thi-qar, Iraq. Email: wahe36@yahoo.com

Abstract

The most important drawback of existing methods used to solve the sequencing problems is the sequence must have a few products and dependent setup time for single demand. The main advantage of this new methodology, it using two methods to determine optimum products sequences with many products and muti-demands and also applied in Wasit company, that has production line produce multi-products. First, modified assignment method (MAM) depends on fundamental of TSP and using it decision maker to determine optimum products sequences step by step or demand by demand to short planning. The second method genetic algorithm (GA) depend on fundamental of (TSPPCA) and using it decision maker to determine optimum products sequences as global optimal or optimal entirely or long planning.

Keywords

References

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