ORIGINAL_ARTICLE
Methodology based on MCDM for risk management in EPC projects: A Case Study of LPG Storage Tanks Construction
The most important aim of every project is on time completion, budget consideration and reaching the highest possible quality, based on contract. This paper suggests a methodology for risk management in engineering, procurement, and construction (EPC) projects. Risk management enables project teams to perform with minimum deviation from predetermined goals. The proposed methodology identifies and evaluates critical risks of EPC projects using multi criteria decision making (MCDM) techniques. Then, by the means of developed earned value management (EVM) technique and considering Project Risk index (PRI), this work will proceed to estimate the degree of risk effects on project objectives. The optimal control measures (CMs) to address the risks are found through a goal programming model. The methodology was implemented on a case study in oil and gas industry in Iran. Results of this study show the impact of critical risks on objectives of EPC projects.
http://www.jise.ir/article_9934_b5b19e6d23999d16bf7489ec0d49988d.pdf
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23
Risk management in EPC projects
Critical risks
MCDM techniques
EVM method
Linear Programming
Control Measures
Mohammadreza
Badalpur
mrbp@osanat.com
true
1
Industrial Engineering College, Islamic Azad University, South Tehran Branch,
Tehran, Iran.
Industrial Engineering College, Islamic Azad University, South Tehran Branch,
Tehran, Iran.
Industrial Engineering College, Islamic Azad University, South Tehran Branch,
Tehran, Iran.
AUTHOR
Ashkan
Hafezalkotob
a_hafez@azad.ac.ir
true
2
Industrial Engineering College, Islamic Azad University, South Tehran Branch,
Tehran, Iran.
Industrial Engineering College, Islamic Azad University, South Tehran Branch,
Tehran, Iran.
Industrial Engineering College, Islamic Azad University, South Tehran Branch,
Tehran, Iran.
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Adedokun, O.A., D.R. Ogunsemi, I.O. Aje, O.A. Awodele and D.O Dairo.(2011).
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Sound Parkway NW, Suite 300,Boca Raton, FL 33487-2742.
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Alam Tabriz Akbar et al.(2013), A Combined Approach of the Earned Value Management
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and the Risk Management for Estimating Final Results of Projects in Fuzzy
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Environment
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Aliverdi Reza, Moslemi Naeni Leila, Salehipour Amir.(2013).Monitoring project duration
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and cost in a construction project by applying statistical quality control charts,
11
International Journal of Project Management 31, 411–423.
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Identifying, Prioritization and Responding to Quality Risks Using COQ Approach in
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Construction Industry, Caspian Journal of Applied Sciences Research, 2(6), pp. 21-32.
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Karlstads Universitet, Universitetsgatan, 65188, KARLSTAD SWEDEN.
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95
ORIGINAL_ARTICLE
Two new heuristic algorithms for Covering Tour Problem
Covering Tour Problem (CTP) is the generalized form of Traveling Salesman Problem (TSP), which has found different applications in the designing of distribution networks, disaster relief, and transportation routing. The purpose of this problem is to determine the Hamiltoniancyclewiththe lowest costusinga subset of all the nodes, such that the other nodes would be in a distance shorter than the pre-specified one, from at least one visited node. In this paper, two new heuristic algorithms called MDMC and AGENI are offered to solve CTP. In order to assess the performance of the proposed algorithms in small scale, several test problems are accurately solved and the results compared with those from the proposed heuristic algorithms. Also, in large scales, the results of each of proposed algorithms are compared with the three heuristic algorithms existing in the literature. Finally, the effect of neighborhood searcheson the performance of the proposed algorithms will be investigated. The results, show that the performance of the proposed algorithms in small and large scales is appropriate.
http://www.jise.ir/article_9800_d4492b70647f0516665822fae6c8e61d.pdf
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41
covering tour problem
local search, heuristic methods
Mehdi
Alinaghian
true
1
Department of Industrial and Systems Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran.
Department of Industrial and Systems Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran.
Department of Industrial and Systems Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran.
LEAD_AUTHOR
Alireza
Goli
true
2
Department of Industrial and Systems Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran.
Department of Industrial and Systems Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran.
Department of Industrial and Systems Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran.
AUTHOR
Arkin E. M. and Hassin R.,(1994)."Approximation algorithms for the geometric covering salesman
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pp. 791-812.
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5
vol. 23, pp. 208-213.
6
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Formulations and heuristics," European Journal of Operational Research, vol. 73, pp. 114-126.
8
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16
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Health Care Facilities in SuhumDistrict, Ghama," Journal of Regional Science, vol. 38, pp. 621-638.
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25
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Networks, 64(3), 160-168.
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34
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44
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IN FACILITY LOCATION," Computers & Operations Research.
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49
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50
ORIGINAL_ARTICLE
Multi-objective robust optimization model for social responsible closed-loop supply chain solved by non-dominated sorting genetic algorithm
In this study a supply chain network design model has been developed considering both forward and reverse flows through the supply chain. Total Cost, environmental factors such as CO2 emission, and social factors such as employment and fairness in providing job opportunities are considered in three separate objective functions. The model seeks to optimize the facility location problem along with determining network flows, type of technology, and capacity of manufacturers. Since the customer’s demand is tainted with high degree of uncertainty, a robust optimization approach is proposed to deal with this important issue. An efficient genetic algorithm is applied to determine the Pareto optimal solutions. Finally, a case study is conducted on steel industry to evaluate the efficiency of the developed model and solution algorithm.
http://www.jise.ir/article_8743_784608f2723577554e85f3ed4708962a.pdf
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58
Supply chain
reverse logistic
social responsibility
robust optimization
multi-objective genetic algorithm
Hamid
Saffari
hamid_saffari@ind.iust.ac.ir
true
1
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Ahmad
Makui
amakui@iust.ac.ir
true
2
Iran University of Science and Technology
Iran University of Science and Technology
Iran University of Science and Technology
AUTHOR
Vahid
Mahmoodian
vahid_mahmoodian@ind.iust.ac.ir
true
3
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Mir Saman
Pishvaee
pishvaee@iust.ac.ir
true
4
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Amin, S.H. & Zhang, G. (2013), A multi-objective facility location model for closed-loop supply chain network
1
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2
Carter, C.R., Jennings, M.M. (2002), Social responsibility and supply chain relationships. Transportation
3
Research Part E: Logistics and Transportation Review, 38; 37-52.
4
Cruz, J.M. (2009), The impact of corporate social responsibility in supply chain management: Multicriteria
5
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6
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networks, transaction costs, emissions, and risk. International Journal of Production Economics, 116(1); 61-74.
8
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9
quality-based product-mix planning. International Journal of Production Economics, 135(1); 209-221.
10
De Rosa, V., Gebhard, M., Hartmann, E. & Wollenweber, J. (2013), Robust sustainable bi-directional logistics
11
network design under uncertainty. International Journal of Production Economics, 145(1);184-198.
12
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002), A fast and elitist multiobjective genetic algorithm:
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NSGA-II. Evolutionary Computation, IEEE Transactions on, 6(2); 182-197.
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16
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genetic algorithm. Resources, Conservation and Recycling, 53(10); 559-570.
18
Easwaran, G. & Üster, H. (2010), A closed-loop supply chain network design problem with integrated forward
19
and reverse channel decisions. IIE Transactions, 42(11); 779-792.
20
El-Sayed, M., Afia, N. & El-Kharbotly, A. (2010), A stochastic model for forward-reverse logistics network
21
design under risk. Computers & Industrial Engineering, 58(3); 423-431.
22
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recovery on logistics network design. Production and operations management, 10(2); 156-173.
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Horn, J., N. Nafpliotis, and D.E. Goldberg. A niched Pareto genetic algorithm for multiobjective optimization.
29
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Responsibility International Organization for Standardization, Geneva.
33
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34
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36
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37
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flowshop scheduling. Computers & Industrial Engineering, 30(4); 957-968.
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45
design. International Journal of Production Research, 50(8); 2218-2233.
46
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47
environment. Journal of Cleaner Production, 41(0); 114-125.
48
Pishvaee, M.S., Rabbani, M. & Torabi, S.A. (2011), A robust optimization approach to closed-loop supply chain
49
network design under uncertainty. Applied Mathematical Modelling, 35(2); 637-649.
50
Pishvaee, M.S. & Razmi, J. (2012), Environmental supply chain network design using multi-objective fuzzy
51
mathematical programming. Applied Mathematical Modelling, 36(8); 3433-3446.
52
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53
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54
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55
forward/reverse logistics network design. Computers & Operations Research, 37(6); 1100-1112.
56
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57
logistics network design. Journal of Manufacturing Systems, 28(4); 107-114.
58
Pishvaee, M.S., Razmi, J. & Torabi, S.A. (2012), Robust possibilistic programming for socially responsible
59
supply chain network design: A new approach. Fuzzy Sets and Systems, 206(0); 1-20.
60
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61
forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modeling,
62
37(1-2); 328-344.
63
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64
chain network under an uncertain environment. The International Journal of Advanced Manufacturing
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Technology, 66(5-8); 825-843.
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Salema, M.I.G., Póvoa, A.P.B. & Novais, A.Q. (2005), Design and planning of supply chains with reverse
67
flows. Computer Aided Chemical Engineering, 20; 1075-1080.
68
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85, pp. 593-595): Citeseer.
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Genetic Algorithm. Genetic and Evolutionary Computation - GECCO, 31; 1214-1225.
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Evolutionary computation,. 2(3); 221-248.
77
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78
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79
Subramanian, P., Ramkumar, N., Narendran, T.T. & Ganesh, K. (2013), PRISM: PRIority based SiMulated
80
annealing for a closed loop supply chain network design problem. Applied Soft Computing, 13(2); 1121-1135.
81
Vahdani, B., Tavakkoli-Moghaddam, R. & Jolai, F. (2013), Reliable design of a logistics network under
82
uncertainty: A fuzzy possibilistic-queuing model. Applied Mathematical Modeling, 37(5); 3254-3268.
83
Wang, H.-F. & Hsu, H.-W. (2010), A closed-loop logistic model with a spanning-tree based genetic algorithm.
84
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85
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86
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strength Pareto approach. Evolutionary Computation, IEEE Transactions on, 3(4); 257-271.
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93
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94
Kommunikationsnetze (TIK).
95
ORIGINAL_ARTICLE
Applying a decision support system for accident analysis by using data mining approach: A case study on one of the Iranian manufactures
Uncertain and stochastic states have been always taken into consideration in the fields of risk management and accident, like other fields of industrial engineering, and have made decision making difficult and complicated for managers in corrective action selection and control measure approach. In this research, huge data sets of the accidents of a manufacturing and industrial unit have been studied by applying clustering methods and association rules as data mining methods. First, the accident data was briefly studied. Then, effective features in an accident were selected while consulting with industry experts and considering production process information. By performing clustering method, data was divided into separate clusters and by using Dunn Index as validator of clustering, optimum number of clusters has been determined. In the next stage, by using the Apriori Algorithm as one of association rule methods, the relations between these fields were identified and the association rules among them were extracted and analyzed. Since managers need precise information for decision making, data mining methods, when to be used properly, may act as a supporting system.
http://www.jise.ir/article_9798_0732d925620aac4b17b36b4423a49e04.pdf
2015-07-01T11:23:20
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59
76
Accident
data mining, association rules, K-means algorithm, a priori Algorithm
Rouzbeh
Ghousi
ghousi@iust.ac.ir
true
1
Iran University of Science and Technology, Industrial Engineering Dept.
Iran University of Science and Technology, Industrial Engineering Dept.
Iran University of Science and Technology, Industrial Engineering Dept.
LEAD_AUTHOR
Ale, B.J.M., Baksteen, H., Bellamy, L.J., Bloemhof, A., Goossens, L.G., Hale, A., Mud, M.L., OH, J.I.H., Papaszoglou, I.A., Rost, J., Whiston, J.Y., 2008, Quantifying occupational risk: The development of an occupational risk model, Safety Science, 46, 176-185.
1
Aneziris, O.N., Papazoglou, I.A., Mud, M.L., Damen, M., Kuiper, J., Baksteen, H., Ale, B.J., Bellamy, L.J., Hale, A.R., Bloemhoff, A., Post, J.G., Oh, J. 2008. Towards risk assessment for crane activities, Safety science, 46,872-884.
2
Aneziris, O.N., Papazoglou, I.A., Mud, M.L., Baksteen, M.H., Ale, B.J., Bellamy, L.J., Hale, A.R., Bloemhoff, A., Post, J.G., Oh, J., 2008. Qualified risk assessment for fall from height, Safety science, 46,198-220.
3
Buldu, A., Mucgun, K., 2010, Data mining application on student's data, Procedia social & Behavioral sciences, 2, 5251-5259.
4
Canadian center for Occupational Health and Safety (CCOHS,), 2006, Accident Investigation, 1-10.International Labor Organization (ILO), 2005.
5
Cheng, C.W., Lin, C.C., Leu, S.S., 2010, Use of association rules to explore cause–effect relationship in occupational accidents in the Taiwan construction industry, Safety Science, 48, 436-444.
6
Cheng, C.W., Leu, S.S., Cheng, Y.M., Wu, T.C., Lin, C.C., 2012, Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan’s construction industry, Accident analysis and prevention, 48, 214-222.
7
Cheng, C.W., Yao, H.Q., Wu, T.C., 2013, Applying data mining techniques to analyze the causes of major occupational accidents in the petrochemical industry, Loss Prevention in the Process Industries, 26, 1269-1278.
8
Cheng, Y., Yu, W.d., Li, Qiming., 2015, GA‐based multi-level association rule mining approach for defect analysis in the construction industry, Automation in Construction, 51, 78-91.
9
Emre gurcanli, G., Mungen, U., 2009. An occupational safety risk analysis method at construction site using fuzzy sets, Industrial Ergonomics, 39, 371-387.
10
Gunter, S., Bunke, H.T., 2003, Validation indices for graph clustering, Pattern Recognition, 24, 1107-1113.
11
Grassi,A., Gamberini, R., Mora, C., Rimini, b., 2009. A fuzzy multi-attribute model for risk evaluation in workplaces, safety science, 47, 707-716.
12
Gurrutxaga, I., Muguerza, J., Arbelaitz, O., Perez, J.M., Martin, J.I., 2011, Towards a standard methodology to evaluate internal cluster validity indices, Pattern Recognition, 32, 505-515.
13
Han, J., Data Mining: Concept and Techniques, second Edition, 2006.
14
Liao, C.W., Perng, Y.H., 2008, Data mining for occupational injuries in the Taiwan construction industry, Safety Science, 46, 1091-1102.
15
Liang, Y.H., 2010, Integration of Data mining techniques to analysis customer value for the automotive maintenance industry, Expert systems with Applications, 37, 7489- 7496.
16
Lluís, S., Josep M. R., Carla, V., 2015, Study of Spanish mining accidents using data mining techniques, Safety Science, 75, 49-55.
17
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18
Mirabadi, A., Sharifian, S., 2010, Application of Association rules in Iranian Railways (RAI) accident data analysis, Safety Science, 1427-1435.
19
Nait-Said, R., Zidani, F., Ouzraoui, N., 2009. Modified risk graph method using fuzzy rule – based approach, Hazardous Materials, 64 651-658.
20
Ngai, E.W.T., Xiu, L., Chau, D.C., 2009, Application of Data mining techniques in customer relation management: A literature review and classification, Expert systems with applications, 36, 2592-2602.
21
Nenonen, N., 2013, Analyzing factors related to slipping, stumbling, and falling accidents at work: Application of data mining methods to Finnish occupational accidents and Diseases statistics database, Applied Ergonomics, 44, 215-224.
22
Pakhira, M.K., Bandyopadhyay, S., Maulik, U., 2004, Validity index for crisp and fuzzy clusters, Pattern Recognition, 37,487-501.
23
Reniers, G.L.L., Dollaret, D., Ale, B.J.M., Soudan, K., 2005, Developing an external domino accident prevention framework: HAZWIM, Journal of Loss Prevention, Loss Prevention in the process industries, 18, 127-138.
24
Rad, A., Naderi, B., Soltani,. M., 2011.,Clustering and ranking university majors using data mining and AHP algorithms: A case study in Iran, Expert systems with Application, 38, 755-763.
25
Rivas, T., Paz, M., Martın, J.E., Matıas, J.M., Garcıa, J.F., Taboada, J., 2011., Explaining and predicting workplace accidents using data-mining techniques, Reliability Engineering and System Safety, 96,739–747.
26
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27
Sari, M., Sevtap Selcuk, A., Karpuz, C., Sebnem, H., Duzgun, B., 2009,Stochastic modeling of accident risks associated with an underground coal mine in Turkey, Safety Science, 4, 778 – 87.
28
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29
Verma,A., Das Khan, S., Maiti, J.,rishna, O.B., Identifying patterns of safety related incidents in a steel plant using association rule mining of incident investigation reports, Safety Science, 70, 89-98.
30
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31
Zheng, X., Liu, M., 2009, An overview of accident forecasting methodologies, Loss Prevention in the process Industries, 22,484-491.
32
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33
ORIGINAL_ARTICLE
A Comparison of Regression and Neural Network Based for Multiple Response Optimization in a Real Case Study of Gasoline Production Process
Most of existing researches for multi response optimization are based on regression analysis. However, the artificial neural network can be applied for the problem. In this paper, two approaches are proposed by consideration of both methods. In the first approach, regression model of the controllable factors and S/N ratio of each response has been achieved, then a fuzzy programming has been applied to find the optimal factors' levels. In the second approach, a tuned Artificial Neural Network (ANN) is used to relate controllable factors and overall exponential desirability function then Genetic Algorithm(GA) is used to find factors optimum value. Mentioned approaches have been discussed in a real case study of oil refining industry. Experimental results for the suggested levels confirm efficiency of the both proposed methods; however, the Neural Network based approach shows more suitability in our case study.
http://www.jise.ir/article_9801_21b0b0050cc76ca9985dc1c2593eb9ed.pdf
2015-07-01T11:23:20
2020-01-21T11:23:20
77
94
multi-response optimization
Taguchi method
Artificial Neural Network
Genetic algorithm
fuzzy programming
M
Bashiri
true
1
Department of Industrial Engineering, Shahed University, Tehran, Iran.
Department of Industrial Engineering, Shahed University, Tehran, Iran.
Department of Industrial Engineering, Shahed University, Tehran, Iran.
LEAD_AUTHOR
H
Rezaei
true
2
Department of Industrial Engineering, Shahed University, Tehran, Iran.
Department of Industrial Engineering, Shahed University, Tehran, Iran.
Department of Industrial Engineering, Shahed University, Tehran, Iran.
AUTHOR
A
Geranmayeh
true
3
Department of Industrial Engineering, College of Engineering, University of Tehran, Iran.
Department of Industrial Engineering, College of Engineering, University of Tehran, Iran.
Department of Industrial Engineering, College of Engineering, University of Tehran, Iran.
AUTHOR
F
Ghobadi
true
4
Department of Chemical Engineering, Isfahan University of Technology, Isfahan ,Iran.
Department of Chemical Engineering, Isfahan University of Technology, Isfahan ,Iran.
Department of Chemical Engineering, Isfahan University of Technology, Isfahan ,Iran.
AUTHOR
Ahn C. (2006), Advances in evolutionary algorithms: theory design and practice; Springer Verlag.
1
Al-Refaie A. (2009), Optimizing SMT performance using comparisons of Efficiency between different systems technique in DEA; IEEE Transactions on Electronics Packaging Manufacturing 32, 256-264.
2
Al-Refaie A., Al-Durgham L. & Bata N. (2009), Optimal Parameter Design by Regression Technique and Grey Relational Analysis; Proceedings of World Congress on Engineering, London, U.K.
3
Al-Refaie A. (2010), Grey-DEA approach for solving the multi-response problem in Taguchi method; Proceeding of the Institution of Mechanical Engineering-Part B, Journal of Engineering Manufacture 224, 147-158.
4
Ahn C. (2006), Advances in evolutionary algorithms: theory design and practice; Springer Verlag.
5
Al-Refaie A. (2009), Optimizing SMT performance using comparisons of Efficiency between different systems technique in DEA; IEEE Transactions on Electronics Packaging Manufacturing 32, 256-264.
6
Al-Refaie A., Al-Durgham L. & Bata N. (2009), Optimal Parameter Design by Regression Technique and Grey Relational Analysis; Proceedings of World Congress on Engineering, London, U.K.
7
Al-Refaie A. (2010), Grey-DEA approach for solving the multi-response problem in Taguchi method; Proceeding of the Institution of Mechanical Engineering-Part B, Journal of Engineering Manufacture 224, 147-158.
8
Bashiri M. & Farshbaf Geranmayeh A. (2011), Tuning the parameters of an artificial neural network using Central composite design and genetic algorithm; Scientia Iranica 18, 1600-1608.
9
Beigmoradi S., Hajabdollahi H. & Ramezani A.(2014), Multi-objective aero acoustic optimization of rear end in a simplified car model by using hybrid Robust Parameter Design, Artificial Neural Networks and Genetic Algorithm methods, Journal of Computers & Fluids 90,123-132.
10
Chang H. (2008), A data mining approach to dynamic multiple responses in Taguchi experimental design; Expert Systems with Applications 35, 1095-1103.
11
Chang H. & Chen Y. (2011), Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments; Applied Soft Computing 11, 436-442.
12
Cheng Chi. Bin., Cheng C. J. & Lee E. S. (2002), Neuro-Fuzzy and Genetic Algorithm in Multiple response optimization; Computers and Mathematics with Applications 44, 1503-1514.
13
Del Castillo E., Montgomery D. C. & McCarville D. (1996), Modified desirability functions for multiple response optimization; Journal of Quality Technology 28, 337-345.
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Derringer G. & Suich R. (1980), Simultaneous optimization of several response variables; Journal of Quality Technology 12, 214-219.
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Derringer G. (1994), A balancing act: optimizing a product's properties; Quality Progress 27, 51-60.
16
Desai K., Saudagar, S.P., Lele, S. & Singhal, R. (2008), Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan; Biochemical Engineering Journal 41, 266-273.
17
Erzurumlu T. & Oktem, H. (2007), Comparison of response surface model with neural network in determining the surface quality of moulded parts; Materials and design 28, 459-465.
18
Gauri S. K., & Chakraborty, S. (2010), A study on the performance of some multi-response optimization methods for WEDM processes; The International Journal of Advanced Manufacturing Technology 49, 155-166.
19
Gauri S. K., & Pal S. (2010), Comparison of performances of five prospective approaches for the multi-response optimization; The International Journal of Advanced Manufacturing Technology 48, 1205-1220.
20
Gutierrez E. & Lozano S. (2010), Data Envelopment Analysis of multiple response experiments; Applied Mathematical Modelling 34, 1139-1148.
21
Harrington Jr. E. (1965), The Desirability Function; Industrial Quality Control 21, 494-498.
22
Jeong I. & Kim K. (2009), An interactive desirability function method to multiresponse optimization; European Journal of Operational Research 195, 412-426.
23
Jeypaul R., Shahabudin P. & Krishnaiah K. (2006), Simultaneous optimization of multi response problems in the Taguchi method using genetic algorithm; The International Journal of Advanced Manufacturing Technology 30, 870-878.
24
Kim K.J., Byun J.H., Min D. I.J. Jeong. (2001), Multiresponse surface optimization: concept, methods, and future directions, Tutorial; Korea Society for Quality Management.
25
Koyee R. D., Eisseler R. & Schmauder S. (2014), Application of Taguchi coupled Fuzzy Multi Attribute Decision Making (FMADM) for optimizing surface quality in turning austenitic and duplex stainless steels; Journal of Measurement 58, 375-386.
26
Kuo C. F. J., Tu K. H. M., Liang S. W. & Tsai W. L. (2010), Optimization of microcrystalline silicon thin film solar cell isolation processing parameters using ultraviolet laser; Optics & Laser Technology 42, 945-955.
27
Lan T. S. (2009), Taguchi optimization of multi-objective CNC Machining using TOPSIS; Information Technology Journal 8, 917-922.
28
Lin, H. C., Su, C. T., Wang, C. C., Chang, B. H., & Juang, R. C. (2012), Parameter optimization of continuous sputtering process based on Taguchi methods, neural networks, desirability function, and genetic algorithms; Expert Systems with Applications 39(17), 12918-12925.
29
Lin J. L. & Tarng Y. S. (1998), Optimization of the multi-response process by the Taguchi method with grey relational analysis; The Journal of Grey System 10, 355-370.
30
Li M. H. C., Al-Refaie A. & Yang C. Y. (2009), DMAIC Approach to Improve the Capability of SMT solder Printing Process; IEEE Transactions on Electronics Packaging Manufacturing 31, 126-133.
31
Manivannan S., Devi S. P., Arumugam R. & Sudharsan N. M. (2011), Multi-objective optimization of flat plate heat sink using Taguchi-based Grey relational analysis; International Journal of Advanced Manufacturing Technology 52, 739-749.
32
Montgomery D. C. (2009), Design and Analysis of Experiments; John Wiley.
33
Namvar-Asl M., Soltanieh M. A. & Rashidi A. (2008), Irandoukht, Modeling and preparation of activated carbon for methane storage I. Modeling of activated carbon characteristics with neural networks and response surface method; Energy Conversion and Management 49, 2471-2477.
34
Tsao C. (2008), Comparison between response surface methodology and radial basis function network for core-center drill in drilling composite materials; The International Journal of Advanced Manufacturing Technology 37, 1061-1068.
35
Yum B. & Kim Y. (2004), Robust design of multilayer feedforward neural networks: an experimental approach; Engineering Applications of Artificial Intelligence 17, 249-263.
36
Zimmerman H. J. (1978), Fuzzy programming and linear programming with several objective functions; Fuzzy sets and systems 1, 45-55.
37
ORIGINAL_ARTICLE
Optimization of multi-product, multi-period closed loop supply chain under uncertainty in product return rate: case study in Kalleh dairy company
Closed Loop production systems attempt to economic improvement, deliver goods to customers with the best quality, decrease in the return rate of expired material and decrease environmental pollution and energy usage. In this study, we solve a multi-product, multi-period closed loop supply chain network in Kalleh dairy company, considering the return rate under uncertainty. The objective of this paper is to develop a supply chain model including raw material suppliers, manufacturers, distributors and a recycle center for returned products. Solving this model helps us to make a good decision about providing materials, production, distribution and recovery. Our basic goal is to estimate optimum return rate of some products such as yoghurt, to production cycle. Once the products pass of their shelf life, they are returned to production cycle. For this study, we develop a linear programming model with a consideration of chance constraints. Finally, this model is implemented by Lingo software with using real data. The obtained results by our model show 9.5 % decrease for total cost in comparison with the current status.
http://www.jise.ir/article_10151_c9fc615ec6bea24f243390ef78948028.pdf
2015-07-01T11:23:20
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95
114
Closed loop supply chain
optimization
Multi-Product
Multi-Period
Rouhollah
Karimi
r.karimi@kalleh-pole.com
true
1
School of Industrial Engineering, Islamic Azad University-South Tehran Branch, Tehran, Iran
School of Industrial Engineering, Islamic Azad University-South Tehran Branch, Tehran, Iran
School of Industrial Engineering, Islamic Azad University-South Tehran Branch, Tehran, Iran
AUTHOR
Vahid
Ghezavati
v_ghezavati@azad.ac.ir
true
2
School of Industrial Engineering, Islamic Azad University-South Tehran Branch, Tehran, Iran
School of Industrial Engineering, Islamic Azad University-South Tehran Branch, Tehran, Iran
School of Industrial Engineering, Islamic Azad University-South Tehran Branch, Tehran, Iran
LEAD_AUTHOR
Kaveh
Damghani
k.khalili@azad.ac.ir
true
3
School of Industrial Engineering, Islamic Azad University-South Tehran Branch, Tehran, Iran
School of Industrial Engineering, Islamic Azad University-South Tehran Branch, Tehran, Iran
School of Industrial Engineering, Islamic Azad University-South Tehran Branch, Tehran, Iran
AUTHOR
Ahumada, O., & Villalobos, J. R. (2011). A tactical model for planning the production and distribution of fresh produce.Annals of Operations Research, 190(1), 339-358.
1
Akkerman, R., Farahani, P., & Grunow, M. (2010). Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges.Or Spectrum , 32(4), 863-904.
2
Amorim, P., Günther, H. O., & Almada-Lobo, B. (2012). Multi-objective integrated production and distribution planning of perishable products. International Journal of Production Economics, 138(1), 89-101.
3
A.I. Barros, R. Dekker, V. Scholten, A two-level network for recycling sand: a case study,European Journal of Operational Research . 110 (1998) 199–214.
4
Fleischmann, M. (2001). Quantitative models for reverse logistics. Lecture Notes in Economics nd Mathematical Systems , 501.
5
Fleischmann, M., Beullens, P., Bloemhof-Ruwaard, J. M., & Van Wassenhove, L. N. (2001). The impact of product recovery on logistics network design. Production and Operations Management, 10(2), 156–173.
6
Fleischmann M, Jacqueline MB-R, Rommert D, Erwin van der Laan JAEE, van Nunen, Van Wassenhove LN. Quantitative models for reverse logistics: a review. European Journal of Operational Research 1997;16:1–17.
7
Guide, V. D. R., Jr., & Van Wassenhove, L. N. (2009). The evolution of closed loop supply chain research.
8
Operations Research, 57(1), 10–18.
9
Haji, M., Haji, R., Darabi, H., (2007), Price Discount and Stochastic Initial Inventory in the Newsboy Problem,
10
Journal of Industrial and Systems Engineering , 1 (2), Page 130-138
11
Hassanzadeh Amin, S., and Guoqing Zhang;(2012); An integrated model for closed loop supply chain configuration and supplier selection: Multi-objective approach, Expert Systems with Applications 39 (2012) 6782–6791.
12
Ko, H. J., & Evans, G. W. (2007). A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Computers & Operations Research, 34(2), 346–366.
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Jayaraman, V., Guide, V. D. R., Jr., & Srivastava, R. (1999). A closed loop logistics model for remanufacturing. Journal of the Operational Research Society, 50(5), 497–508.
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H.R. Krikke, A. Van Harten, P.C. Schuur, Reverse logistic network re-design for copiers, OR Spektrum 21 (1999) 381–409.
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O. Listes, A generic stochastic model for supply-and-return network design, Computers & Operations Research
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Z. Lu, N. Bostel, A facility location model for logistics systems including reverse flows: the case
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of remanufacturing activities, Computers & Operations Research. 34 (2) (2007) 299–323.
20
Melo, M. T., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management – A review.European Journal of Operational Research, 196(2), 401–412.
21
H. Min, H.J. Ko, B.I. Park, A Lagrangian relaxation heuristic for solving the multi-echelon, multi-commodity, closed loop supply chain network design problem, Int. J. Logist. Syst. Manage. 1 (4) (2005) 382–404.
22
Nagurney, A., Dong, J., (2002). Super networks: Decision-Making for the Information Age.Edward Elgar Publishing, Chelthenham, England .
23
Mir Saman Pishvaee, Masoud Rabbani, Seyed Ali Torabi,A robust optimization approach to closed loop supply chain network design under uncertainty,Applied Mathematical Modelling. 35 (2011) 637–649.
24
Rajkumar, M., Sivakumar, B., and Arivarignan, G., (2011), Continuous Review Perishable Inventory System with One Supplier, One Retailer and Positive Lead Time,Journal of Industrial and Systems Engineering 5 (2), Page 80-106.
25
Rogers, D. S., & Tibben-Lembke, R. S. (1999). Going backwards: Reverse logistics trends and practices. Pittsburgh, PA: RLEC Press.
26
Rong, A., Akkerman, R., & Grunow, M. (2011). An optimization approach for managing fresh food quality throughout the supply chain.International Journal of Production Economics,131(1), 421-429.
27
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28
Shi, J., Zhang, G., & Sha, J. (2011). Optimal production planning for a multi-product closed loop system with uncertain demand and return.Computers and Operations Research, 38(3), 641–650.
29
Tan, B., & Çömden, N. (2012). Agricultural planning of annual plants under demand, maturation,harvest, and yield risk.European Journal of Operational Research, 220(2), 539-549.
30
Yu, M., & Nagurney, A. (2013). Competitive food supply chain networks with application to fresh produce.
31
European Journal of Operational Research, 224(2), 273-282.
32