Sat, Dec 21, 2024
[Archive]
.png)
Volume 7, Issue 1 (3-2021)
jhehp 2021, 7(1): 41-53 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Hassanpour M. Evaluation of Iranian Food Manufacturing and Processing Industries. jhehp 2021; 7 (1) :41-53
URL: http://jhehp.zums.ac.ir/article-1-387-en.html
URL: http://jhehp.zums.ac.ir/article-1-387-en.html
Department of Environmental Science, UCS, Osmania University, Telangana State, Hyderabad, India.
Abstract: (6605 Views)
Background: The Iranian Food Manufacturing and Producing Industries (IFMPI) were evaluated by the Iranian evaluator team according to the guidelines of the industrial projects before entering the full-scale implementation step. The current cluster study followed the objective of a similar research about evaluating the initial data of 57 various kinds of IFMPI in terms of energy, materials streams, and technical aspects.
Methods: The present research took into consideration the assessment of initial data in the screening step of project identification to further processing via Multi-Criteria Decision Making (MCDM) models and developing a techno-economic assessment model empirically.
Results: A good reliability ( a = 0.808) among the weight values in MCDM models confirmed a developed classification. The proposed economic assessment model is able to estimate the outlays for the screening step of IFMPI projects.
Conclusion: It can be concluded that the present study executed IFMPI in input and output materials besides energy streams and placed them in the reliable ranking levels using MCDM models along with Data Envelopment Analysis (DEA).
Methods: The present research took into consideration the assessment of initial data in the screening step of project identification to further processing via Multi-Criteria Decision Making (MCDM) models and developing a techno-economic assessment model empirically.
Results: A good reliability ( a = 0.808) among the weight values in MCDM models confirmed a developed classification. The proposed economic assessment model is able to estimate the outlays for the screening step of IFMPI projects.
Conclusion: It can be concluded that the present study executed IFMPI in input and output materials besides energy streams and placed them in the reliable ranking levels using MCDM models along with Data Envelopment Analysis (DEA).
Type of Study: Original Article |
Subject:
Food Safety and Hygiene
Received: 2020/12/5 | Accepted: 2021/02/7 | Published: 2021/03/20
Received: 2020/12/5 | Accepted: 2021/02/7 | Published: 2021/03/20
References
1. Molaei M. Investigating and Comparing the Productivity of Various Small and Large Industrial Groups in Iran. Iran: Second National Conference on Industrial Engineering, Yazd University. 2000: 407-18. [Persian].
2. Ghanatabadi F. Internationalization of Small and Medium-Sized Enterprises in Iran. Ph. D Thesis, Lulea University of Technology Department of Business Administration and Social Sciences Division of Industrial Marketing and e-Commerce. 2005; 1-350.
3. Hassanpour M. Hierarchical Cluster Classification of Iranian Food Manufacturing and Processing Industries. J Food Safe Hyg. 2018; 4(1-2): 27-40. [Google Scholar]
4. Karpushkin E. Rheology, Principles, Application and Environmental Impacts. 1th Edition. USA, New York; Nova Publisher: 2015.
5. Mishra J. Effects of Bran Treatment on Rheology and Sensory Quality of Whole Wheat Flat Bread. A master Thesis South Dakota State University. 2016; 1-100. [Google Scholar]
6. Patel AD. Stability, Rheology and the Effect of Various Environmental Factors on the Formation of Food Protein-Stabilized Nanogels from Nanoemulsions. A Thesis Submitted to the College of Graduate and Postdoctoral Studies in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Department of Food and Bioproduct Sciences University of Saskatchewan Saskatoon, SK. 2017; 1-133. [Google Scholar]
7. Peressini D, Braunstein D, Page JH, Strybulevych A, Lagazioc C, Scanlond MG. Relation between Ultrasonic Properties, Rheology and Baking Quality for Bread Dough of Widely Differing Formulation. J Sci Food Agric. 2017; 97: 2366–74. [Crossref] [Google Scholar]
8. Chaudhary S, Sagar S, Kumar M, Sengar RS, Tomar A. The Use of Enzymes in Food Processing: A Review. South Asian J Food Technol Environ. 2015; 1(3&4): 190-210. [Crossref] [Google Scholar]
9. Hassanpour M. Evaluation of Iranian Electronic Products Manufacturing Industries Using an Unsupervised Model, ARAS, SAW and DEA Models. J Industrial Eng Manag Stud. 2019; 6(2):1-24. [Google Scholar]
10. Mukhametzyanov I, Pamucar D. A Sensitivity Analysis in MCDM Problems: A Statistical Approach. Decision Mak: Appl Manag Eng. 2018; 1: 51-80. [Crossref] [Google Scholar]
11. Mehdiabadi A, Rohani A, Amirabdollahiyan S. Ranking Industries Using a Hybrid of DEA-TOPSIS. Decision Sci Lett. 2013; 2: 251-6. [Crossref] [Google Scholar]
12. Lohani BN, Evans JW, Everitt RR, Ludwig H, Carpenter RA, Tu SL.. Environmental Impact Assessment for Developing Countries in Asia. Asian Dev Bank.1997; 1: 1-356.
13. Milosavljević M, Bursać M, Tričković G. Selection of the railroad container terminal in Serbia based on multi criteria decision-making methods. Decision Making: Applicatons in Management and Engineering. 2018; 1(2): 1-15. [Crossref] [Google Scholar]
14. Yazdani M, Chatterjee P, Pamucar D. A Risk-Based Integrated Decision-Making Model for Green Supplier Selection: A Case Study of a Construction Company in Spain. Kybernetes. 2019; 49(4): 1229-52. [Crossref] [Google Scholar]
15. Velez Ruiz JF, Barbosa Cánovas GV, Peleg M. Rheological Properties of Selected Dairy Products. Crit Rev Food Sci Nut.. 1997; 37(4): 311-59. [Crossref] [Google Scholar]
16. Rosentrater KA. Strategic Methodology for Advancing Food Manufacturing Waste Management Paradigms. Int J Environ Conscious Des Manuf. 2006; 13(1): 1-13. [Google Scholar]
17. Li S, Jin H, Gao L, Zhang X, Ji X. Techno-Economic Performance and Cost Reduction Potential for the Substitute/Synthetic Natural Gas and Power Cogeneration Plant with CO2 Capture. Energy Convers Manag. 2014; 85: 875–87. [Crossref] [Google Scholar]
18. Compton M, Willis S, Rezaie B, Humes K. Food Processing Industry Energy and Water Consumption in the Pacific Northwest. Innov Food Sci Emerg Technol. 2018; 47: 371–83. [Crossref] [Google Scholar]
19. Baroutian S, Munir MT, Sun J, Eshtiaghi N, Young BR. Rheological Characterization of Biologically Treated and Non-Treated Putrescible Food Waste. J Waste Manage. 2018; 71: 494-501. [Crossref] [Google Scholar]
20. Meza BE, Peralta JM, Zorrilla SE. Rheological Properties of a Commercial Food Glaze Material and Their Effect on the Film Thickness Obtained by Dip Coating. J Food Process Eng. 2014; 35(5): 1-8. [Crossref] [Google Scholar]
21. Hassanpour, M. Efficiency Score Assessment of Iranian Automotive and Food Industries. Int.J. Data Envelopment Analysis. 2019; 7(2): 65-82. [Crossref] [Google Scholar]
22. Ecer F, Pamucar D, Zolfani SH, Eshkalag MK. Sustainability Assessment of OPEC Countries: Application of a Multiple Attribute Decision Making Tool. J Clean Prod. 2019; 241:118324. [Crossref] [Google Scholar]
23. Pamucar D, Petrovic I, Cirovic G. Modification of the Best–Worst and MABAC Methods: A Novel Approach Based on Interval-Valued Fuzzy-Rough Numbers. Expert Syst Appl. 2018; 91: 89–106. [Crossref] [Google Scholar]
24. Debnath A, Roy J, Kar S, Zavadskas EK. Antucheviciene J. A Hybrid MCDM Approach for Strategic Project Portfolio Selection of Agro By-Products. Sustain. 2017; 9: 1302. [Crossref] [Google Scholar]
25. Bozanic D, Tesic D, Kocic J. Multi-Criteria Fucom –Fuzzy MABAC Model for the Selection of Location for Construction of Single-Span Bailey Bridge. Decision Mak: Appl Manag Eng. 2019; 2(1): 132-46. [Crossref] [Google Scholar]
26. Sutthichaimethee P, Tanoamchard W. Carrying Capacity Model of Food Manufacturing Sectors for Sustainable Development from Using Environmental and Natural Resources of Thailand. J Ecol Eng. 2015; 16(5): 1–8. [Crossref] [Google Scholar]
27. Mantihal S, Prakash S, Godoi FC, Bhandar B. Optimization of Chocolate 3D Printing by Correlating Thermal and Flow Properties with 3D Structure Modeling. Innov Food Sci Emerg Technol. 2017; 44: 21–9. [Crossref] [Google Scholar]
28. Delgado KF, Da Silva Frasao B, Da Costa MP, Junior CAC. Different Alternatives to Improve Rheological and Textural Characteristics of Fermented Goat Products - A Review. Rheol: Open Access. 2017; 1(1): 2-6. [Google Scholar]
29. Dupuy C, Botta Genoulaz V, Guinet A. Batch Dispersion Model to Optimize Traceability in Food Industry. J Food Eng. 2005; 70: 333–9. [Crossref] [Google Scholar]
30. Ahmed EM. Malaysia’s Food Manufacturing Industries Productivity Determinants. Modern Economy. 2012; 3: 444-53. [Crossref] [Google Scholar]
31. Huang KS. Food Manufacturing Productivity and Its Economic Implications. Electronic Report from the Economic Research Service. United States Department of Agriculture. 2003; 1-56.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
© 2024 The Author(s)
