Processing, Please wait...

  • Home
  • About Us
  • Search:
  • Advanced Search

Growing Science » International Journal of Industrial Engineering Computations » Parameters optimization of fabric finishing system of a textile industry using teaching–learning-based optimization algorithm

Journals

  • IJIEC (726)
  • MSL (2637)
  • DSL (649)
  • CCL (508)
  • USCM (1092)
  • ESM (404)
  • AC (562)
  • JPM (247)
  • IJDS (912)
  • JFS (91)
  • HE (26)
  • SCI (26)

IJIEC Volumes

    • Volume 1 (17)
      • Issue 1 (9)
      • Issue 2 (8)
    • Volume 2 (68)
      • Issue 1 (12)
      • Issue 2 (20)
      • Issue 3 (20)
      • Issue 4 (16)
    • Volume 3 (76)
      • Issue 1 (9)
      • Issue 2 (15)
      • Issue 3 (20)
      • Issue 4 (12)
      • Issue 5 (20)
    • Volume 4 (50)
      • Issue 1 (14)
      • Issue 2 (10)
      • Issue 3 (12)
      • Issue 4 (14)
    • Volume 5 (47)
      • Issue 1 (13)
      • Issue 2 (12)
      • Issue 3 (12)
      • Issue 4 (10)
    • Volume 6 (39)
      • Issue 1 (7)
      • Issue 2 (12)
      • Issue 3 (10)
      • Issue 4 (10)
    • Volume 7 (47)
      • Issue 1 (10)
      • Issue 2 (14)
      • Issue 3 (10)
      • Issue 4 (13)
    • Volume 8 (30)
      • Issue 1 (9)
      • Issue 2 (7)
      • Issue 3 (8)
      • Issue 4 (6)
    • Volume 9 (32)
      • Issue 1 (9)
      • Issue 2 (6)
      • Issue 3 (7)
      • Issue 4 (10)
    • Volume 10 (34)
      • Issue 1 (8)
      • Issue 2 (10)
      • Issue 3 (8)
      • Issue 4 (8)
    • Volume 11 (36)
      • Issue 1 (9)
      • Issue 2 (8)
      • Issue 3 (9)
      • Issue 4 (10)
    • Volume 12 (29)
      • Issue 1 (9)
      • Issue 2 (6)
      • Issue 3 (8)
      • Issue 4 (6)
    • Volume 13 (41)
      • Issue 1 (10)
      • Issue 2 (8)
      • Issue 3 (10)
      • Issue 4 (13)
    • Volume 14 (50)
      • Issue 1 (11)
      • Issue 2 (15)
      • Issue 3 (9)
      • Issue 4 (15)
    • Volume 15 (55)
      • Issue 1 (19)
      • Issue 2 (15)
      • Issue 3 (12)
      • Issue 4 (9)
    • Volume 16 (75)
      • Issue 1 (12)
      • Issue 2 (15)
      • Issue 3 (19)
      • Issue 4 (29)

Keywords

Supply chain management(163)
Jordan(161)
Vietnam(148)
Customer satisfaction(120)
Performance(113)
Supply chain(108)
Service quality(98)
Tehran Stock Exchange(94)
Competitive advantage(93)
SMEs(86)
optimization(84)
Financial performance(83)
Trust(81)
TOPSIS(80)
Job satisfaction(79)
Sustainability(79)
Factor analysis(78)
Social media(78)
Knowledge Management(77)
Genetic Algorithm(76)


» Show all keywords

Authors

Naser Azad(82)
Mohammad Reza Iravani(64)
Zeplin Jiwa Husada Tarigan(60)
Endri Endri(45)
Muhammad Alshurideh(42)
Hotlan Siagian(39)
Jumadil Saputra(36)
Dmaithan Almajali(36)
Muhammad Turki Alshurideh(35)
Barween Al Kurdi(32)
Ahmad Makui(32)
Basrowi Basrowi(31)
Hassan Ghodrati(31)
Mohammad Khodaei Valahzaghard(30)
Shankar Chakraborty(29)
Ni Nyoman Kerti Yasa(29)
Sulieman Ibraheem Shelash Al-Hawary(28)
Prasadja Ricardianto(28)
Sautma Ronni Basana(27)
Haitham M. Alzoubi(27)


» Show all authors

Countries

Iran(2177)
Indonesia(1278)
Jordan(784)
India(782)
Vietnam(500)
Saudi Arabia(440)
Malaysia(438)
United Arab Emirates(220)
China(182)
Thailand(151)
United States(110)
Turkey(103)
Ukraine(102)
Egypt(97)
Canada(92)
Pakistan(84)
Peru(83)
Morocco(79)
United Kingdom(79)
Nigeria(77)


» Show all countries

International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print)
Quarterly Publication
Volume 9 Issue 2 pp. 221-234 , 2018

Parameters optimization of fabric finishing system of a textile industry using teaching–learning-based optimization algorithm Pages 221-234 Right click to download the paper Download PDF

Authors: Rajiv Kumar, P.C. Tewari, Dinesh Khanduja

DOI: 10.5267/j.ijiec.2017.6.002

Keywords: Performance modeling, TLBO, Markov process, Genetic algorithm, Probabilistic Approach

Abstract: In the present work, a recently developed advanced optimization algorithm named as teaching–learning-based optimization (TLBO) is used for the parameters optimization of fabric finishing system of a textile industry. Fabric Finishing System has four main subsystems, arranged in hybrid configuration. For performance modeling and analysis of availability, a performance evaluating model of fabric finishing system has been developed with the help of mathematical formulation based on Markov-Birth-Death process using Probabilistic Approach. Then, the overall performance of the concerned system has first analyzed and then, optimized by using teaching–learning-based optimization (TLBO). The results of optimization using the proposed algorithm are validated by comparing with those obtained by using the genetic algorithm (GA) on the same system. Improvement in the results is obtained by the proposed algorithm. The results of effect of variation of the algorithm parameters on fitness values of the objective function are reported.

How to cite this paper
Kumar, R., Tewari, P & Khanduja, D. (2018). Parameters optimization of fabric finishing system of a textile industry using teaching–learning-based optimization algorithm.International Journal of Industrial Engineering Computations , 9(2), 221-234.

Refrences
Cafaro, G., Corsi, F., & Vacca, F. (1986). Multi state markov models and structural properties of the transition rate matrix. IEEE Transactions on Reliability, 35(2), 192-200.
Çekyay, B., & Özekici, S. (2015). Reliability, MTTF and steady-state availability analysis of systems with exponential lifetimes. Applied Mathematical Modelling, 39(1), 284-296.
Chung, W.K. (1987). Reliability analysis of repairable parallel system with standby involving human error and common-cause failures. Microelectronics Reliability, 27(2), 269-271.
Coit, D.W., & Smith, A.E. (1996). Reliability optimization of series parallel systems using genetic algorithm. IEEE Transactions on Reliability, 45(2), 254-260.
Fu, J.C. (1986). Reliability of large consecutive-K-out-of-N: F systems with k−1 step markov dependence. IEEE Transactions on Reliability, 35(5), 602–606.
Fu, J.C., & Hu, B. (1987). On reliability of large consecutive-K-out-of-N: F systems with k-1 step markov dependence. IEEE Transactions on Reliability, 36(1), 75-77.
Garg, D., Kumar, K., & Meenu (2010). Availability optimization for screw plant based on genetic algorithm. International Journal of Engineering Science and Technology, 2(4), 658-668.
Garg, H., & Sharma, S.P. (2012). Behavior analysis of synthesis unit in fertilizer plant. International Journal of Quality & Reliability Management, 29(2), 217 – 232.
Goyal, A., & Gupta, P. (2012). Performance evaluation of a multi-state repairable production system – a case study. International Journal of Performability Engineering, 8(3), 330-338.
Gupta, P., Lal, A.K., Sharma, R.K., & Singh, J. (2005). Numerical Analysis of reliability and availability of the serial processes in butter-oil processing plant. International Journal of Quality & Reliability Management, 22(3), 303 – 316.
Gupta, S., Tewari, P.C., & Sharma, A.K. (2009). Reliability and Availability analysis of ash handling unit of a steam thermal power plant. South African Journal of Industrial Engineering, 20(1), 147-158.
Gupta, P. (2011). Markov modeling and availability analysis of a chemical production system-a case study. Proceedings of the World Congress on Engineering, Vol. I, WCE -2011, July 6 - 8, 2011, London, U.K.
Khanduja, R., Tewari, P.C., & Chauhan, R.S. (2009). Performance analysis of screening unit in a paper plant using genetic algorithm. Journal of Industrial and Systems Engineering, 3(2), 140-151.
Khanduja, R., Tewari, P.C., Chauhan, R.S. & Kumar, D. (2010). Mathematical modeling and performance optimization for paper making system of a paper plant. Jordan Journal of Mechanical and Industrial Engineering, 4(4), 487-494.
Khanduja, R., Tewari, P. C., & Chauhan, R.S. (2012). Performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm. International Journal of Quality Reliability and Management, 28(6), 688-703.
Khanduja, R., Tewari, P. C. & Gupta, M. (2012). Performance enhancement for crystallization unit of sugar plant using genetic algorithm technique. Journal of Industrial Engineering International, 28(6), 688-703.
Krishnanand, K. R., Panigrahi, B. K., Rout, P. K., & Mohapatra, A. (2011, December). Application of multi-objective teaching-learning-based algorithm to an economic load dispatch problem with incommensurable objectives. In International Conference on Swarm, Evolutionary, and Memetic Computing (pp. 697-705). Springer Berlin Heidelberg.
Kumar, D., Singh, I.P., & Singh, J. (1988). Reliability analysis of the feeding system in the paper industry. Microelectronics Reliability, 28(2), 213-215.
Kumar, D., Singh, J., & Pandey, P.C. (1989). Availability of a washing system in the paper industry. Microelectronics Reliability, 29(5), 775-778.
Kumar, D, Singh, J., & Pandey, P.C. (1990). Cost analysis of a multi-component screening system in the paper industry. Microelectronics Reliability, 30(3), 457-461.
Kumar, D., Singh, J., & Pandey, P.C. (1991). Behavioral analysis of a paper production system with different repair policies. Microelectronics Reliability, 31(1), 47-51.
Kumar, D., Singh, J., & Pandey, P.C. (1992). Availability of the crystallization system in the sugar industry under common-cause failure. IEEE Transactions on Reliability, 41(1), 85-91.
Kumar, R. (2014). Availability analysis of thermal power plant boiler air circulation system using Markov approach. Decision Science Letters, 3(1), 65-72.
Kumar, P., & Tewari, P. (2017). Performance analysis and optimization for CSDGB filling system of a beverage plant using particle swarm optimization. International Journal of Industrial Engineering Computations, 8(3), 303-314.
Lai, C. D., Xie, M., Poh, K. L., Dai, Y. S., & Yang, P. (2002). A model for availability analysis of distributed software/hardware systems. Information and Software Technology, 44(6), 343-350.
Levitin, G., Xing, L., Amari, S. V., & Dai, Y. (2013). Reliability of non-repairable phased-mission systems with propagated failures. Reliability Engineering & System Safety, 119, 218-228.
Modgil V., Sharma, S. K., & Singh, J. (2013). Performance modeling and availability analysis of shoe upper manufacturing unit. Int J Quality Reliability and Management, 30(8), 816-831.
Niknam, T., Fard, A.K. & Baziar, A. (2012). Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants. Energy, 42, 563–573.
Niknam, T., Azizipanah-Abarghooee, R., & Narimani, M. R. (2012). An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation. Applied Energy, 99, 455-470.
Niknam, T., Azizipanah-Abarghooee, R., & Narimani, M. R. (2012). A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems. Engineering Applications of Artificial Intelligence, 25(8), 1577-1588.
Niknam, T., Golestaneh, F., & Sadeghi, M. S. (2012). $\ theta $-Multiobjective Teaching–Learning-Based Optimization for Dynamic Economic Emission Dispatch. IEEE Systems Journal, 6(2), 341-352.
Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.
Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1-15.
Rao, R., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560.
Venkata Rao, R., & Kalyankar, V. D. (2012). Parameter optimization of machining processes using a new optimization algorithm. Materials and Manufacturing Processes, 27(9), 978-985.
Rao, R.V., & Kalyankar, V.D. (2012). Multi-objective multi-parameter optimization of the industrial LBW process using a new optimization algorithm. In Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 226(6), 1018–1025.
Rao, R. V., & Kalyankar, V. D. (2013). Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26(1), 524-531.
Rao, R. V., & Patel, V. (2013). Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Applied Mathematical Modelling, 37(3), 1147-1162.
Rao, R. V., & Patel, V. (2013). Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26(1), 430-445.
Sabouhi, H., Abbaspour, A., Fotuhi-Firuzabad, M., & Dehghanian, P. (2016). Reliability modeling and availability analysis of combined cycle power plants. International Journal of Electrical Power & Energy Systems, 79, 108-119.
Singh, J., & Mahajan, P. (1999). Reliability of utensils manufacturing plant-a case study. Opsearch, 36(3), 260-269.
Tewari, P.C., Kumar, D., & Mehta, N.P. (2003). Decision support system of refining system of sugar plant. Journal of Institution of Engineers (India), 84, 41-44.
Togan, V. (2012). Design of planar steel frames using teaching–learning based optimization. Engineering Structure, 34, 225–232.
Wang, K.H., Yen, T.C., & Fang, Y.C. (2012). Comparison of availability between two systems with warm standby units and different imperfect coverage. Quality Technology and Quantitative Management, 9(3), 256-282.
  • 34
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: International Journal of Industrial Engineering Computations | Year: 2018 | Volume: 9 | Issue: 2 | Views: 2505 | Reviews: 0

Related Articles:
  • Performance analysis and optimization for CSDGB filling system of a beverag ...
  • Availability analysis of thermal power plant boiler air circulation system ...
  • Improved teaching learning based optimization for global function optimizat ...
  • Markov approach to evaluate the availability simulation model for power gen ...
  • Mathematical modelling and performance optimization of CO2 cooling system o ...

Add Reviews

Name:*
E-Mail:
Review:
Bold Italic Underline Strike | Align left Center Align right | Insert smilies Insert link URLInsert protected URL Select color | Add Hidden Text Insert Quote Convert selected text from selection to Cyrillic (Russian) alphabet Insert spoiler
winkwinkedsmileam
belayfeelfellowlaughing
lollovenorecourse
requestsadtonguewassat
cryingwhatbullyangry
Security Code: *
Include security image CAPCHA.
Refresh Code

® 2010-2025 GrowingScience.Com