CFP last date
20 December 2024
Reseach Article

Neuro-Fuzzy based Decision Support System for Electrical Cable Production Planning

by Olumide Obe, Akinyokun Oluyomi, Seriki Oluwadamilola
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 31
Year of Publication: 2021
Authors: Olumide Obe, Akinyokun Oluyomi, Seriki Oluwadamilola
10.5120/ijca2021921255

Olumide Obe, Akinyokun Oluyomi, Seriki Oluwadamilola . Neuro-Fuzzy based Decision Support System for Electrical Cable Production Planning. International Journal of Computer Applications. 174, 31 ( Apr 2021), 31-40. DOI=10.5120/ijca2021921255

@article{ 10.5120/ijca2021921255,
author = { Olumide Obe, Akinyokun Oluyomi, Seriki Oluwadamilola },
title = { Neuro-Fuzzy based Decision Support System for Electrical Cable Production Planning },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2021 },
volume = { 174 },
number = { 31 },
month = { Apr },
year = { 2021 },
issn = { 0975-8887 },
pages = { 31-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number31/31879-2021921255/ },
doi = { 10.5120/ijca2021921255 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:23:37.630320+05:30
%A Olumide Obe
%A Akinyokun Oluyomi
%A Seriki Oluwadamilola
%T Neuro-Fuzzy based Decision Support System for Electrical Cable Production Planning
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 31
%P 31-40
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Overtime the manufacturing industry and information technology (IT) has become intertwined as the electric cable manufacturing bears heavy expectation on IT to achieve its desired goals and commercial competitive advantage through effective and efficient production planning process. Production planning and overcoming its ensuing challenges such as high precision in predicting and meeting demand in a continuously non-stable business environment has become an evolving research area in the field of management sciences. This research therefore offers a neuro-fuzzy decision support system (DSS) for electrical cable production planning. The system consists of database of cable information and adaptive neuro-fuzzy inference system (ANFIS) module. The functionality of this system is tested and validated using preprocessed record of customer orders of Coleman Technical Industries Limited Nigeria and evaluated with standard statistical procedure. The outcome of evaluation proved the proposed system to be 90.06% accurate in predicting the production plan.

References
  1. Bansal, S., Vashiath, Y. and Batra, U. (2015), Production Planning. Journal of Computer Science and Engineering. 1 (5), 53 – 57.
  2. Galina, S. and Sławomir, P. (2009), Intelligent Manufacturing Systems, International Book Series "Information Science and Computing", 142 – 149.
  3. Cirovic, I., Simeonov, S., Stano, P., and Pfaff, O., (2011), Approaches for Solving Production Planning and Scheduling Problems Using Genetic Algorithms. International DAAAM Symposium, 22(1), 0771 – 0772.
  4. Suparta, W., and Alhasa K., (2016), Modeling of Tropospheric Delays Using ANFIS. SpringerBriefs in Meteorology, DOI 10.1007/978-3-319-28437-8_2, 5 – 18.
  5. Thipparat, T., (2012), Implementations/Application of Adaptive Neuro-Fuzzy Inference System in Supply Chain Management Evaluation. Application of Adaptive Neuro Fuzzy, Fuzzy Logic - Algorithms, Techniques and Implementations, 115 – 126.
  6. Efendigil, T., Onut, S. and Kahraman, C. (2008), A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models. Expert Systems with Applications, 1 – 11.
  7. Abdel-Aleem, A., El-Sharief, M., Hassan, M., and El-Sebaie M. (2017), Implementation of Fuzzy and Adaptive Neuro-Fuzzy Inference Systems in Optimization of Production Inventory Problem. Applied Mathematics and Information Sciences Journal 2 (1), 289 – 29.
  8. Akindipe, O. (2014), The Role of Raw Material Management In Production Operations. International Journal of Managing Value and Supply Chains (IJMVSC) 5 (3), 37 – 44.
  9. Amol, G., Harri H. and Pekka K. (2011), Production Planning Through Customized ERP at a Nordic Manufacturing Company. Journal of Enterprise Information Management, 6 (3), 211 – 223.
  10. Ashwini, A., and Keswani, P., (2014), Scheduling By Using Fuzzy Logic in Manufacturing. Int. Journal of Engineering Research and Applications, 4(7), 104-111.
  11. Azizi, A., Yazid, A., Ali, and Ping, W. (2013), An Adaptive Neuro-fuzzy Inference System for A Dynamic Production Environment Under Uncertainties. World Applied Sciences Journal, 25 (3), 428 – 433.
  12. Boran, F., Genc, S., Kurt, M., and Akay D. (2009), A Multi-Criteria Intuitionistic Fuzzy Group Decision Making for Supplier Selection with Topsis Method. Expert Systems with Applications 36 (1), 11363 – 11368.
  13. Bubeník, P. and Horák, F. (2014), Knowledge-Based Systems to Support Production Planning. Sustavi temeljeni na znanju kao podrška planiranju proizvodnje, 21(3), 505 – 509.
  14. Burduk, A., (2013), Artificial Neural Networks as Tools for Controlling Production Systems and Ensuring Their Stability. International Conference on Information
  15. Abdel-Aleem, A., El-Sharief, M., Hassan, M., and El-Sebaie M. (2017), Implementation of Fuzzy and Adaptive Neuro-Fuzzy Inference Systems in Optimization of Production Inventory Problem. Applied Mathematics and Information Sciences Journal 2 (1), 289 – 29.
  16. Akindipe, O. (2014), The Role Of Raw Material Management In Production Operations. International Journal of Managing Value and Supply Chains (IJMVSC) 5 (3), 37 – 44.
  17. Amol, G., Harri H. and Pekka K. (2011), Production Planning Through Customized ERP at a Nordic Manufacturing Company. Journal of Enterprise Information Management, 6 (3), 211 – 223.
  18. Ashwini, A., and Keswani, P., (2014), Scheduling By Using Fuzzy Logic in Manufacturing. Int. Journal of Engineering Research and Applications, 4(7), 104-111.
  19. Azizi, A., Yazid, A., Ali, and Ping, W. (2013), An Adaptive Neuro-fuzzy Inference System for A Dynamic Production Environment Under Uncertainties. World Applied Sciences Journal, 25 (3), 428 – 433.
  20. Bansal, S., Vashiath, Y. and Batra, U. (2015), Production Planning. Journal of Computer Science and Engineering. 1 (5), 53 – 57.
  21. Boran, F., Genc, S., Kurt, M., and Akay D. (2009), A Multi-Criteria Intuitionistic Fuzzy Group Decision Making for Supplier Selection with Topsis Method. Expert Systems with Applications 36 (1), 11363 – 11368.
  22. Bubeník, P. and Horák, F. (2014), Knowledge-Based Systems to Support Production Planning. Sustavi temeljeni na znanju kao podrška planiranju proizvodnje, 21(3), 505 – 509.
  23. Burduk, A., (2013), Artificial Neural Networks as Tools for Controlling Production Systems and Ensuring Their Stability. International Conference on Information Systems and Industrial Management (CISIM), 12 (45), 487 - 498. ‌
  24. Carmignani, G., Passacantando M and Tumminelli G. (2014), A Novel Method Based on Artificial Neural Network to Production Planning: a case study of a paints producer. 1-14.
  25. Chakrabortty, R., and Hasin, A., (2013), Solving an Aggregate Production Planning Problem By Fuzzy Based Genetic Algorithm (Fbga) Approach. International Journal of Fuzzy Logic Systems (IJFLS), 3(1), 1 – 16.
  26. Eraslan, E. (2009), The Estimation of Product Standard Time by Artificial Neural Networks in the Molding Industry. Mathematical Problems in Engineering, 2009 (527452), 1 - 12.
  27. Erdem, A. and Gocen, E. (2012), a decision support system for supplier evaluation and order allocation. Expert Systems with Applications, 39 (1), 4927 – 4937.
  28. Filip, F., Zamfirescu, B. and Ciurea C. (2017), Computer-Supported Collaborative Decision- Making, Automation, Collaboration, & E-Services, 4, DOI 10.1007/978-3-319-47221-8_2, 31 – 69.
  29. Huang, H. (2009), Designing a Knowledge-Based System For Strategic Planning: A balanced scorecard perspective. Expert Systems with Applications, 36 (1). 209–218.
  30. Marek, J. and Roger R. (2002), Decision Support Systems. Encyclopedia of Library and Information Science, 1 – 15.
  31. Moore, G., Electric Cables Handbook/BICC Cables. Third Edition, 23 – 125.
  32. Németh, P., Ladinig, T., and Ferenczi, B. (2016), Use of Artificial Neural Networks in the Production Control of Small Batch Production. International Conference on Artificial Intelligence, 237 – 240.
  33. Pranolo, A., In’ammurrohman, F., Hendriana, Y., and Octaviani D. (2015), A Decision Support System using ANFIS to Determine the Major of Prospective Students in A Vocational School of Indonesia. International Journal of Computer Trends and Technology (IJCTT,) 27 (2), 100 - 105.
  34. Power, D.J., (2007), A Brief History of Decision Support Systems. http://DSSResources.COM/history/dsshistory.html.
  35. Rakesh, P., Imtiyaz, K., and Ghosh, M., (2014), Linking the Production Planning and Supply Chain Using Fuzzy Logic: An Integrated Model For FMCG Products. International Journal of Engineering Research and Development, 10(10), 33 – 43.
  36. Ramlan, R., and Cheng, A., (2016), The Conceptual Framework of Production Planning Optimisation Using Fuzzy Inference System with Tsukamoto. International Journal of Industrial Management (IJIM) 2(1), 80 - 91.
  37. Rippel, D., Harjes, F., and Scholz-Reiter B., (2010), Modeling a Neural Network Based Control for Autonomous Production Systems. 1- 6.
  38. Sharma, R., and Sinha A., (2012), A Production Planning Model Using Fuzzy Neural Network. International Journal of Computer Applications, 40(4), 19 – 22.
  39. Simeunović, N., Kamenko, I., Bugarski, V., Jovanović, M., and Lalić, B., (2017), Improving Workforce Scheduling Using Artificial Neural Networks Model. Advances in Production Engineering & Management, 12(4), 337 – 352.
Index Terms

Computer Science
Information Sciences

Keywords

Decision Support System Production Planning Electrical Cable Adaptive Neuro-Fuzzy Inference System.