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Fuzzy approach for production planning by using a three-dimensional printing-based ubiquitous manufacturing system

Published online by Cambridge University Press:  15 August 2019

Tin-Chih Toly Chen*
Affiliation:
Department of Industrial Engineering and Management, National Chiao Tung University, 1001, University Rd., Hsinchu, Taiwan
*
Author for correspondence: Tin-Chih Toly Chen, E-mail: tolychen@ms37.hinet.net

Abstract

A ubiquitous manufacturing (UM) system is used in manufacturing for obtaining the Internet of things solutions and provides location-based manufacturing services. Human-induced uncertainty and early termination are two complications that hamper the effectiveness of an UM system based on three-dimensional (3D) printing. To resolve these complications, several solutions were considered in this study. First, fuzzy-valued parameters were defined to determine uncertainty. Subsequently, slack was derived to determine whether to restart an early terminated 3D printing process in the same 3D printing facility. Consequently, two optimization models – a fuzzy mixed-integer linear programming model and a fuzzy mixed-integer quadratic programming model – were developed in this study. Based on the two optimization models, a fuzzy 3D printing-based UM system that considers uncertainty and early termination was developed. The effectiveness of the proposed methodology was tested by conducting a regional experiment. The experimental results revealed that the proposed methodology could shorten the average cycle time by 9% and could enable 3D printing facilities to make real-time, online reprinting decisions.

Type
Practicum Paper
Copyright
Copyright © Cambridge University Press 2019

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References

Bao, J, Zheng, X, Zhang, J, Ji, X and Zhang, J (2018) Data-driven process planning for shipbuilding. AI EDAM 32, 122130.Google Scholar
Bhattacharya, A, Mohapatra, P, Kumar, V, Dey, PK, Brady, M, Tiwari, MK and Nudurupati, SS (2014) Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach. Production Planning & Control 25, 698714.CrossRefGoogle Scholar
Chen, T (2003) A fuzzy mid-term single-fab production planning model. Journal of Intelligent Manufacturing 14, 273285.CrossRefGoogle Scholar
Chen, T (2014) Strengthening the competitiveness and sustainability of a semiconductor manufacturer with cloud manufacturing. Sustainability 6, 251268.CrossRefGoogle Scholar
Chen, T and Lin, Y-C (2017) Feasibility evaluation and optimization of a smart manufacturing system based on 3D printing. International Journal of Intelligent Systems 32, 394413.CrossRefGoogle Scholar
Chen, T and Lin, C-W (2018) INLP-BPN approach for recommending hotels to a mobile traveler. Journal of Ambient Intelligence and Humanized Computing 9, 329336.CrossRefGoogle Scholar
Chen, T and Tsai, H-R (2017) Ubiquitous manufacturing: current practices, challenges, and opportunities. Robotics and Computer-Integrated Manufacturing 45, 126132.CrossRefGoogle Scholar
da Silva, AF and Marins, FAS (2014) A fuzzy goal programming model for solving aggregate production-planning problems under uncertainty: a case study in a Brazilian sugar mill. Energy Economics 45, 196204.CrossRefGoogle Scholar
Fang, J, Huang, GQ and Li, Z (2013) Event-driven multi-agent ubiquitous manufacturing execution platform for shop floor work-in-progress management. International Journal of Production Research 51, 11681185.CrossRefGoogle Scholar
Gholamian, N, Mahdavi, I, Tavakkoli-Moghaddam, R and Mahdavi-Amiri, N (2015) Comprehensive fuzzy multi-objective multi-product multi-site aggregate production planning decisions in a supply chain under uncertainty. Applied Soft Computing 37, 585607.CrossRefGoogle Scholar
Giralt, J, Moreno-Garcia, J, Jimenez-Linares, L and Rodriguez-Benitez, L. (2017) A road departure warning system based on video motion analysis and fuzzy logic. International Journal of Intelligent Systems 32, 830842.CrossRefGoogle Scholar
Grieser, F (2015) 3D Printing Quality Issues: 10 Tricks to Avoid them. Available at: https://all3dp.com/3d-printing-quality/.Google Scholar
Hsu, HM and Wang, WP (2001) Possibilistic programming in production planning of assemble-to-order environments. Fuzzy Sets and Systems 119, 5970.CrossRefGoogle Scholar
Jiménez, F, Sánchez, G and Vasant, P (2013) A multi-objective evolutionary approach for fuzzy optimization in production planning. Journal of Intelligent & Fuzzy Systems 25, 441455.CrossRefGoogle Scholar
Khemiri, R, Elbedoui-Maktouf, K, Grabot, B and Zouari, B (2017) A fuzzy multi-criteria decision-making approach for managing performance and risk in integrated procurement–production planning. International Journal of Production Research 55, 53055329.CrossRefGoogle Scholar
Kim, T-H, Ramos, C and Mohammed, S (2017) Smart city and IoT. Future Generation Computer Systems 76, 159162.CrossRefGoogle Scholar
Lee, Y, Jeong, J and Son, Y (2017) Design and implementation of the secure compiler and virtual machine for developing secure IoT services. Future Generation Computer Systems 76, 350357.CrossRefGoogle Scholar
Lin, Y-C and Chen, T (2017) A ubiquitous manufacturing network system. Robotics and Computer-Integrated Manufacturing 45, 157167.CrossRefGoogle Scholar
Morawski, J, Stepan, T, Dick, S and Miller, J (2017) A fuzzy recommender system for public library catalogs. International Journal of Intelligent Systems 32, 1098–111X.CrossRefGoogle Scholar
Rahman, HF, Sarker, R and Essam, D (2017) A genetic algorithm for permutation flowshop scheduling under practical make-to-order production system. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, 87103.CrossRefGoogle Scholar
Sarker, R, Essam, D, Hasan, SK and Karim, AM (2016) Managing risk in production scheduling under uncertain disruption. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, 289299.CrossRefGoogle Scholar
Su, TS and Lin, YF (2015) Fuzzy multi-objective procurement/production planning decision problems for recoverable manufacturing systems. Journal of Manufacturing Systems 37, 396408.CrossRefGoogle Scholar
Sun, Y, Chen, M, Hu, L, Qian, Y and Hassan, MM (2017) ASA: against statistical attacks for privacy-aware users in location-based service. Future Generation Computer Systems 70, 4858.CrossRefGoogle Scholar
Wu, H-C and Chen, T-CT (2018) Quality control issues in 3D-printing manufacturing: A review. Rapid Prototyping Journal 24, 607614.CrossRefGoogle Scholar
Wu, D, Terpenny, J and Schaefer, D (2017) Digital design and manufacturing on the cloud: a review of software and services. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, 104118.CrossRefGoogle Scholar
Zhang, Y, Huang, GQ, Qu, T, Ho, O and Sun, S (2011) Agent-based smart objects management system for real-time ubiquitous manufacturing. Robotics and Computer-Integrated Manufacturing 27, 538549.CrossRefGoogle Scholar
Zhong, RY, Huang, GQ, Lan, S, Dai, QY, Zhang, T and Xu, C (2015) A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing. Advanced Engineering Informatics 29, 799812.CrossRefGoogle Scholar
Zimmermann, L, Chen, T and Shea, K (2018) A 3D, performance-driven generative design framework: automating the link from a 3D spatial grammar interpreter to structural finite element analysis and stochastic optimization. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, 189199.CrossRefGoogle Scholar