A Decision-Support Model for Additive Manufacturing Scheduling Using an Integrative Analytic Hierarchy Process and Multi-Objective Optimization
Source abstract
Abstract
Additive manufacturing (AM) became widespread through several organizations due to its benefits in providing design freedom, inventory improvement, cost reduction, and supply chain design. Process planning in AM involving various AM technologies is also complicated and scarce. Thus, this study proposed a decision-support tool that integrates production and distribution planning in AM involving material extrusion (ME), stereolithography (SLA), and selective laser sintering (SLS). A multi-objective optimization approach was used to schedule component batches to a network of AM printers. Next, the analytic hierarchy process (AHP) technique was used to analyze trade-offs among conflicting criteria. The developed model was then demonstrated in a decision-support system environment to enhance practitioners’ applications. Then, the developed model was verified through a case study using automotive and healthcare parts. Finally, an experimental design was conducted to evaluate the complexity of the model and computation time by varying the number of parts, printer types, and distribution locations.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Manufacturing Process and Optimization · Sustainable Supply Chain Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Industrial Engineering, Ubonratchathani University, Ubon Ratchathani 34190, Thailand
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44776, Korea
Funding
Office of the Higher Education Commissions and Thailand Research Fund · Ulsan National Institute of Science and Technology
References (44)
Show source-provided references
1. Design for Additive Manufacturing: Trends, opportunities, considerations, and constraints10.1016/j.cirp.2016.05.004 ↗
2. Wohlers, T. (2018). Wohlers Report 2018, Wohlers Associates, Inc.
3. Ha, S., Ransikarbum, K., and Kim, N. (2016, January 10–12). Phenomenological deformation patterns of 3D printed products in a selective laser sintering process. Proceedings of the 18th International Conference on Industrial Engineering, Seoul, Korea.
4. A dimensional compensation algorithm for vertical bending deformation of 3D printed parts in selective laser sintering10.1108/RPJ-12-2016-0202 ↗
5. Kim, N., Bhalerao, I., Han, D., Yang, C., and Lee, H. (2019). Improving surface roughness of additively manufactured parts using a photopolymerization model and multi-objective particle swarm optimization. Appl. Sci., 9.10.3390/app9010151 ↗
6. Ransikarbum, K., and Kim, N. (2017, January 10–13). Multi-criteria selection problem of part orientation in 3D fused deposition modeling based on analytic hierarchy process model: A case study. Proceedings of the 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore.10.1109/IEEM.2017.8290134 ↗
7. Ransikarbum, K., and Kim, N. (2017, January 21–23). Data envelopment analysis-based multi-criteria decision making for part orientation selection in fused deposition modeling. Proceedings of the 2017 4th International Conference on Industrial Engineering and Applications (ICIEA), Nagoya, Japan.10.1109/IEA.2017.7939183 ↗
8. Evaluation of Assembly Part Build Orientation in Additive Manufacturing Environment using Data Envelopment Analysis10.1051/matecconf/201929302002 ↗
9. Ransikabum, K., Yingviwatanapong, C., Leksomboon, R., Wajanavisit, T., and Bijaphala, N. (2019, January 11–13). Additive Manufacturing-based Healthcare 3D Model for Education: Literature Review and A Feasibility Study. Proceedings of the 2019 Research, Invention, and Innovation Congress (RI2C), Bangkok, Thailand.10.1109/RI2C48728.2019.8999943 ↗
10. Sculpteo (2018, June 05). Comparison between 3D Printing and Traditional Manufacturing Processes for Plastics. Available online: http://www.sculpteo.com/en/3d-printing.
11. Multi-objective optimization analysis for part-to-Printer assignment in a network of 3D fused deposition modeling10.1016/j.jmsy.2017.02.012 ↗
12. Role of build orientation in layered manufacturing: A review10.1504/IJMTM.2013.058637 ↗
13. Genetic-algorithm-based multi-objective optimization of the build orientation in Stereolithography10.1007/s00170-009-2006-y ↗
14. Freens, J.P., Adan, I.J., Pogromsky, A.Y., and Ploegmakers, H. (2015, January 6–9). Automating the production planning of a 3D printing factory. Proceedings of the IEEE 2015 Winter Simulation Conference (WSC), Huntington Beach, CA, USA.10.1109/WSC.2015.7408327 ↗
15. Multiple-Objective Analysis of Integrated Relief Supply and Network Restoration in Humanitarian Logistics Operations10.1080/00207543.2014.977458 ↗
16. Goal programming-based post-disaster decision making for integrated relief distribution and network restoration10.1016/j.ijpe.2016.08.030 ↗
17. An analysis of multi-criteria decision making methods
18. Relative measurement and its generalization in decision making why pairwise comparisons are central in mathematics for the measurement of intangible factors—The analytical hierarchy/network process, RACSAM-Revista de la Real Academia de Ciencias Exactas, Fiscas y naturales. Series A
19. Extending the measurement of tangibles to intangibles10.1142/S0219622009003247 ↗
20. ASTM (2012). Standard Terminology for Additive Manufacturing Technologies, ASTM International.
21. Bourell, D.L., Beaman, J.J., Leu, M.C., and Rosen, D.W. (2009, January 24–25). A Brief History of Additive Manufacturing and the 2009 Roadmap for Additive Manufacturing: Looking Back and Looking Ahead. Proceedings of the RapidTech 2009: US-TURKEY Workshop on Rapid Technologies, Istanbul, Turkey.
22. 3-D printing: The new industrial revolution10.1016/j.bushor.2011.11.003 ↗
23. Additive manufacturing technologies: State of the art and trends10.1080/00207543.2015.1115909 ↗
24. A study on the Prototype Modeling Method using 3D Printing
25. (2020, January 15). 3D Hubs. Available online: https://www.3dhubs.com/.
26. (2020, January 15). Shapeways. Available online: https://www.shapeways.com/.
27. A methodological framework for the inclusion of modern additive manufacturing into the production portfolio of a focused factory10.1016/j.jmsy.2014.07.014 ↗
28. Additive manufacturing-integrated hybrid manufacturing and subtractive processes: Economic model and analysis10.1080/0951192X.2015.1067920 ↗
29. Build orientation optimization for multi-part production in additive manufacturing10.1007/s10845-015-1057-1 ↗
30. Hasan, S., and Rennie, A.E.W. (2008, January 4–8). The Application of Rapid Manufacturing Technologies in the Spare Parts Industry. Proceedings of the Solid Freeform Fabrication Symposium, Austin, TX, USA.
31. Rapid manufacturing in the spare parts supply chain: Alternative approaches to capacity deployment10.1108/17410381011063996 ↗
32. Digital manufacturing-driven transformations of service supply chains for complex products10.1108/SCM-10-2013-0387 ↗
33. Additive manufacturing in the spare parts supply chain10.1016/j.compind.2013.07.008 ↗
34. Costs and Cost Effectiveness of Additive Manufacturing
35. Verma, A., and Rai, R. (2013). Energy efficient modeling and optimization of additive manufacturing processes. Proc. of Solid Freeform Fabrication Symposium, University of Texas.
36. A comparison of energy consumption in bulk forming, subtractive, and additive processes: Review and case study10.1007/s40684-014-0033-0 ↗
37. A Cost-Driven Design Methodology for Additive Manufactured Variable Platforms in Product Families10.1115/1.4032504 ↗
38. (2018, January 30). Materialise. Available online: https://www.materialise.com/en.
39. Analytic hierarchy process: An overview of applications10.1016/j.ejor.2004.04.028 ↗
40. Criteria Analysis of Food Safety using the Analytic Hierarchy Process (AHP)-A Case study of Thailand’s Fresh Markets10.1051/e3sconf/202014102001 ↗
41. Integrated analytic hierarchy process and its applications—A literature review10.1016/j.ejor.2007.01.004 ↗
42. Khamhong, P., Yingviwatanapong, C., and Ransikarbum, K. (2019, January 11–13). Fuzzy Analytic Hierarchy Process (AHP)-based Criteria Analysis for 3D Printer Selection in Additive Manufacturing. Proceedings of the 2019 Research, Invention, and Innovation Congress (RI2C), Bangkok, Thailand.10.1109/RI2C48728.2019.8999950 ↗
43. Fourer, R., Gay, D., and Kernighan, B. (2002). AMPL: A Modeling Language for Mathematical Programming, Duxbury Press, Brooks/Cole Publishing. [2nd ed.].
44. National Institutes of Health (2019, January 30). NIH 3D Print Exchange, Available online: https://3dprint.nih.gov/.