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Growing Science » International Journal of Industrial Engineering Computations » A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling

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International Journal of Industrial Engineering Computations
ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print)
Quarterly Publication
Volume 9 Issue 4 pp. 423-438, 2018

A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling Pages 423-438 Right click to download the paper Download PDF

Authors: M. Fera, F. Fruggiero, A. Lambiase, R. Macchiaroli, V. Todisco

📋 Author Affiliations:
M. Fera ORCID 1, F. Fruggiero ORCID 2, A. Lambiase ORCID 3, R. Macchiaroli ORCID 1, V. Todisco1
1 Department of Industrial and Information Engineering, University of Campania “Luigi Vanvitelli”, Via Roma 29, Aversa, CE, Italy
2 University of Basilicata, School of Engineering, Via Nazario Sauro, 85, Potenza, PZ, Italy
3 Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II, Fisciano, SA, Italy
doi 10.5267/j.ijiec.2018.1.001
82 Source: Scopus
Crossref 49 Source: CrossRef

🔑 Keywords: Additive Manufacturing, Scheduling, Time, Cost, Metaheuristics, Production Planning

Abstract: Additive Manufacturing (AM) is a process of joining materials to make objects from 3D model data, usually layer by layer, as opposed to subtractive manufacturing methodologies. Selective Laser Melting, commercially known as Direct Metal Laser Sintering (DMLS®), is the most diffused additive process in today’s manufacturing industry. Introduction of a DMLS® machine in a production department has remarkable effects not only on industrial design but also on production planning, for example, on machine scheduling. Scheduling for a traditional single machine can employ consolidated models. Scheduling of an AM machine presents new issues because it must consider the capability of producing different geometries, simultaneously. The aim of this paper is to provide a mathematical model for an AM/SLM machine scheduling. The complexity of the model is NP-HARD, so possible solutions must be found by metaheuristic algorithms, e.g., Genetic Algorithms. Genetic Algorithms solve sequential optimization problems by handling vectors; in the present paper, we must modify them to handle a matrix. The effectiveness of the proposed algorithms will be tested on a test case formed by a 30 Part Number production plan with a high variability in complexity, distinct due dates and low production volumes.

How to cite this paper
APA: Fera, M., Fruggiero, F., Lambiase, A., Macchiaroli, R & Todisco, V. (2018). A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling. International Journal of Industrial Engineering Computations, 9(4), 423-438.
Chicago/Turabian: Fera, M., Fruggiero, F., Lambiase, A., Macchiaroli, R & Todisco, V. 2018. "A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling." International Journal of Industrial Engineering Computations 9, no. 4 (2018): 423-438.
AMA: Fera, M., Fruggiero, F., Lambiase, A., Macchiaroli, R & Todisco, V. A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling. International Journal of Industrial Engineering Computations. 2018;9(4):423-438.

References
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Fera, M., Fruggiero, F., Lambiase, A., & Macchiaroli, R. (2016a). State of the art of additive manufacturing: Review for tolerances, mechanical resistance and production costs. Cogent Engineering, 3(1), 1261503.
Fera, M., Macchiaroli, R., Fruggiero, F. & Lambiase, A. (2017). A new perspective for production process analysis using additive manufacturing—complexity vs production volume. International Journal of Advanced Manufacturing and Technology, https://doi.org/10.1007/s00170-017-1221-1
Fera, M., Macchiaroli, R., Iannone, R., Miranda, S., & Riemma, S. (2016b). Economic evaluation model for the energy Demand Response. Energy, 112, 457-468.
Fruggiero, F., Riemma, S., Ouazene, Y., Macchiaroli, R., & Guglielmi, V. (2016). Incorporating the human factor within manufacturing dynamics. IFAC-PapersOnLine, 49(12), 1691-1696.
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📚 Journal: International Journal of Industrial Engineering Computations | 📅 Year: 2018 | 📖 Volume: 9 | 📄 Issue: 4 | 👁️ Views: 7340 | 📊 Crossref: 49

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