Treffer: Scheduling of distributed additive manufacturing machines considering carbon emissions.

Title:
Scheduling of distributed additive manufacturing machines considering carbon emissions.
Source:
International Journal of Optimization & Control: Theories & Applications; Jan2024, Vol. 14 Issue 1, p20-31, 12p
Database:
Complementary Index

Weitere Informationen

Additive manufacturing is a rapidly growing technology shaping the future of manufacturing. In an increasingly competitive economy, additive manufacturing can help businesses to remain agile, innovative, and sustainable. This paper introduces the multi-site additive manufacturing (AM) machine scheduling problem considering carbon emissions caused by production and transportation. A mixed-integer linear programming model is developed aiming to optimise two separate objectives addressing economic and environmental sustainability in a multiple unrelated AM machine environment. The former is the total cost caused by production, transportation, set-up and tardiness penalty and the latter is the total amount of carbon emissions caused by production and transportation. The model is coded in Python and solved by Gurobi Optimizer. A numerical example is provided to represent the basic characteristics of the problem and show the necessity of the proposed framework. A comprehensive computational study is conducted under 600s and 1800s time limits for two main scenarios and the results have been elaborated. This article introduces the concept of considering both economic and environmental sustainability caused by production and transportation, proposing the first mathematical model and measuring its performance through a comprehensive experimental study. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Optimization & Control: Theories & Applications is the property of Ramazan YAMAN and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)