Scheduling of Parallel 3D-Printing Machines with Incompatible Job Families: A Matheuristic Algorithm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00351244" target="_blank" >RIV/68407700:21730/21:00351244 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-85874-2_6" target="_blank" >https://doi.org/10.1007/978-3-030-85874-2_6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-85874-2_6" target="_blank" >10.1007/978-3-030-85874-2_6</a>
Alternative languages
Result language
angličtina
Original language name
Scheduling of Parallel 3D-Printing Machines with Incompatible Job Families: A Matheuristic Algorithm
Original language description
Additive manufacturing (AM) is a promising technology for the rapid prototyping and production of highly customized products. The scheduling of AM machines has an essential role in increasing profitability and has recently received a great deal of attention. This paper investigates the scheduling of batch processing of parallel 3d-printing machines to minimize the total weighted tardiness. Accordingly, a mathematical model is proposed to formulate the problem considering the sequence-dependent setup time and incompatible job families, where jobs of different families are processed with different materials and desired quality. Due to the high complexity of the problem, an efficient matheuristic algorithm is presented based on the hybridization of a genetic algorithm and a local search method based on mixed integer programming (MIP). Computational results show that the proposed approach is efficient and promising to solve the problem.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LL1902" target="_blank" >LL1902: Powering SMT Solvers by Machine Learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems
ISBN
978-3-030-85901-5
ISSN
1868-4238
e-ISSN
1868-422X
Number of pages
11
Pages from-to
51-61
Publisher name
Springer
Place of publication
Cham
Event location
Nantes
Event date
Sep 5, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000717630100006