RT Conference Proceedings
T1 A Parallel Variable Neighborhood Search for Solving Real-World Production-Scheduling Problems
A1 Osaba, Eneko
A1 Loizaga, Erlantz
A1 Goenaga, Xabier
A1 Sanchez, Valentin
A2 Camacho, David
A2 Tino, Peter
A2 Allmendinger, Richard
A2 Yin, Hujun
A2 Tallón-Ballesteros, Antonio J.
A2 Tang, Ke
A2 Cho, Sung-Bae
A2 Novais, Paulo
A2 Nascimento, Susana
AB In recent years, industry has evolved towards the efficient digitalization and optimization of products and processes. This situation is the consequence of the huge amount of information available in indus trial environments and its efficient management for reaching unprece dented productivity levels. The momentum that enjoys this application field has led to the proposal of advanced methods for the dealing of robotic processes in industrial plants, optimal packaging of goods and the efficient scheduling of production plans, among many others. This paper is focused on the last of these categories. More concretely, we present a Parallel Variable Neighborhood Search for solving an industrial problem in which a fixed amount of materials should be constructed into a limited number of production lines. The construction of these materials has sev eral particularities, such as the need of some specific tools to be correctly produced. It is also relevant to underscore that the problem solved in this research corresponds to a real-world situation, and that it is currently deployed in a production plant in the Basque Country (Spain).
PB Springer Science and Business Media Deutschland GmbH
SN 9783030916077
YR 2021
FD 2021-11-23
LA eng
NO Osaba , E , Loizaga , E , Goenaga , X & Sanchez , V 2021 , A Parallel Variable Neighborhood Search for Solving Real-World Production-Scheduling Problems . in D Camacho , P Tino , R Allmendinger , H Yin , A J Tallón-Ballesteros , K Tang , S-B Cho , P Novais & S Nascimento (eds) , unknown . vol. 13113 , 0302-9743 , Springer Science and Business Media Deutschland GmbH , pp. 12-20 , 22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021 , Virtual, Online , 25/11/21 . https://doi.org/10.1007/978-3-030-91608-4_2
NO conference
NO Publisher Copyright: © 2021, Springer Nature Switzerland AG.
DS TECNALIA Publications
RD 29 sept 2024