An integrated spatial-temporal neural network for proactive throughput bottleneck prediction in high-variety shops with complex job routings
Lin Ma, Ting Qu, Matthias Thürer, Zaoqi Wang, Mingze Yuan, Lei Liu
| Year of publication: |
2023
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|---|---|
| Authors: | Ma, Lin ; Qu, Ting ; Thürer, Matthias ; Wang, Zaoqi ; Yuan, Mingze ; Liu, Lei |
| Published in: |
International journal of production research. - London [u.a.] : Taylor & Francis, ISSN 1366-588X, ZDB-ID 1485085-0. - Vol. 61.2023, 13, p. 4437-4449
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| Subject: | Bottleneck analysis | high-variety production | machine learning | temporal-spatial LSTM | theory of constraints | Engpass | Bottleneck | Neuronale Netze | Neural networks | Künstliche Intelligenz | Artificial intelligence | Prognoseverfahren | Forecasting model | Scheduling-Verfahren | Scheduling problem | Tourenplanung | Vehicle routing problem |
Online Resource
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1080/00207543.2022.2148769 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014381543