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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/w77vm44enzh4tgk44gtrsw7f64" style="color: black;">Tsinghua Science and Technology</a>
Currently, manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization. Hence, they have to extend their production mode into distributed environments and establish multiple factories in various geographical locations. Nowadays, distributed manufacturing systems have been widely adopted in industrial production processes. In recent years, many studies have been done on the modeling and<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.26599/tst.2021.9010009">doi:10.26599/tst.2021.9010009</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bmdgzjbzr5cs3le32dispcnjai">fatcat:bmdgzjbzr5cs3le32dispcnjai</a> </span>
more »... ion of distributed scheduling problems. This work provides a literature review on distributed scheduling problems in intelligent manufacturing systems. By summarizing and evaluating existing studies on distributed scheduling problems, we analyze the achievements and current research status in this field and discuss ongoing studies. Insights regarding prior works are discussed to uncover future research directions, particularly swarm intelligence and evolutionary algorithms, which are used for managing distributed scheduling problems in manufacturing systems. This work focuses on journal papers discovered using Google Scholar. After reviewing the papers, in this work, we discuss the research trends of distributed scheduling problems and point out some directions for future studies.
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