English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The interrupting swap-allowed blocking job shop problem (ISBJSSP) is acomplex scheduling problem that is able to model many manufacturing planningand logistics applications realistically by addressing both the lack of storagecapacity and unforeseen production interruptions. Subjected to randomdisruptions due to machine malfunction or maintenance, industry productionsettings often choose to adopt dispatching rules to enable adaptive, real-timere-scheduling, rather than traditional methods that require costlyre-computation on the new configuration every time the problem conditionchanges dynamically. To generate dispatching rules for the ISBJSSP problem, weintroduce a dynamic disjunctive graph formulation characterized by nodes andedges subjected to continuous deletions and additions. This formulation enablesthe training of an adaptive scheduler utilizing graph neural networks andreinforcement learning. Furthermore, a simulator is developed to simulateinterruption, swapping, and blocking in the ISBJSSP setting. Employing a set ofreported benchmark instances, we conduct a detailed experimental study onISBJSSP instances with a range of machine shutdown probabilities to show thatthe scheduling policies generated can outperform or are at least as competitiveas existing dispatching rules with predetermined priority. This study showsthat the ISBJSSP, which requires real-time adaptive solutions, can be scheduledefficiently with the proposed method when production interruptions occur withrandom machine shutdowns.

Generating Dispatching Rules for the Interrupting Swap-Allowed Blocking Job Shop Problem Using Graph Neural Network and Reinforcement Learning