FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4226193309> ?p ?o ?g. }

• W4226193309 endingPage "232" @default.
• W4226193309 startingPage "227" @default.
• W4226193309 abstract "In this work we propose a Reinforcement Learning approach for a real-world flexible job shop scheduling scenario, where a two-armed robot and a human operator share two workstations in order to assemble light switches. The approach returns a schedule of predefined assembly actions, taking into account given constraints specific to the scenario, such as which actions should precede others, which actions should occur together, or which actions should occur separately. In order to avoid collisions, the work area is divided into zones and all these constraints are taken into account by the algorithm in order to propose a schedule that optimizes the time it takes to prepare the customer orders. The approach that is taken is a reinforcement learning approach. There both arms and the operator are considered to be agents, and they learn from experience who should do which job best, taking into account the above mentioned constraints, as well as the estimated timings to execute the tasks. These estimated timings can be online updated, as well as their uncertainty. The experiments show that the proposed algorithm outperforms the currently used algorithm, which is a constraint solver, in terms of computation time and it is more user friendly with respect to expressing constraints. It also allows to take into account the uncertainty on the execution times of the tasks, and to add slack at critical timeslots, without unnecessarily increasing the makespan. Moreover, the algorithm allows to recompute an updated solution dynamically and efficiently when it turns out an action cannot be performed within the proposed time due to unexpected events. While we show the benefits in a scenario with a two-armed robot collaborating with a human for assembling light switches, the approach is relevant for many assembly tasks in the manufacturing industry where machines and human operators work together." @default.
• W4226193309 created "2022-05-05" @default.
• W4226193309 creator A5004660966 @default.
• W4226193309 creator A5059221390 @default.
• W4226193309 creator A5064553018 @default.
• W4226193309 creator A5075764887 @default.
• W4226193309 creator A5087074197 @default.
• W4226193309 date "2022-01-01" @default.
• W4226193309 modified "2023-10-18" @default.
• W4226193309 title "A Q-Learning algorithm for flexible job shop scheduling in a real-world manufacturing scenario" @default.
• W4226193309 cites W1488422606 @default.
• W4226193309 cites W1988634397 @default.
• W4226193309 cites W1990185442 @default.
• W4226193309 cites W2155900361 @default.
• W4226193309 cites W2157030740 @default.
• W4226193309 cites W2287386220 @default.
• W4226193309 cites W2471676583 @default.
• W4226193309 cites W2473537816 @default.
• W4226193309 cites W2571502949 @default.
• W4226193309 cites W2747092504 @default.
• W4226193309 cites W2774864303 @default.
• W4226193309 cites W2811338889 @default.
• W4226193309 cites W2953727365 @default.
• W4226193309 cites W3005944338 @default.
• W4226193309 cites W3009209772 @default.
• W4226193309 cites W3011279400 @default.
• W4226193309 cites W3040778284 @default.
• W4226193309 cites W3120185273 @default.
• W4226193309 cites W32403112 @default.
• W4226193309 cites W85477776 @default.
• W4226193309 doi "https://doi.org/10.1016/j.procir.2022.02.183" @default.
• W4226193309 hasPublicationYear "2022" @default.
• W4226193309 type Work @default.
• W4226193309 citedByCount "2" @default.
• W4226193309 countsByYear W42261933092023 @default.
• W4226193309 crossrefType "journal-article" @default.
• W4226193309 hasAuthorship W4226193309A5004660966 @default.
• W4226193309 hasAuthorship W4226193309A5059221390 @default.
• W4226193309 hasAuthorship W4226193309A5064553018 @default.
• W4226193309 hasAuthorship W4226193309A5075764887 @default.
• W4226193309 hasAuthorship W4226193309A5087074197 @default.
• W4226193309 hasBestOaLocation W42261933091 @default.
• W4226193309 hasConcept C104317684 @default.
• W4226193309 hasConcept C111919701 @default.
• W4226193309 hasConcept C11413529 @default.
• W4226193309 hasConcept C120314980 @default.
• W4226193309 hasConcept C126255220 @default.
• W4226193309 hasConcept C154945302 @default.
• W4226193309 hasConcept C158336966 @default.
• W4226193309 hasConcept C158448853 @default.
• W4226193309 hasConcept C17020691 @default.
• W4226193309 hasConcept C185592680 @default.
• W4226193309 hasConcept C199360897 @default.
• W4226193309 hasConcept C206729178 @default.
• W4226193309 hasConcept C2524010 @default.
• W4226193309 hasConcept C2776036281 @default.
• W4226193309 hasConcept C2777243215 @default.
• W4226193309 hasConcept C2778770139 @default.
• W4226193309 hasConcept C33923547 @default.
• W4226193309 hasConcept C41008148 @default.
• W4226193309 hasConcept C45374587 @default.
• W4226193309 hasConcept C55416958 @default.
• W4226193309 hasConcept C55493867 @default.
• W4226193309 hasConcept C67953723 @default.
• W4226193309 hasConcept C68387754 @default.
• W4226193309 hasConcept C86339819 @default.
• W4226193309 hasConcept C90509273 @default.
• W4226193309 hasConcept C97541855 @default.
• W4226193309 hasConceptScore W4226193309C104317684 @default.
• W4226193309 hasConceptScore W4226193309C111919701 @default.
• W4226193309 hasConceptScore W4226193309C11413529 @default.
• W4226193309 hasConceptScore W4226193309C120314980 @default.
• W4226193309 hasConceptScore W4226193309C126255220 @default.
• W4226193309 hasConceptScore W4226193309C154945302 @default.
• W4226193309 hasConceptScore W4226193309C158336966 @default.
• W4226193309 hasConceptScore W4226193309C158448853 @default.
• W4226193309 hasConceptScore W4226193309C17020691 @default.
• W4226193309 hasConceptScore W4226193309C185592680 @default.
• W4226193309 hasConceptScore W4226193309C199360897 @default.
• W4226193309 hasConceptScore W4226193309C206729178 @default.
• W4226193309 hasConceptScore W4226193309C2524010 @default.
• W4226193309 hasConceptScore W4226193309C2776036281 @default.
• W4226193309 hasConceptScore W4226193309C2777243215 @default.
• W4226193309 hasConceptScore W4226193309C2778770139 @default.
• W4226193309 hasConceptScore W4226193309C33923547 @default.
• W4226193309 hasConceptScore W4226193309C41008148 @default.
• W4226193309 hasConceptScore W4226193309C45374587 @default.
• W4226193309 hasConceptScore W4226193309C55416958 @default.
• W4226193309 hasConceptScore W4226193309C55493867 @default.
• W4226193309 hasConceptScore W4226193309C67953723 @default.
• W4226193309 hasConceptScore W4226193309C68387754 @default.
• W4226193309 hasConceptScore W4226193309C86339819 @default.
• W4226193309 hasConceptScore W4226193309C90509273 @default.
• W4226193309 hasConceptScore W4226193309C97541855 @default.
• W4226193309 hasLocation W42261933091 @default.
• W4226193309 hasOpenAccess W4226193309 @default.
• W4226193309 hasPrimaryLocation W42261933091 @default.
• W4226193309 hasRelatedWork W1543299114 @default.

• next