FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2985520022> ?p ?o ?g. }

• W2985520022 endingPage "105" @default.
• W2985520022 startingPage "94" @default.
• W2985520022 abstract "This article investigates a hybrid flow shop scheduling problem that consists of a batch processor in the upstream and a discrete processor in the downstream. Limited waiting time between the batch processor and discrete processor is taken into consideration. Such a scheduling problem is commonly seen as bottlenecks in the production of precision parts, back-end process of semiconductor products, and glass and steel industries. A mixed-integer linear programming model is presented to minimize the makespan. Considering the complexity of this problem and the imperative requirement in real-time optimization, we first develop a constructive heuristic together with the worst case analysis by exploiting the key decision structure of the problem. Based on the decision structure, we then develop a learning-based scheduling approach via customized genetic programming to automatically generate effective heuristics for this problem. Lower bounds are also developed to provide a measurement for the performance of proposed algorithms. Numerical results show that our proposed algorithms outperform the existing metaheuristics and are capable of providing high-quality solutions using less computational time. Note to Practitioners-The production system consisting of a batch processor in the upstream and a discrete processor in the downstream is common in practice. The batch processor first handles a group of jobs simultaneously. Then, the jobs are released to a buffer to wait for the process on the discrete processor one by one. However, the waiting time of the jobs in the buffer is often required to be limited according to the production requirements. For example, after being heated in the heat-treatment oven, the aerospace precision parts have to be processed on the machining equipment in limited waiting time to improve the processability in subsequent manufacturing. The semiconductor chips have to be packed in limited waiting time after baking to avoid getting wet. The incongruous production modes between the batch processor and discrete processor, together with the limited waiting time constraint, make such operations always the bottleneck in manufacturing. Efficient heuristics, providing high-quality solutions with low time complexity, are much preferred in practice for most of the complicated scheduling problems, such as the scenarios described earlier. However, the designing process of an effective heuristic is tedious, and the heuristic is usually deeply customized for a certain production scenario. Genetic programming (GP) provides an inspiring approach to automatically generate sophisticated heuristics for complicated scheduling problems through evolutionary learning processes. By customizing a GP-based approach, the designing process of heuristics is automated, and some undetectable knowledge relations can be obtained to enhance the quality of heuristics. Such an approach facilitates to obtain more sophisticated schedules by analyzing valuable knowledge for smart manufacturing. The superiority of the heuristic learned by GP is shown in the computational experiment, and it has great potential to be applied to the practical scheduling." @default.
• W2985520022 created "2019-11-22" @default.
• W2985520022 creator A5014158687 @default.
• W2985520022 creator A5019242492 @default.
• W2985520022 creator A5019251583 @default.
• W2985520022 creator A5069749373 @default.
• W2985520022 creator A5070777072 @default.
• W2985520022 date "2021-01-01" @default.
• W2985520022 modified "2023-10-18" @default.
• W2985520022 title "A Genetic Programming-Based Scheduling Approach for Hybrid Flow Shop With a Batch Processor and Waiting Time Constraint" @default.
• W2985520022 cites W1488422606 @default.
• W2985520022 cites W1966097591 @default.
• W2985520022 cites W1966303191 @default.
• W2985520022 cites W1981029154 @default.
• W2985520022 cites W1990529873 @default.
• W2985520022 cites W2012945770 @default.
• W2985520022 cites W2021261368 @default.
• W2985520022 cites W2023002456 @default.
• W2985520022 cites W2032302696 @default.
• W2985520022 cites W2040492000 @default.
• W2985520022 cites W2042380163 @default.
• W2985520022 cites W2060749288 @default.
• W2985520022 cites W2063271630 @default.
• W2985520022 cites W2073870328 @default.
• W2985520022 cites W2087376002 @default.
• W2985520022 cites W2087973581 @default.
• W2985520022 cites W2119924737 @default.
• W2985520022 cites W2126707169 @default.
• W2985520022 cites W2133826558 @default.
• W2985520022 cites W2398671303 @default.
• W2985520022 cites W2526903408 @default.
• W2985520022 cites W2557368223 @default.
• W2985520022 cites W2567428193 @default.
• W2985520022 cites W2766585785 @default.
• W2985520022 cites W2768390119 @default.
• W2985520022 cites W2804781695 @default.
• W2985520022 cites W2903041613 @default.
• W2985520022 cites W2904245325 @default.
• W2985520022 cites W2913661964 @default.
• W2985520022 cites W2942797034 @default.
• W2985520022 doi "https://doi.org/10.1109/tase.2019.2947398" @default.
• W2985520022 hasPublicationYear "2021" @default.
• W2985520022 type Work @default.
• W2985520022 sameAs 2985520022 @default.
• W2985520022 citedByCount "12" @default.
• W2985520022 countsByYear W29855200222020 @default.
• W2985520022 countsByYear W29855200222022 @default.
• W2985520022 countsByYear W29855200222023 @default.
• W2985520022 crossrefType "journal-article" @default.
• W2985520022 hasAuthorship W2985520022A5014158687 @default.
• W2985520022 hasAuthorship W2985520022A5019242492 @default.
• W2985520022 hasAuthorship W2985520022A5019251583 @default.
• W2985520022 hasAuthorship W2985520022A5069749373 @default.
• W2985520022 hasAuthorship W2985520022A5070777072 @default.
• W2985520022 hasConcept C111919701 @default.
• W2985520022 hasConcept C11413529 @default.
• W2985520022 hasConcept C120314980 @default.
• W2985520022 hasConcept C126255220 @default.
• W2985520022 hasConcept C127705205 @default.
• W2985520022 hasConcept C149635348 @default.
• W2985520022 hasConcept C158336966 @default.
• W2985520022 hasConcept C173608175 @default.
• W2985520022 hasConcept C206729178 @default.
• W2985520022 hasConcept C33923547 @default.
• W2985520022 hasConcept C41008148 @default.
• W2985520022 hasConcept C41045048 @default.
• W2985520022 hasConcept C55416958 @default.
• W2985520022 hasConcept C56086750 @default.
• W2985520022 hasConcept C74172769 @default.
• W2985520022 hasConceptScore W2985520022C111919701 @default.
• W2985520022 hasConceptScore W2985520022C11413529 @default.
• W2985520022 hasConceptScore W2985520022C120314980 @default.
• W2985520022 hasConceptScore W2985520022C126255220 @default.
• W2985520022 hasConceptScore W2985520022C127705205 @default.
• W2985520022 hasConceptScore W2985520022C149635348 @default.
• W2985520022 hasConceptScore W2985520022C158336966 @default.
• W2985520022 hasConceptScore W2985520022C173608175 @default.
• W2985520022 hasConceptScore W2985520022C206729178 @default.
• W2985520022 hasConceptScore W2985520022C33923547 @default.
• W2985520022 hasConceptScore W2985520022C41008148 @default.
• W2985520022 hasConceptScore W2985520022C41045048 @default.
• W2985520022 hasConceptScore W2985520022C55416958 @default.
• W2985520022 hasConceptScore W2985520022C56086750 @default.
• W2985520022 hasConceptScore W2985520022C74172769 @default.
• W2985520022 hasFunder F4320321001 @default.
• W2985520022 hasFunder F4320323970 @default.
• W2985520022 hasIssue "1" @default.
• W2985520022 hasLocation W29855200221 @default.
• W2985520022 hasOpenAccess W2985520022 @default.
• W2985520022 hasPrimaryLocation W29855200221 @default.
• W2985520022 hasRelatedWork W1504346515 @default.
• W2985520022 hasRelatedWork W2025999952 @default.
• W2985520022 hasRelatedWork W2768736937 @default.
• W2985520022 hasRelatedWork W2770649705 @default.
• W2985520022 hasRelatedWork W2786155106 @default.
• W2985520022 hasRelatedWork W2795923574 @default.
• W2985520022 hasRelatedWork W3028389691 @default.
• W2985520022 hasRelatedWork W4205535298 @default.

• next