A Hybrid Migratory Bird Optimization Algorithm for solving the Large-Scale Flow Shop Scheduling Problem

The flow-shop scheduling problem (FSP) is significant in modern industry, with increasing complexity and computation time as the scheduling scale grows. To address this, this paper integrates the Migratory Bird Optimization (MBO) algorithm with the Simulated Annealing (SA) algorithm. This hybrid app...

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Vydáno v:Chinese Automation Congress (Online) s. 6232 - 6237
Hlavní autoři: Yang, Zonglin, Li, Shaoyuan, Yin, Xiaohong
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2024
Témata:
automatic scheduling
Birds
Job shop scheduling
Maintenance
Memetics
migratory bird optimization algorithm
Optimization
Processor scheduling
Production
Schedules
Search problems
Simulated annealing
simulated annealing algorithm
styling
workshop scheduling
ISSN:2688-0938
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Shrnutí:The flow-shop scheduling problem (FSP) is significant in modern industry, with increasing complexity and computation time as the scheduling scale grows. To address this, this paper integrates the Migratory Bird Optimization (MBO) algorithm with the Simulated Annealing (SA) algorithm. This hybrid approach leverages MBO's robust global search and SA's effectiveness in avoiding local optima. The hybrid MBO algorithm aims to minimize makespan, supporting automated, intelligent, and unmanned production in core workshops. Parts of the Taillard benchmark are used to verify the proposed SAMBO algorithm's performance, comparing it with the memetic and hybrid genetic algorithms. Experimental results show that SAMBO better saves processing time.
ISSN:2688-0938
DOI:10.1109/CAC63892.2024.10864756