An adaptive genetic algorithm approach for the mixed-model assembly line sequencing problem

DOI10.1080/00207540903117857zbMath1197.90320OpenAlexW2091483924MaRDI QIDQ3163205

Semra Tunalı, Onur Serkan Akgündüz

Publication date: 25 October 2010

Published in: International Journal of Production Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207540903117857

zbMATH Keywords

sequencinggenetic algorithmmulti-objective genetic algorithmmixed-model assembly linemixed-model sequencing

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Deterministic scheduling theory in operations research (90B35) Production models (90B30)

Related Items (6)

Solving a bi-objective mixed-model assembly-line sequencing using metaheuristic algorithms considering ergonomic factors, customer behavior, and periodic maintenance ⋮ A bi-objective mixed-model assembly line sequencing problem considering customer satisfaction and customer buying behaviour ⋮ A genetic regulatory network based method for multi-objective sequencing problem in mixed-model assembly lines ⋮ Balancing and scheduling of flexible mixed model assembly lines ⋮ Optimal production sequencing problem to minimise line stoppage time in a mixed-model assembly line ⋮ Lean holistic fuzzy methodology employing cross-functional worker teams for new product development projects: a real case study from high-tech industry

Cites Work

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• Sequencing JIT Mixed-Model Assembly Lines Under Station-Load and Part-Usage Constraints
• Hybrid evolutionary multi-objective algorithms for integrating assembly sequence planning and assembly line balancing
• Level scheduling of mixed-model assembly lines under storage constraints
• A proposed approach for reconfiguration of flexible assembly line systems by motion genes
• Multi-objective assembly line balancing via a modified ant colony optimization technique
• Integrated planning for product module selection and assembly line design/reconfiguration

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