Mixed-model assembly line balancing problem considering learning effect and uncertain demand

DOI10.1016/J.CAM.2022.114823OpenAlexW4306319250MaRDI QIDQ2104042

Dan Liu, Yuchen Li, Ibrahim Kucukkoc

Publication date: 9 December 2022

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2022.114823

zbMATH Keywords

variable neighborhood searchmixed-model assembly line balancinguncertain demandlearning effect

Mathematics Subject Classification ID

Mixed integer programming (90C11) Stochastic programming (90C15) Approximation methods and heuristics in mathematical programming (90C59) Production models (90B30) Combinatorial optimization (90C27)

Uses Software

• SALBPGen
• TUALB

Cites Work

• Systematic data generation and test design for solution algorithms on the example of SALBPGen for assembly line balancing
• Balancing mixed-model assembly lines using adjacent cross-training in a demand variation environment
• An optimization algorithm for solving the rich vehicle routing problem based on variable neighborhood search and tabu search metaheuristics
• Variable neighborhood search
• Benders' decomposition for the balancing of assembly lines with stochastic demand
• A simheuristic approach for throughput maximization of asynchronous buffered stochastic mixed-model assembly lines
• State-of-the-art exact and heuristic solution procedures for simple assembly line balancing
• Optimal allocation of work in assembly lines for lots with homogeneous learning
• Integrated real-time control of mixed-model assembly lines and their part feeding processes
• Multi-item capacitated lot-sizing with demand uncertainty
• A two-stage heuristic method for balancing mixed-model assembly lines with parallel workstations

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