Sequencing to minimize work overload in assembly lines with product options
Management Science
Algorithms for sequencing mixed models on an assembly line in a JIT production system
Computers and Industrial Engineering
A genetic alorithm for multiple objective sequencing problems in mixed model assembly lines
Computers and Operations Research
Designing a mixed-model assembly line to minimize the costs of idle and utility times
Computers and Industrial Engineering
ACM Computing Surveys (CSUR)
Pattern Based Vocabulary Building for Effectively SequencingMixed-Model Assembly Lines
Journal of Heuristics
Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Computers and Industrial Engineering
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
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We address a mixed-model assembly-line sequencing problem with work overload minimization criteria. We consider time windows in work stations of the assembly line (closed stations) and different versions of a product to be assembled in the line, which require different processing time according to the work required in each work station. In a paced assembly line, products are feeded in the line at a predetermined constant rate (cycle time). Then, if many products with processing time greater than cycle time are feeded consecutively, work overload can be produced when the worker has insufficient time to finish his/her job. We propose a scatter search based hyper-heuristic for this NP-hard problem. In the low-level, the procedure makes use of priority rules through a constructive procedure. Computational experiments over a wide range of instances from the literature show the effectiveness of the proposed hyper-heuristics when compared to existing heuristics. The relevance of the priority rules was evaluated as well.