An efficient grouping genetic algorithm for U-shaped assembly line balancing problems with maximizing production rate

Simple item page
dc.contributor.authorŞahin M.
dc.contributor.authorKellegöz T.
dc.date.accessioned2024-07-22T08:10:37Z
dc.date.available2024-07-22T08:10:37Z
dc.date.issued2017
dc.description.abstractU-type assembly line is one of the important tools that may increase companies’ production efficiency. In this study, two different modeling approaches proposed for the assembly line balancing problems have been used in modeling type-II U-line balancing problems, and the performances of these models have been compared with each other. It has been shown that using mathematical formulations to solve medium and large size problem instances is impractical since the problem is NP-hard. Therefore, a grouping genetic and simulated annealing algorithms have been developed, and a particle swarm optimization algorithm is adapted to compare with the proposed methods. A special crossover operator that always obtains feasible offspring has been suggested for the proposed grouping genetic algorithm. Furthermore, a local search procedure based on problem-specific knowledge was applied to increase the intensification of the algorithm. A set of well-known benchmark instances was solved to evaluate the effectiveness of the proposed and existing methods. Results showed that while the mathematical formulations can only be used to solve small size instances, metaheuristics can obtain high quality solutions for all size problem instances within acceptable CPU times. Moreover, grouping genetic algorithm has been found to be superior to the other methods according to the number of optimal solutions, or deviations from the lower bound values. © 2017, Springer-Verlag GmbH Germany.
dc.identifier.DOI-ID10.1007/s12293-017-0239-0
dc.identifier.issn18659284
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/15315
dc.language.isoEnglish
dc.publisherSpringer Verlag
dc.subjectAssembly machines
dc.subjectBenchmarking
dc.subjectGenetic algorithms
dc.subjectOptimization
dc.subjectParticle swarm optimization (PSO)
dc.subjectProduction
dc.subjectSimulated annealing
dc.subjectAssembly line balancing
dc.subjectAssembly line balancing problems
dc.subjectGenetic and simulated annealing algorithms
dc.subjectGrouping genetic algorithms
dc.subjectMathematical formulation
dc.subjectProblem-specific knowledge
dc.subjectProduction rates
dc.subjectU-shaped assembly lines
dc.subjectAssembly
dc.titleAn efficient grouping genetic algorithm for U-shaped assembly line balancing problems with maximizing production rate
dc.typeArticle

Files

Collections

Scopus Koleksiyonu