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Simulated Annealing for Single and Mixed Model Assembly Line Balancing with Setups
conference contributionposted on 2020-01-01, 00:00 authored by Asef NazariAsef Nazari, Dhananjay ThiruvadyDhananjay Thiruvady, Atabak ElmiAtabak Elmi, Jean-Guy Schneider
Balancing and sequencing of assembly lines is the process of partitioning the assembly work into operations and to assign and schedule them to workstations in an optimal way. In particular, in response to highly competitive market conditions, manufacturers face the problem of producing several models of a base product on the assembly line, leading to a mixed-model assembly line balancing problem. This problem is proven to be NPhard and is thus computationally challenging. In this study, we tackle the mixed model assembly line problem, but additionally, we consider sequence dependant setup times between operations. We present an approach based on simulated annealing, which focuses on finding good permutations of the operations, using simple neighbourhood moves and α-Sampling. Using an efficient assignment heuristic, the operations are mapped to workstations in a greedy fashion. We conducted experiments on a range of instances, and we find that simulated annealing is more effective than a mixed integer programming model, by finding solutions to large problems in short time-frames. Furthermore, for a large number of problem instances, simulated annealing outperforms ant colony optimisation.
EventComputational Intelligence. Symposium (2020 : Canberra, Australian Capital Territory))
Pagination2762 - 2769
LocationCanberra, Australian Capital Territory
Place of publicationPiscataway, N.J.
Publication classificationE1 Full written paper - refereed
Title of proceedingsSSCI 2020 : Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence
CategoriesNo categories selected
Assembly line balancingMixed-modelSetup timesSimulated AnnealingMixed Integer ProgrammingAnt Colony OptimisationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Interdisciplinary ApplicationsEngineering, Electrical & ElectronicComputer ScienceEngineeringALGORITHMOPTIMIZATIONHYBRIDACOSTATIONSno CORE2020