University of Michigan-Dearborn

Assembly Line

The Multi Crane Scheduling Problem: A Comparison Between Genetic Algorithms and Neural Network approaches based on Simulation Modeling


Authors: Naomie Bartoli, Roberto Revetria, Emanuele Morra, Gabriele Galli, Edward Williams

Topics: Simulation, Genetic Algorithm, Multilayer Perceptron

Organizations: University of Genoa, University of Michigan-Dearborn, PMC

The internal logistics for warehouses of many industrial applications, based on the movement of heavy goods, is commonly solved by the installment of a multi-crane system. The job scheduling of a multi-crane is an interesting problem of optimization, solved in many ways in the past: this paper describes a comparison between the optimization by the use of Genetic Algorithms and the machine learning piloting driven by Neural Networks. A case-study for steel coil production is proposed as a test frame for two different simulation software tools, one based on heuristic solution and one on machine learning; performances and data achieved from reviews and simulations are compared.

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