Abstract
This paper addresses the optimization of logistic processes in supply-chains using meta-heuristics: genetic algorithms and ant colony optimization. The dynamic assignment of components to orders and choosing the solution that is able to deliver more orders at the correct date, is a scheduling problem that classical scheduling methods can not cope with. However, the implementation of meta-heuristics is done only after a positive assessment of the performance’s expectation provided by the fitness-distance correlation analysis. Both meta-heuristics are then applied to a simulation example that describes a general logistic process. The performance is similar for both methods, but the ant colony optimization method provides more information at the expenses of computational costs.
This work is supported by the German Ministry of Education and Research (BMBF) under Contract no.13N7906 (project Nivelli) and by the Portuguese Foundation for Science and Technology (FCT) under Grant no. SFRH/BD/6366/2001.
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Silva, C.A., Runkler, T.A., Sousa, J.M., da Costa, J.M.S. (2003). Optimization of Logistic Processes in Supply-Chains Using Meta-heuristics. In: Pires, F.M., Abreu, S. (eds) Progress in Artificial Intelligence. EPIA 2003. Lecture Notes in Computer Science(), vol 2902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24580-3_9
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DOI: https://doi.org/10.1007/978-3-540-24580-3_9
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