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• W2334971295 abstract "Abstract Ready Mixed Concrete (RMC) suffers from a lack of practical solutions for automatic resource allocation. Under these circumstances, RMC dispatching systems are mostly handled by experts. This paper attempts to introduce a machine learning based method to automatically match experts' decisions in RMC. For this purpose, seven machine learning techniques with their boosted algorithms were selected. A set of attributes was extracted from the collected field data. Eleven metrics were used to assess the performance of the selected techniques using different approaches. Due to concerns about randomness, significant testing was performed to assist in finding the best algorithm for this purpose. Results show that Random-Forest with 85% accuracy outperforms the other selected techniques. One of the most interesting achieved results is related to the computing time. The results show that all the selected algorithms can solve large-scale depot allocations with a very short computing time. This is possibly because a model built by a machine learning algorithm only needs to be tested with new instances, which does not need an extensive computation effort. This provides us with a chance to move toward automation in Ready Mixed Concrete Dispatching Problems (RMCDPs), especially for those RMCs with dynamic environments where resource allocation might need to be quickly recalculated during the RMC process due to changes in the system." @default.
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• W2334971295 date "2016-08-01" @default.
• W2334971295 modified "2023-09-26" @default.
• W2334971295 title "Matching experts' decisions in concrete delivery dispatching centers by ensemble learning algorithms: Tactical level" @default.
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• W2334971295 doi "https://doi.org/10.1016/j.autcon.2016.03.007" @default.
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