![Solving a concrete sleepers production scheduling by genetic algorithms [An article from: European Journal of Operational Research]](https://www.ebooknetworking.net/books/B00/0PD/bigB000PDSH66.jpg)
Solving a concrete sleepers production scheduling by genetic algorithms [An article from: European Journal of Operational Research]
Description
This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
PRECON S.A. is a manufacturing company devoted to produce prefabricated concrete parts for several industries as railway transportation and agricultural industries. Recently, PRECON S.A. signed a contract with RENFE, the Spanish National Railway Company, to manufacture pre-stressed concrete sleepers for the sidings of the new railways of the high speed train (AVE). The scheduling problem associated with the manufacturing process of the sleepers is very complex, since this involves several constraints and objectives. These constraints are related to production capacity, the quantity of available moulds, demand satisfaction and other operational constraints. The two main objectives are related to the way to maximize the utilization of manufacturing resources and minimize mould movements. We developed a deterministic crowding genetic algorithm for this multiobjective problem. The algorithm has proved to be a powerful and flexible tool to solve large-scale instances of this real and complex scheduling problem.
Description:
PRECON S.A. is a manufacturing company devoted to produce prefabricated concrete parts for several industries as railway transportation and agricultural industries. Recently, PRECON S.A. signed a contract with RENFE, the Spanish National Railway Company, to manufacture pre-stressed concrete sleepers for the sidings of the new railways of the high speed train (AVE). The scheduling problem associated with the manufacturing process of the sleepers is very complex, since this involves several constraints and objectives. These constraints are related to production capacity, the quantity of available moulds, demand satisfaction and other operational constraints. The two main objectives are related to the way to maximize the utilization of manufacturing resources and minimize mould movements. We developed a deterministic crowding genetic algorithm for this multiobjective problem. The algorithm has proved to be a powerful and flexible tool to solve large-scale instances of this real and complex scheduling problem.
