Solving a concrete sleepers production scheduling by genetic algorithms

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.The authors gratefully acknowledge the financial support form VA102/04 project, Consejería de Educación y Cultura, Junta de Castilla y León, Spain

Solving a Concrete Sleepers Production Scheduling by Genetic Algorithms

PRECON S.A is a manufacturing company dedicated to produce prefabricated concrete parts to several industries as rail transportation and agricultural industries. Recently, PRECON signed a contract with RENFE, the Spanish Nnational Rail Transportation Company to manufacture pre-stressed concrete sleepers for siding 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 it involves several constraints and objectives. The constraints are related with production capacity, the quantity of available moulds, satisfying demand and other operational constraints. The two main objectives are related with maximizing the usage of the manufacturing resources and minimizing the moulds 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 the large-scale instance of this complex real scheduling problem