A Genetic Algorithm for the Integrated Scheduling of Production and Transport Systems
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The integrated scheduling of production and transport systems is a NP-hard mixed-integer problem. This paper introduces a genetic algorithm (GA) that addresses this problem by decomposing it into combinatorial and continuous subproblems. The binary variables of the combinatorial subproblem form the chromosomes of each individual. Knowledge-based evolutionary operators are deployed for reducing the solution search space. Furthermore, dependent binary variables are identified which can be efficiently determined rather by a local search than by the evolutionary process. Then, in the continuous subproblem, for fixed binary variables, the optimization problem turns into a linear program that can be efficiently solved, so that the fitness value of an individual is determined.
KeywordsContinuous Subproblem Solution Search Space Binary Dependent Variables Feasible Offspring Machine Related Parameters
This research was supported by CAPES, CNPq, FINEP and DFG as part of the Brazilian-German Collaborative Research Initiative on Manufacturing Technology (BRAGECRIM). The authors also thank Mr. Christoph Timmer for the implementation of the heuristics.
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