A Genetic Algorithm for the Integrated Scheduling of Production and Transport Systems

Conference paper
Part of the Operations Research Proceedings book series (ORP)


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.


Continuous Subproblem Solution Search Space Binary Dependent Variables Feasible Offspring Machine Related Parameters 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.BIBA - Bremer Institut für Produktion und Logistik GmbH at the University of BremenBremenGermany
  2. 2.Industrial and Systems Engineering DepartmentFederal University of Santa Catarina (UFSC)Florianópolis-SCBrazil

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