Applying an Iterated Greedy Algorithm to Production Programming on Manufacturing Environment Controlled by the PBC Ordering System

  • Fabio Molina da SilvaEmail author
  • Roberto Tavares Neto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11299)


Ordering systems are a mechanism used to program the flow of production orders into the manufacturing system. The correct usage and parametrization of such systems have a significant impact on the performance of the production. One of the well-succeed ordering systems available in the literature is the Period-batch control (PBC), that allows one to group the orders into different production periods, and program it into the planning horizon. This paper assumes a manufacturing system controlled by PBC. On this system, this paper considered two performance indicators: a primary goal is to minimize the total tardiness and the second goal is to minimize the idleness of the production system. Two approaches are implemented to solve this problem: a mixed-integer programming model and eight algorithms based on the Iterated Greedy method. Beyond finding good results when comparing to the ones found by the mathematical model approach, this paper also performs the Tardiness \(\times \) Production Capacity on each algorithm.


Periodic Batch Control Iterated Greedy Production planning and control Scheduling 


  1. 1.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)CrossRefGoogle Scholar
  2. 2.
    Garcia-Martinez, C., Rodriguez, F., Lozano, M.: Tabu-enhanced iterated greedy algorithm: a case study in the quadratic multiple knapsack problem. Eur. J. Oper. Res. 232(3), 454–463 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, Boston (1989)zbMATHGoogle Scholar
  4. 4.
    Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Heidelberg (2011). Scholar
  5. 5.
    Nawaz, M., Enscore, E.E., Ham, I.: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11, 91–95 (1983)CrossRefGoogle Scholar
  6. 6.
    Pan, Q.-K., Wang, L., Zhao, B.-H.: An improved iterated greedy algorithm for the no-wait flow shop scheduling problem with makespan criterion. Int. J. Adv. Manuf. Technol. 38(7–8), 778–786 (2008)CrossRefGoogle Scholar
  7. 7.
    Rodriguez, F.J., Lozano, M., Blum, C., García-Martínez, C.: An iterated greedy algorithm for the large-scale unrelated parallel machines scheduling problem. Comput. Oper. Res. 40(7), 1829–1841 (2013)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Ruiz, R., Stützle, T.: An iterated greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. Eur. J. Oper. Res. 187(3), 1143–1159 (2008)CrossRefGoogle Scholar
  9. 9.
    Ying, K.-C., Lin, S.-W., Huang, C.Y.: Sequencing single-machine tardiness problems with sequence dependent setup times using an iterated greedy heuristic. Expert Syst. Appl. 36(3), 7087–7092 (2009)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Federal University of Sao CarlosSao PauloBrazil

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