A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS



A carefully designed and efficiently managed material handling system plays an important role in planning and operation of a flexible manufacturing system. Most of the researchers have addressed machine and vehicle scheduling as two independent problems and most of the research has been emphasized only on single objective optimization. Multiobjective problems in scheduling with conflicting objectives are more complex and combinatorial in nature and hardly have a unique solution. This paper addresses multiobjective scheduling problems in a flexible manufacturing environment using evolutionary algorithms. In this paper the authors made an attempt to consider simultaneously the machine and vehicle scheduling aspects in an FMS and addressed the combined problem for the minimization of makespan, mean flow time and mean tardiness objectives.


Automated guided vehicle Evolutionary algorithms Multiobjective Non-dominated solutions Scheduling 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blazewicz J, Eiselt HA, Finke G, Laporte G, Weglarz J (1991) Scheduling tasks and vehicles in a flexible manufacturing system. Int J Flex Manuf Syst 4:5–16CrossRefGoogle Scholar
  2. 2.
    Anwar MF, Nagi R (1998) Integrated scheduling of material handling and manufacturing activities for JIT production of complex assemblies. Int J Prod Res 36(3):653–681CrossRefMATHGoogle Scholar
  3. 3.
    Baker KR (1974) Introduction to sequencing and scheduling. Wiley, New YorkGoogle Scholar
  4. 4.
    Sabuncuoglu I, Hommertzheim DL (1992) Experimental investigation of FMS machine and AGV scheduling rules against the mean flow time criterion. Int J Prod Res 30(7):1617–1635Google Scholar
  5. 5.
    Gen M, Cheng R (1997) Genetic algorithms and engineering design. Wiley, New YorkGoogle Scholar
  6. 6.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, BostonMATHGoogle Scholar
  7. 7.
    Raman N, Talbot FB, Rachamadgu RV (1986) Simultaneous scheduling of machines and material handling devices in automated manufacturing. In: Proc Second ORSA/TIMS Conf.on Flexible Manufacturing SystemsGoogle Scholar
  8. 8.
    Bilge U, Ulusoy G (1995) A time window approach to simultaneous scheduling of machines and material handling system in an FMS. Opns Res 43:1058−1070MATHCrossRefGoogle Scholar
  9. 9.
    Ulusoy G, Serifoglu FS, Bilge U (1997) A genetic algorithm approach to the simultaneous scheduling of machines and automated guided vehicles. Comput Ops Res 14(4):335−351CrossRefGoogle Scholar
  10. 10.
    Abdelmaguid TF, Nassef AO, Kamal BA, Hassan MF (2004) A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles. Int J Prod Res 42:267−281CrossRefMATHGoogle Scholar
  11. 11.
    Lacomme P, Moukrim A, Tchernev N (2005) Simultaneous job input sequencing and vehicle dispatching in a single – vehicle automated guided vehicle system: a heuristic branch -and -bound approach coupled with a discrete events simulation model. Int J Prod Res 43(9):1911–1942MATHGoogle Scholar
  12. 12.
    Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. Proc First International Conference on Genetic Algorithms, pp 93–100Google Scholar
  13. 13.
    Hajela P, Lin CY (1992) Genetic search strategies in multi-criterion optimal design. Struct Optimiz 4:99–107CrossRefGoogle Scholar
  14. 14.
    Horn J, Nafpliotis N, Goldberg DE, (1994) A niched pareto genetic algorithm for multi-objective optimization. Proc First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Computation 1:82–87, Piscataway, NJGoogle Scholar
  15. 15.
    Fonseca CM, Fleming PJ (1993) Genetic algorithms for multi objective optimization: Formulation, discussion and generalization. Proc Fifth International Conference on Genetic Algorithms, San Mateo, CA, pp 416–423Google Scholar
  16. 16.
    Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248Google Scholar
  17. 17.
    Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comput 3(4):257–271CrossRefGoogle Scholar
  18. 18.
    Knowles JD, Corne DW (1999) The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimization. Congress on Evolutionary Computation (CEC99), vol. 1, Piscataway, NJ, 98–105Google Scholar
  19. 19.
    Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. KanGAL Report No. 200001, Indian Institute of Technology Kanpur, IndiaGoogle Scholar
  20. 20.
    Zitzler E, Lawmans M, Thiele L (2001) SPEA2: improving the strength pareto evolutionary algorithm. TIK-Report 103, Swiss Federal Institute of Technology (ETH), ZurichGoogle Scholar
  21. 21.
    Bagchi TP (1999) Multiobjective scheduling by genetic algorithms. Kluwer, DordrechtMATHGoogle Scholar
  22. 22.
    Deb K (2000) Multi-objective optimization using evolutionary algorithms. Wiley, New YorkMATHGoogle Scholar
  23. 23.
    Lawrence S (1984) Resource constrained project scheduling an experimental investigation of heuristic scheduling technique. Graduate School of Industrial Adminstration. Carnegie-Mellon University, Pittsburgh, PAGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  1. 1.Manufacturing simulation lab, Department of Mechanical EngineeringNational Institute of TechnologyAndhra PradeshIndia

Personalised recommendations