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Solving a multi-objective no-wait flow shop scheduling problem with an immune algorithm

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Abstract

Flow shop scheduling problems have gained wide attention both in practical and academic fields. In this paper, we consider a multi-objective no-wait flow shop scheduling problem by minimizing the weighted mean completion time and weighted mean tardiness simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in a strong sense, an effective immune algorithm (IA) is proposed for searching locally the Pareto-optimal frontier for the given problem. To validate the performance of the proposed algorithm in terms of solution quality and diversity level, various test problems are carried out and the efficiency of the proposed algorithm, based on some comparison metrics, is compared with a prominent multi-objective genetic algorithm, i.e., strength Pareto evolutionary algorithm II (SPEA-II). The computational results show that the proposed IA outperforms the above genetic algorithm, especially for large problems.

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References

  1. Solimanpur M, Vrat P, Shankar R (2004) A neuro-tabu search heuristic for flow shop scheduling problem. Comput Oper Res 31(13):2151–2164

    Article  MATH  Google Scholar 

  2. Pinedo M (1995) Scheduling: theory algorithms and systems. Prentice-Hall, Englewood Cliffs, New Jersey

    MATH  Google Scholar 

  3. Gupta JND, Stafford EF Jr (2006) Flow shop scheduling research after five decades. Eur J Oper Res 169(3):699–711

    Article  MATH  Google Scholar 

  4. Murata T, Ishibuchi H, Tanaka H (1996) Multi-objective genetic algorithm and its applications to flow shop scheduling. Comput Ind Eng 30(4):957–968

    Article  Google Scholar 

  5. Pan JCH, Chen JS, Chao CM (2002) Minimizing tardiness in a two-machine flow shop. Comput Oper Res 29(7):869–885

    Article  MATH  MathSciNet  Google Scholar 

  6. Fink A, Vob S (2003) Solving the continuous flow shop scheduling problem by meta-heuristics. Eur J Oper Res 151(2):400–414

    Article  MATH  Google Scholar 

  7. Bulfin RL, M’Hallah R (2003) Minimizing the weighted number of tardy jobs on a two-machine flow shop. Comput Oper Res 30(12):1887–1900

    Article  MATH  MathSciNet  Google Scholar 

  8. Blazewicz J, Pesch E, Sterna M, Werner F (2005a) A comparison of solution procedures for two-machine flow shop scheduling with late work criterion. Comput Ind Eng 49(9):611–624

    Article  Google Scholar 

  9. Choi BC, Yoon SH, Chung SJ (2005) Minimizing maximum completion time in a proportionate flow shop with one machine of different speed. Eur J Oper Res 176(2):964–974

    Article  MathSciNet  Google Scholar 

  10. Grabowski J, Pempera J (2005) Some local search algorithms for no-wait flow shop problem with makespan criterion. Comput Oper Res 32(8):2197–2212

    Article  MATH  MathSciNet  Google Scholar 

  11. Blazewicz J, Pesch E, Sterna M, Werner F (2005b) The two-machine flow shop problem with weighted late work criterion and common due date. Eur J Oper Res 165(2):408–415

    Article  MATH  MathSciNet  Google Scholar 

  12. Wang JB, Daniel Ng CT, Cheng TCE, Liu LL (2006) Minimizing total completion time in a two-machine flow shop with deteriorating jobs. Appl Math Comput 180(1):185–193

    Article  MATH  MathSciNet  Google Scholar 

  13. Nowicki E, Smutnicki C (2006) Some aspects of scatter search in the flow shop problem. Eur J Oper Res 169(2):654–666

    Article  MATH  MathSciNet  Google Scholar 

  14. Lian Z, Gu X, Jiao B (2006) A similar particle swarm optimization algorithm for permutation flow shop scheduling to minimize makespan. Appl Math Comput 175(1):773–785

    Article  MATH  MathSciNet  Google Scholar 

  15. Toktas B, Azizoglu M, Koksalan SK (2004) Two-machine flow shop scheduling with two criteria: Maximum earliness and makespan. Eur J Oper Res 157(2):286–295

    Article  MATH  Google Scholar 

  16. Marett R, Wright M (1996) A comparison of neighborhood search techniques for multi-objective combinatorial problems. Comput Oper Res 23(5):465–483

    Article  MATH  Google Scholar 

  17. Sayin S, Karabati S (1999) A bicriteria approach to the two-machine flow shop scheduling problem. Eur J Oper Res 113(2):435–449

    Article  MATH  MathSciNet  Google Scholar 

  18. Danneberg D, Tautenhahn T, Werner F (1999) A comparison of heuristic algorithms for flow shop scheduling problems with setup times and limited batch size. Math Comput Model 29(9):101–126

    Article  MATH  MathSciNet  Google Scholar 

  19. Ponnambalam SG, Jagannathan H, Kataria M, Gadicherla A (2004) A TSP-GA multi-objective algorithm for flow shop scheduling. Int J Adv Manuf Technol 23(11–12):909–915

    Google Scholar 

  20. Ravindran D, Noorul Haq A, Selvakuar SJ, Sivaraman R (2005) Flow shop scheduling with multiple objective of minimizing makespan and total flow time. Int J Adv Manuf Technol 25(9–10):1007–1012

    Article  Google Scholar 

  21. Loukil T, Teghem J, Tuyttens D (2005) Solving multi-objective production scheduling problems using meta-heuristics. Eur J Oper Res 161(1):42–61

    Article  MATH  MathSciNet  Google Scholar 

  22. Collette Y, Siarry P (2003) Multiobjective optimization: Principles and case studies. Springer, Berlin Heidelberg New York

  23. Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: JD Schaffer (ed) Genetic algorithms and their applications: Proceedings of the first international conference on genetic algorithms. Lawrence Erlbaum, Hillsdale, New Jersey, pp 93–100

    Google Scholar 

  24. Fonseca CM, Fleming PJ (1993) Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization. In: Forrest S (ed) Proc. of the fifth international conference on genetic algorithms. Morgan Kaufman Publishers, San Mateo, California, University of Illinois at Urbana-Champaign, pp 416–423

    Google Scholar 

  25. Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multi-objective optimization. In: Proceedings of the first IEEE conference on evolutionary computation. IEEE World Congress on Computational Intelligence, Orlando, FL, USA, pp 82–87, June 1994

  26. Deb K (1999) Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evol Comput J 7(3):205–230

    Article  Google Scholar 

  27. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  28. Hyun CJ, Kim Y, Kim YK (1998) A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines. Comput Oper Res 25(7–8):675–690

    Article  MATH  Google Scholar 

  29. Jaszkiewicz A (1999) Genetic local search for multiple objective combinatorial optimization, Technical Report RA-014/98, Institute of Computing Science, Poznan University of Technology

  30. Zitzler E, Thiele L (1998) An evolutionary algorithm for multiobjective optimization: The strength Pareto approach. Technical Report 43, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland

  31. Zitzler E, Laumanns M, Thiele L (2001a) SPEA2: Improving the strength Pareto evolutionary algorithm. In: Giannakoglou K, Tsahalis D, Periaux J, Papailou P, Fogarty T (eds) EUROGEN 2001, Evolutionary methods for design, Optimization and control with applications to industrial problems, Athens, Greece, pp 95–100

  32. Coello Coello CA, Toscano Pulido G (2001) A micro-genetic algorithm for multiobjective optimization. In: Zitzler E, Deb K, Thiele L, Coello Coello CA, Corne D (eds) First international conference on evolutionary multi-criterion optimization, Lecture notes in computer sciences, No. 1993, Springer-Verlag, Berlin Heidelberg New York, pp 126–140

  33. Knowles JD, Corne DW (1999) The Pareto archived evolution strategy: A new baseline algorithm for multiobjective optimization. In: Proceedings of the 1999 Congress on evolutionary computation, CEC 99, Washington, DC, USA, pp 98–105

  34. Pilegaard Hansen M (1997) Tabu search in multiobjective optimization: MOTS. In: Proc. of the 13th international conference on Multiple Criteria Decision Making (MCDM 97). Cape Town, South Africa

  35. Beausoleil RP (2006) “MOSS” multiobjective scatter search applied to non-linear multiple criteria optimization. Eur J Oper Res 169:426–449

    Article  MATH  MathSciNet  Google Scholar 

  36. Aickelin U, Dasgupta D (2005) Artificial immune systems tutorial, to appear in introductory tutorials in optimization, decision support and search methodology. E Burke, G Kendall (eds) Kluwer

  37. Khoo LP, Situmdrang TD (2003) Solving the assembly configuration problem for modular products using an immune algorithm approach. Int J Prod Res 41(15):3419–3434

    Article  MATH  Google Scholar 

  38. Ada GL, Nossal GJV (1987) The clonal selection theory. Sc Am 257(2):50–57

    Google Scholar 

  39. Engin O, Doyen A (2004) A new approach to solve hybrid flow shop scheduling problems by artificial immune system. Future Gener Comput Syst 20(6):1083–1095

    Article  Google Scholar 

  40. Zandieh M, Fatemi Ghomi SMT, Moattar Husseini SM (2006) An immune algorithm approach to hybrid flow shops scheduling with sequence-dependent setup times. Appl Math Comput 180(1):111–127

    Article  MATH  MathSciNet  Google Scholar 

  41. Dasgupta D, Forrest S (1995) Tool Breakage Detection in Milling Operations using a Negative-Selection Algorithm. Technical Report No. CS95-5, Department of Computer Science, University of New Mexico, Download URL: ftp://ftp.cs.unm.edu/pub/cs_tech_reports/1995/cs95-5.ps.z

  42. De Castro LN, Von Zuben FJ (2000) The clonal selection algorithm with engineering applications. In: Workshop Proc. of GECCO’00, Workshop on artificial immune systems and their applications, Las Vegas, USA, pp 36–37

  43. De Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3):239–251

    Article  Google Scholar 

  44. Luh GC, Chueh CH, Liu WW (2003) Moia: multi-objective immune algorithm. Eng Optim 35(2):143–164

    Article  MathSciNet  Google Scholar 

  45. Coello Coello CA, Cortes NC (2005) Solving multiobjective optimization problems using an artificial immune system. Genet Program Evolvable Machines 6(2):163–190

    Article  Google Scholar 

  46. Alisantoso DL, Khoo PP, Jiang Y (2003) An immune algorithm approach to the scheduling of a flexible PCB flow shop. Int J Adv Manuf Technol 22(11–12):819–827

    Article  Google Scholar 

  47. Tasgetiren MF, Sevkli M, Liang YC, Gencyilmaz G (2004) Particle swarm optimization algorithm for single machine total weighted tardiness problem. In: Proc. of the IEEE congress on evolutionary computation, Oregon: Portland, pp 1412–1419

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, University of Tehran, P.O. Box 11365/4563, Tehran, Iran

    R. Tavakkoli-Moghaddam & A. R. Rahimi-Vahed

  2. Department of Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, P.O. Box 14115/111, Tehran, Iran

    A. H. Mirzaei

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  1. R. Tavakkoli-Moghaddam
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  2. A. R. Rahimi-Vahed
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  3. A. H. Mirzaei
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Correspondence to R. Tavakkoli-Moghaddam.

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Tavakkoli-Moghaddam, R., Rahimi-Vahed, A.R. & Mirzaei, A.H. Solving a multi-objective no-wait flow shop scheduling problem with an immune algorithm. Int J Adv Manuf Technol 36, 969–981 (2008). https://doi.org/10.1007/s00170-006-0906-7

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  • DOI: https://doi.org/10.1007/s00170-006-0906-7

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