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An adaptive multi-population genetic algorithm to solve the multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times

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Abstract

In this paper we consider a multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times by minimizing total weighted tardiness and maximum completion time simultaneously. Whereas these kinds of problems are NP-hard, thus we proposed a multi-population genetic algorithm (MPGA) to search Pareto optimal solution for it. This algorithm comprises two stages. First stage applies combined objective of mentioned objectives and second stage uses previous stage’s results as an initial solution. In the second stage sub-population will be generated by re-arrangement of solutions of first stage. To evaluate performance of the proposed MPGA, it is compared with two distinguished benchmarks, multi-objective genetic algorithm (MOGA) and non-dominated sorting genetic algorithm II (NSGA-II), in three sizes of test problems: small, medium and large. The computational results show that this algorithm performs better than them.

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References

  • Allahverdi A., Gupta J. N. D., Aldowaisan T. (1999) A survey of scheduling research involving setup considerations, OMEGA. International Journal of Management Science 27: 219–239

    Google Scholar 

  • Baker K. R. (1988) Scheduling the production of components at a common facility. IIE Transactions 20: 32–35

    Article  Google Scholar 

  • Baker K. R. (1990) Scheduling groups of jobs in the two-machine flow shop. Mathematical and Computer Modeling 13: 29–36

    Article  Google Scholar 

  • Bean J. C. (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing 6: 154–160

    Google Scholar 

  • Beausoleil R. P. (2006) “MOSS” multiobjective scatter search applied to non-linear multiple criteria optimization. European Journal of Operational Research 169: 426–449

    Article  Google Scholar 

  • Behnamian J., Zandieh M., Fatemi Ghomi S. M. T. (2009) Due windows group scheduling using an effective hybrid optimization approach. International Journal of Advanced Manufacturing Technology 44(7): 795–808

    Article  Google Scholar 

  • Burbidge J. L. (1975) The introduction of group technology. Heinemann, London

    Google Scholar 

  • Cavalieri S., Gaiardelli P. (1998) Hybrid genetic algorithms for a multiple-objective scheduling problem. Journal of intelligent manufacturing 9: 361–367

    Article  Google Scholar 

  • Cetinkaya F. C., Kayaligil M. S. (1992) Unit sized transfer batch scheduling with setup times. Computers and Industrial Engineering 22: 177–183

    Article  Google Scholar 

  • Cheng T. C. E., Gupta J. N. D., Wang G. (2000) A review of flowshop scheduling research with setup times. Production and Operations Management 9: 283–302

    Google Scholar 

  • Cochran, J. K., Horng, S. M., & Fowler, J. W. (2003). A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines. Computers and Operations Research, 30, 1087–1102.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Deb K., Pratap A., Agarwal S., Meyarivan T. (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2): 182–197

    Article  Google Scholar 

  • Fonseca, C. M., & Fleming, P. J. (1993). Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In S. Forrest (Ed.), Proceedings of the fifth international conference on genetic algorithms (pp. 416–423). Morgan Kaufman Publishers San Mateo, California University of Illinois at Urbana-Champaign.

  • Franca P. M., Gupta J. N. D., Mendes A., Moscato P., Veltink K. J. (2005) Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups. Computers and Industrial Engineering 48: 491–506

    Article  Google Scholar 

  • Gunaraj V., Murugan N. (1999) Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes. Journal of Materials Processing Technology 88: 266–275

    Article  Google Scholar 

  • Gupta J. N. D., Darrow W. P. (1985) Approximate schedules for the two-machine flowshop with sequence dependent setup times. Indian Journal of Management and Systems 1: 6–11

    Google Scholar 

  • Gupta J. N. D., Darrow W. P. (1986) The two-machine sequence dependent flowshop scheduling problem. European Journal of Operational Research 24: 439–446

    Article  Google Scholar 

  • Ham I., Hitomi K., Yoshida T. (1985) Group technology: Applications to production management. Kluwer, Hingham, MA

    Google Scholar 

  • Hou T. H., Su C. H., Chang H. Z. (2008) An integrated multi-objective immune algorithm for optimizing the wire bonding process of integrated circuits. Journal of intelligent manufacturing 19: 361–374

    Article  Google Scholar 

  • Hyun C. J., Kim Y., Kin Y. K. (1998) A genetic algorithm for multiple objective sequencing problems in mixedmodel assembly. Computers & Operations Research 25: 675–690

    Article  Google Scholar 

  • Janiak A., Kovalyov M. Y., Portmann M. C. (2005) Single machine group scheduling with resource dependent setup and processing times. European Journal of Operational Research 162: 112–121

    Article  Google Scholar 

  • Kim Y. D. (1993) Heuristics for flowshop scheduling problems minimizing mean tardiness. Journal of Operational Research Society 44: 19–28

    Google Scholar 

  • Kim Y. D., Lim H. G., Park M. W. (1996) Search heuristics for a flowshop scheduling problem in a printed circuit board assembly process. European Journal of Operational Research 91: 124– 143

    Article  Google Scholar 

  • Knowles J. D., Corne D. W. (2000) Approximating the nondominated front using the Pareto archived evolution strategy. Evolutionary Computation 8(2): 149–172

    Article  Google Scholar 

  • Kuo W., Yang D. (2006) Single-machine group scheduling with a time-dependent learning effect. Computers & Operations Research 33: 2099–2112

    Article  Google Scholar 

  • Kurz M. E., Askin R. G. (2004) Scheduling flexible flow lines with sequence-dependent setup times. European Journal of Operational Research 159: 66–82

    Article  Google Scholar 

  • Kusiak A. (1987) The generalized group technology concept. International Journal of Production Research 25: 561–569

    Article  Google Scholar 

  • Leu B. Y., Nazemetz J. W. (1995) Comparative analysis of group scheduling heuristics in a flow shop cellular system. International Journal of Operations & Production Management 15((9): 143–157

    Article  Google Scholar 

  • Liaee M. M., Emmons H. (1997) Scheduling families of jobs with setup times. International Journal of Production Economics 51: 165–176

    Article  Google Scholar 

  • Liu Z., Yu W. (1999) Minimizing the number of late jobs under the group technology assumption. Journal of Combinatorial Optimization 3: 5–15

    Article  Google Scholar 

  • Logendran R., Mai L., Talkington D. (1995) Combined heuristics for bi-level group scheduling problems. International Journal of Production Economics 38: 133–145

    Article  Google Scholar 

  • Logendran R., Carson S., Hanson E. (2005) Group scheduling in flexible flow shops. International Journal of Production Economics 96(2): 143–155

    Article  Google Scholar 

  • Logendran R., deSzoeke P., Barnard F. (2006a) Sequence-dependent group scheduling problems in flexible flow shops. International Journal of Production Economics 102: 66–86

    Article  Google Scholar 

  • Logendran R., Salmasi N., Sriskandarajah C. (2006b) Two-machine group scheduling problems in discrete parts manufacturing with sequence-dependent setups. Computers & Operations Research 33: 158–180

    Article  Google Scholar 

  • Mansini R., Grazia Speranza M., Tuza Z. (2004) Scheduling groups of tasks with precedence constraints on three dedicated processors. Discrete Applied Mathematics 134: 141–168

    Article  Google Scholar 

  • Mitrofanov S. P. (1966) Scientific principles of group technology. National Lending Library, London (English Translation Boston Spa)

    Google Scholar 

  • Murata T., Ishibuchi H., Tanaka H. (1996) Multi-objective genetic algorithm and its application to flowshop scheduling. Computers & Industrial Engineering 30: 957–968

    Article  Google Scholar 

  • Naderi, B., Zandieh, M., & Fatemi Ghomi, S. M. (2008). A study on integrating sequence dependent setup time flexible flow lines and preventive maintenance scheduling. Journal of intelligent manufacturing. doi:10.1007/s10845-008-0157-6.

  • Reddy V., Narendran T. T. (2003) Heuristics for scheduling sequence dependent set-up jobs in flow line cells. International Journal of Production Research 41(1): 193–206

    Article  Google Scholar 

  • Schaffer, J. D. (1985). Multiple objective optimization with vector evaluated genetic algorithms. In Proceedings of the first ICGA (pp. 93–100).

  • Schaller J. E., Gupta J. N. D., Vakharia A. J. (2000) Scheduling a flowline manufacturing cell with sequence dependent family setup times. European Journal of Operational Research 125: 324–339

    Article  Google Scholar 

  • Sekiguchi Y. (1983) Optimal schedule in a GT-type flow-shop under series-parallel precedence constraints. Journal of the Operations Research Society of Japan 26: 226–251

    Google Scholar 

  • Tavakkoli-Moghaddam R., Rahimi-Vahed A. R., Mirzaei A. H. (2008) Solving a multi-objective no-wait flow shop scheduling problem with an immune algorithm. International Journal of Advanced Manufacturing Technology 36(9-10): 969–981

    Article  Google Scholar 

  • Vickson R. G., Alfredsson B. E. (1992) Two- and three-machine flow shop scheduling problems with equal sized transfer batches. International Journal of Production Research 30: 1551–1574

    Article  Google Scholar 

  • Wilson A. D., King R. E., Hodgson T. J. (2004) Scheduling non-similar groups on a flow line: Multiple group setups. Robotics and Computer-Integrated Manufacturing 20: 505–515

    Article  Google Scholar 

  • Yang D., Chern M. S. (2000) Two-machine flowshop group scheduling problem. Computers and Operations Research 27: 975–985

    Article  Google Scholar 

  • Zandieh, M., Dorri, B., & Khamseh, A. R. (2008). Robust metaheuristics for group scheduling with sequence-dependent setup times in hybrid flexible flow shops. International Journal of Advanced Manufacturing Technology. doi:10.1007/s00170-008-1740-x.

  • Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. Technical report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland.

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Authors and Affiliations

  1. Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G. C., Tehran, Iran

    M. Zandieh

  2. Faculty of Industrial and Mechanical Engineering, Qazvin Islamic Azad University, Qazvin, Iran

    N. Karimi

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  1. M. Zandieh
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  2. N. Karimi
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Correspondence to M. Zandieh.

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Zandieh, M., Karimi, N. An adaptive multi-population genetic algorithm to solve the multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times. J Intell Manuf 22, 979–989 (2011). https://doi.org/10.1007/s10845-009-0374-7

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  • DOI: https://doi.org/10.1007/s10845-009-0374-7

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