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No-wait flexible flowshop with uniform parallel machines and sequence-dependent setup time: a hybrid meta-heuristic approach

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Abstract

According to the state of the art of no-wait scheduling problem, practitioners have mostly concentrated on pure no-wait flow shop scheduling problem. In the most real world production cases, flow shops operate with uniform parallel machines at each stage to eliminate or reduce the bottleneck stages with aim of enhancing the efficiency of production. This paper deals with a no-wait scheduling problem considering anticipatory sequence-dependent setup times on the flexible flow shop environment with uniform parallel machines. The objective is to find the sequence which minimizes maximum completion time of jobs (i.e. makespan). Since this problem is known to be NP-hard, we introduce a novel approach to tackle the problem. In the solution approach, firstly a heuristic formulation is used for objective function evaluation. Afterwards, principles of meta-heuristic algorithms namely invasive weed optimization, variable neighborhood search and simulated annealing algorithms are hybridized as solution method of the problem. In addition, a Taguchi method is employed for calibration of parameters and operators of the proposed hybrid meta-heuristic. Various computational experiments in two scales of small and large are established to illustrate the effectiveness and robustness of the proposed method. Finally, the experimental results revealed the superiority of the performance of the hybrid meta-heuristic in comparison with original ones singularly.

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Abbreviations

IWO:

Invasive weed optimization

VNS:

Variable neighborhood search

SA:

Simulated annealing

ACO:

Ant colony optimization

GA:

Genetic algorithm

SIWOVNSSA:

Semi invasive weed optimization-variable neighborhood search-simulated annealing

FFS:

Flexible flow shop

NWFFSSP:

No-wait flexible flow shop scheduling problem

MDA:

Minimum deviation algorithm

LPT:

Latest processing time

SDST:

Sequence dependent setup times

NSDST:

Non-anticipatory sequence dependent setup time

ASDST:

Anticipatory sequence-dependent setup times

RPD:

Relative percentage deviation

ARPD:

Average relative percentage deviation

FFE:

Fractional factorial experiment

OA:

Orthogonal array

LSD:

Least significant difference

References

  • Ajorlou, S., & Shams, I. (2012). Artificial bee colony algorithm for CONWIP production control system in a multi-product multi-machine manufacturing environment. Journal of Intelligent Manufacturing. doi:10.1007/s10845-012-0646-5.

  • Aldowaisan, T. A. (2001). A new heuristic and dominance relations for no-wait flowshops with setup times. Computers and Operations Research, 28(6), 563–584.

    Article  Google Scholar 

  • Allahverdi, A. (1997). Scheduling in stochastic flowshops with independent setup, processing and removal times. Computers and Operations Research, 24(11), 995–960.

    Google Scholar 

  • Allahverdi, A., & Aldowaisan, T. (2001). Minimizing total completion time in a no-wait flowshop with sequence-dependent additive changeover times. Journal of the Operational Research Society, 52, 449–462.

    Article  Google Scholar 

  • Arnaout, J.-P., Musa, R., & Rabadi, G. (2012). A two-stage Ant Colony optimization algorithm to minimize the makespan on unrelated parallel machines—part II: Enhancements and experimentations. Journal of Intelligent Manufacturing. doi:10.1007/s10845-012-0672-3.

  • Baker, K. (1974). Introduction to sequence and scheduling. New York: Wiley.

    Google Scholar 

  • Belmecheri, F., Prins, C., Yalaoui, F., & Amodeo, L. (2012). Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows. Journal of intelligent manufacturing. doi:10.1007/s10845-012-0627-8.

  • Bertel, S., & Billaut, J. (2004). A genetic algorithm for an industrial multiprocessor flow shop scheduling problem with recirculation. European Journal of Operational Research, 159(3), 651–62.

    Article  Google Scholar 

  • Blum, C., Puchinger, J., Raidl, G. R., & Roli, A. (2011). Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing, 11, 4135–4151.

    Article  Google Scholar 

  • Cochran, W. G., & Cox, G. M. (1992). Experimental designs (2nd ed.). New York: Wiley.

    Google Scholar 

  • Conway, R., & Maxwell, W. (1967). Theory of scheduling. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Dessouky, M. M., Dessouky, M. I., & Verma, S. K. (1998). Flowshop scheduling with identical jobs and uniform parallel machines. European Journal of Operational Research, 109(3), 620–31.

    Article  Google Scholar 

  • Eren, T., & Güner, T. E. (2006). A bicriteria scheduling with sequence-dependent setup times. Applied Mathematics and Computation, 179, 378–385.

    Article  Google Scholar 

  • Gupta, J. N. D., Strusevich, V. A., & Zwaneveld, C. (1997). Two-stage no-wait scheduling models with setup and removal times. Computers and Operations Research, 24(11), 1025–1031.

    Article  Google Scholar 

  • Hansen, P., & Mladenovic, N. (2001). Variable neighborhood search: Principles and applications. European Journal of Operational Research, 130, 449–467.

    Article  Google Scholar 

  • Huang, W., & Li, S. (1998). A two-stage hybrid flowshop with uniform machines and setup times. Mathematical and Computer Modelling, 27(2), 27–45.

    Article  Google Scholar 

  • Huang, R. H., Yang, C. L., & Huang, Y. (2009). No-wait two-stage multiprocessor flow shop scheduling with unit setup. International Journal of Advanced Manufacturing Technology, 44, 921–927.

    Article  Google Scholar 

  • Jolai, F., Sheikh, S., Rabbani, M., & Karimi, B. (2009). A genetic algorithm for solving no-wait flexible flow lines with due window and job rejection. The International Journal of Advanced Manufacturing Technology, 42(5), 523–532.

    Article  Google Scholar 

  • Karimkashi, S., & Kishk, A. (2010). Invasive weed optimization and its features in electromagnetics. IEEE Transactions on Antennas and Propagation, 58(4), 1269–1278.

    Article  Google Scholar 

  • Kyparisis, G. J., & Koulamas, C. (2006a). A note on makespan minimization in two-stage flexible flow shops with uniform machines. European Journal of Operational Research, 175(2), 1321–1327.

    Google Scholar 

  • Kyparisis, G. J., & Koulamas, C. (2006b). Flexible flow shop scheduling with uniform parallel machines. European Journal of Operational Research, 168(3), 985–997.

    Article  Google Scholar 

  • Li, X., Yalaoui, F., Amodeo, L., & Chehade, H. (2012). Metaheuristics and exact methods to solve a multiobjective parallel machines scheduling problem. Journal of Intelligent Manufacturing, 23(4), 1179–1194.

    Article  Google Scholar 

  • Lozanoa, M., & García-Martínez, C. (2010). Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: Overview and progress report. Computers & Operations Research, 37, 481–497.

    Article  Google Scholar 

  • Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 1, 355–366.

    Article  Google Scholar 

  • Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092.

    Article  Google Scholar 

  • Mirabi, M., Ghomi, S. F., & Jolai, F. (2013). A two-stage hybrid flowshop scheduling problem in machine breakdown condition. Journal of Intelligent Manufacturing, 24(1), 193–199.

    Article  Google Scholar 

  • Mirsanei, H. S., Zandieh, M., Moayed, M. J., & Khabbazi, M. R. (2011). A simulated annealing algorithm approach to hybrid flow shop scheduling with sequence-dependent setup times. Journal of Intelligent Manufacturing, 22(6), 965–978.

    Article  Google Scholar 

  • Moradinasab, N., Shafaei, R., Rabiee, M., & Ramezani, P. (2012). No-wait two stage hybrid flow shop scheduling with genetic and adaptive imperialist competitive algorithms. Journal of Experimental & Theoretical Artificial Intelligence. doi:10.1080/0952813X.2012.682752.

  • Panwalkar, S. S., Dudek, R. A., & Smith, M. L. (1973). Sequencing research and the industrial scheduling problem. In Elmaghra, S. E. (Ed.) Symposium on the theory of scheduling and its applications. New York: Springer, p. 29. International Journal of Advanced Manufacturing Technology, 49, 263–279 (2010).

  • Rebai, M., Kacem, I., & Adjallah, K. H. (2012). Earliness-tardiness minimization on a single machine to schedule preventive maintenance tasks: Metaheuristic and exact methods. Journal of Intelligent Manufacturing, 23(4), 1207–1224.

    Article  Google Scholar 

  • Ross, R. J. (1989). Taguchi techniques for quality engineering. New York: McGraw-Hill.

    Google Scholar 

  • Rossi, A., Puppato, A., & Lanzetta, M. (2012). Heuristics for scheduling a two-stage hybrid flow shop with parallel batching machines: Application at a hospital sterilisation plant. International Journal of Production Research. doi:10.1080/00207543.2012.737942.

  • Roy, G. G., Das, S., Chakraborty, P., & Suganthan, P. N. (2011). Design of non-uniform circular antenna arrays using a modified invasive weed optimization algorithm. IEEE Transactions on Antennas and Propagation, 59(1), 110–118.

    Article  Google Scholar 

  • Ruiz, R., & Maroto, C. (2006). A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility. European Journal of Operational Research, 169(3), 781–800.

    Article  Google Scholar 

  • Salvador, M. S. (1973). A solution to a special case of flow shop scheduling problems. In Symposium of the theory of scheduling and applications (pp. 83–91). NewYork: Springer.

  • Sevastianov, S. V. (2002). Geometrical heuristics for multiprocessor flowshop scheduling with uniform machines at each stage. Journal of Scheduling, 5(3), 205–25.

    Article  Google Scholar 

  • Soewandi, H., & Elmaghraby, S. E. (2003). Sequencing on two-stage hybrid flowshops with uniform machines to minimize makespan. IIE Transactions, 35(5), 467–477.

    Article  Google Scholar 

  • Sriscandarajah, C., & Ladet, P. (1986). Some no-wait shops scheduling problems: Complexity aspects. European Journal of Operational Research, 24, 424–445.

    Article  Google Scholar 

  • Tang, L., Liu, W., & Liu, J. (2005). A neural network model and algorithm for the hybrid flow shop scheduling problem in a dynamic environment. Journal of Intelligent Manufacturing, 16(3), 361–370.

    Article  Google Scholar 

  • Tavakkoli-Moghaddam, R., Ranjbar-Bourani, M., Amin, G. R., & Siadat, A. (2012). A cell formation problem considering machine utilization and alternative process routes by scatter search. Journal of Intelligent Manufacturing, 23(4), 1127–1139.

    Article  Google Scholar 

  • Tsai, J. T., Ho, W. H., Liu, T. K., & Chou, J. H. (2007). Improved immune algorithm for global numerical optimization and job-shop scheduling problems. Applied Mathematics and Computation, 194(2), 406–24.

    Article  Google Scholar 

  • Tseng, C. T., & Liao, C. J. (2008). A particle swarm optimization algorithm for hybrid flowshop scheduling with multiprocessor tasks. International Journal of Production Research, 46(17), 4655–4670.

    Article  Google Scholar 

  • Tseng, L. Y., & Lin, Y. T. (2010). A hybrid genetic algorithm for no-wait flow shop scheduling problem. International Journal of Production Economics, 128, 144–152.

    Article  Google Scholar 

  • Urlings, T., Ruiz, R., & Stützle, T. (2010). Shifting representation search for hybrid flexible flowline problems. European Journal of Operational Research, 207, 1086–1095.

    Article  Google Scholar 

  • Vahdani, B., Tavakkoli-Moghaddam, R., Zandieh, M., & Razmi, J. (2012). Vehicle routing scheduling using an enhanced hybrid optimization approach. Journal of Intelligent Manufacturing, 23(3), 759–774.

    Google Scholar 

  • Verma, S. K., & Dessouky, M. M. (1999). Multistage hybrid flowshop scheduling with identical jobs and uniform parallel machines. Journal of scheduling, 2, 135–50.

    Article  Google Scholar 

  • Wang, L., Pan, Q. K., & Tasgetiren, M. F. (2011). A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem. Computers & Industrial Engineering, 61, 76–83.

    Article  Google Scholar 

  • Wang, I. L., Yang, T., & Chang, Y. B. (2012). Scheduling two-stage hybrid flow shops with parallel batch, release time, and machine eligibility constraints. Journal of Intelligent Manufacturing, 23(6), 2271–2280.

    Article  Google Scholar 

  • Wang, Z., Xing, W., & Bai, F. (2005). No-wait flexible flow shop scheduling with no-idle machines. Operation Research Letter, 33, 609–614.

    Article  Google Scholar 

  • Xie, J., & Wang, X. (2005). Complexity and algorithms for two-stage flexible flow shop scheduling with availability constraints. Computer and Mathematics with Application, 50, 1629–1638.

    Google Scholar 

  • Xie, J., Xing, W., Liu, Z., & Dong, J. (2004). Minimum deviation algorithm for two-stageno-wait flowshops with parallel machines. Computers & Mathematics with Applications, 47(12), 1857–63.

    Article  Google Scholar 

  • Yaurima, V., Burtseva, L., & Tchernykh, A. (2009). Hybrid flowshop with unrelated machines, sequence-dependent setup time, availability constraints and limited buffers. Computers & Industrial Engineering, 56, 1452–1463.

    Google Scholar 

  • Ying, K. C., & Lin, S. W. (2006). Multiprocessor task scheduling in multistage hybrid flowshops: an ant colony system approach. International Journal of Production Research, 44(16), 3161–3177.

    Article  Google Scholar 

  • Zandieh, M., & Karimi, N. (2011). An adaptive multi-population genetic algorithm to solve the multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times. Journal of Intelligent Manufacturing, 22(6), 979–989.

    Article  Google Scholar 

  • Zhang, Y., & Li, X. (2011). Estimation of distribution algorithm for permutation flow shops with total flow time minimization. Computers & Industrial Engineering, 60, 706–718.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Pezhman Ramezani

  2. Department of Industrial Engineering, Tuyserkan’s Engineering Faculty, Bu-Alisina University, Hamadan, Iran

    Meysam Rabiee

  3. Department of Industrial Engineering, University of Tehran, Tehran, Iran

    Fariborz Jolai

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  1. Pezhman Ramezani
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  2. Meysam Rabiee
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  3. Fariborz Jolai
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Correspondence to Pezhman Ramezani.

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Ramezani, P., Rabiee, M. & Jolai, F. No-wait flexible flowshop with uniform parallel machines and sequence-dependent setup time: a hybrid meta-heuristic approach. J Intell Manuf 26, 731–744 (2015). https://doi.org/10.1007/s10845-013-0830-2

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