Heuristic based genetic algorithms for the re-entrant total completion time flowshop scheduling with learning consideration

References

• M. Pinedo, Scheduling: theory, algorithms and systems. NJ, Prentice-Hall: Upper Saddle River, (2008).
   Google Scholar
• Y. Yin, T.C.E. Cheng, D.-J. Wang, C.-C. Wu, (2015) Improved algorithms for single-machine serial-batch scheduling with rejection to minimize total completion time and total rejection cost, IEEE Transactions on Systems, Man and Cybernetics: Systems, (2015) doi:10.1109/TSMC.2015.2505644.
   Google Scholar
• Y. Yin, J. Xu, T.C.E. Cheng, C.-C. Wu, D.-J. Wang, Approximation schemes for single-machine scheduling with a fixed maintenance activity to minimize the total amount of late work, Naval Research Logistics, 63 (2016) 172–183.
   Web of Science ®Google Scholar
• D. Biskup, Single-machine scheduling with learning considerations, European Journal of Operational Research, 115 (1999) 173–178.
   Web of Science ®Google Scholar
• D. Nadler, W.D. Smith, Manufacturing progress functions for types of processes, International Journal of Production Research, 2 (1963) 115–135.
   Google Scholar
• L.E. Yelle, The learning curve: historical review and comprehensive survey, Decision Science, 10 (1979) 302–328.
   Google Scholar
• P. Higgins, P. Le Roy, L. Tierney, Manufacturing Planning and Control-Beyond MRP II. Chapman & Hall, London (1996).
   Google Scholar
• T.C.E. Cheng G. Wang, Single machine scheduling with learning effect considerations, Annals of Operations Research, 98 (2000) 273–290.
   Web of Science ®Google Scholar
• D. Biskup, A state-of-the-art review on scheduling with learning effect, European Journal of Operational Research, 188 (2008) 315–329.
   Web of Science ®Google Scholar
• S.J. Yang, C.J. Hsu, D.L. Yang, Parallel-machine scheduling with setup and removal times under consideration of the learning effect, Journal of the Chinese Institute of Industrial Engineers, 27(5) (2010) 372–378.
   Google Scholar
• Y. Yin, D. Xu, J. Wang, Single-machine scheduling with a general sum-of-actual-processing-times-based and job-position-based learning effect, Applied Mathematical Modelling, 34(11) (2010) 3623–3630.
   Web of Science ®Google Scholar
• N. Yin, X.-Y. Wang, Single machine scheduling with controllable processing times and learning effect, International Journal of Advanced Manufacturing Technology, 54 (2011) 743–748.
   Web of Science ®Google Scholar
• J.-B. Wang, Single-machine scheduling with a sum-of-actual-processing-time-based learning effect, Journal of the Operational Research Society, 61 (2010) 172–177.
   Web of Science ®Google Scholar
• S.-J. Yang, D.-L. Yang, Scheduling problems with past-sequence-dependent delivery times and learning effects, Journal of the Operational Research Society, 63 (2012) 1508–1515.
   Web of Science ®Google Scholar
• J.-B. Wang, Q. Guo, A due-date assignment problem with learning effect and deteriorating jobs, Applied Mathematical Modelling, 34 (2010) 309–313.
   Web of Science ®Google Scholar
• X.-Y. Wang, Z. Zhou, X. Zhang, P. Ji, J.-B. Wang, several flow shop scheduling problems with truncated position-based learning effect, Computers & Operations Research, 40 (2013) 2906–2929.
   Web of Science ®Google Scholar
• J.-B. Wang, Z.Q. Xia, Flow-shop scheduling with a learning effect, Journal of the Operational Research Society, 56 (2005) 1325–1330.
   Web of Science ®Google Scholar
• C.-C. Wu, W.-C. Lee, A note on the total completion time problem in a permutation flowshop with a learning effect, European Journal of Operational Research, 192 (2009) 343–347.
   Web of Science ®Google Scholar
• J.-B. Wang, M.Z. Wang, Worst-case analysis for flow shop scheduling problems with an exponential learning effect, Journal of the Operational Research Society, 63 (2012) 130–137.
   Web of Science ®Google Scholar
• Y.-H. Chung, L.-I. Tong, Bi-criteria minimization for the permutation flowshop scheduling problem with machine-based learning effects, Computers & Industrial Engineering, 63(1) (2012) 302–312.
   Web of Science ®Google Scholar
• W.-H. Kuo, C.-J. Hsu, D.-L. Yang, Worst-case and numerical analysis of heuristic algorithms for flowshop scheduling problems with a time-dependent learning effect, Information Sciences, 184(1) (2012) 282–297.
   Web of Science ®Google Scholar
• T.C.E. Cheng, C.-C. Wu, J.-C. Chen, W.-H. Wu, S.-R. Cheng, Two-machine flowshop scheduling with a truncated learning function to minimize the makespan, International Journal of Production Economics, 141(1) (2013) 79–86.
   Web of Science ®Google Scholar
• M. Cheng, Flowshop scheduling problems with a position-dependent exponential learning effect, Mathematical Problems in Engineering (2013) doi.org/10.1155/2013/753123.
   Google Scholar
• L.H. Sun, K. Cui, J.-H. Chen, J. Wang, X.C. He, Some results of the worst-case analysis for flow shop scheduling with a learning effect, Annals of Operations Research, 211(2013) 481–490.
   Web of Science ®Google Scholar
• L.-H. Sun, K. Cui, J.-H. Chen, J. Wang, X.-C. He, Research on permutation flow shop scheduling problems with general position-dependent learning effects, Annals of Operations Research, 211 (2013) 473–480.
   Web of Science ®Google Scholar
• A. Janiak, T. Krysiak, R. Trela, Scheduling problems with learning and ageing effects: a survey, Decision Making in Manufacturing, 5(1–2) (2011) 19–36.
   Google Scholar
• F.D. Vargas-Villamil, D.E. Rivera, A model predictive control approach for real-time optimization of reentrant manufacturing lines, Computers in Industry, 45 (2001) 45–57.
   Web of Science ®Google Scholar
• M.Y. Wang, S.P. Sethi, S.L. van de Velde, Minimizing makespan in a class of reentrant shops, Operations Research, 45 (1997) 702–712.
   Web of Science ®Google Scholar
• G. Bengu, A simulation-based scheduler for flexible flowlines, International Journal of Production Research, 32 (1994) 321–344.
   Web of Science ®Google Scholar
• C.F. Bispo, S. Tayur, Managing simple re-entrant flow lines: Theoretical foundation and experimental results, IIE Transaction, 33 (2001) 609–623.
   Web of Science ®Google Scholar
• W. Kubiak, S.X.C. Lou, Y. Wang, Mean flow time minimization in reentrant job-shops with a hub, Operations Research, 44 (1996) 764–776.
   Web of Science ®Google Scholar
• R. Uzsoy, C.Y. Lee, L.A. Martin-Vega, A review of production planning and scheduling models in the semiconductor industry—Part 1: System characteristics, performance evaluation and production planning, IIE Transaction, 24 (1992) 47–60.
   Web of Science ®Google Scholar
• R. Bellman, R. Ernest, Mathematical Aspects of Scheduling and Applications. Oxford: Pergamon Press, (1982).
   Google Scholar
• D. Lin, C.K.M. Lee, A review of the research methodology for the re-entrant scheduling problem, International Journal of Production Research, 49(8) (2011) 2221–2242.
   Web of Science ®Google Scholar
• C.-C. Wu, Y.Q. Yin, T.C.E. Cheng, S.-Y. Liu, W.-H. Wu, Re-entrant fowshop scheduling with learning considerations to minimize the makespan, Iranian Journal of Science and Technology, Transactions A: Science, under review, (2016).
   Google Scholar
• J.C.H. Pan, J.-S. Chen, Minimizing makespan in reentrant permutation flow-shops, Journal of the Operational Research Society, 54 (2003) 642–653.
   Web of Science ®Google Scholar
• J.M. Wilson, Alternative formulations of a flow-shop scheduling problem, Journal of the Operational Research Society, 40 (1989) 395–399.
   Web of Science ®Google Scholar
• J.-S. Chen, A branch-and-bound procedure for the reentrant permutation flow-shop scheduling problem, International Journal of Advanced Manufacturing Technology, 29 (2006) 1186–1193.
   Web of Science ®Google Scholar
• J.-S. Chen, J.C.H. Pan, C.K. Wu, Minimizing makespan in reentrant flow-shops using hybrid tabu search, International Journal of Advanced Manufacturing Technology, 34 (2007) 353–361.
   Web of Science ®Google Scholar
• J. Xu, Y. Yin, T.C.E. Cheng, C.-C. Wu, S. Gu, A memetic algorithm for the re-entrant permutation flowshop scheduling problem to minimize the makespan, Applied Soft Computing, 24 (2014) 277–283.
   Web of Science ®Google Scholar
• J.K. Lenstra, A.H.G. Rinnooy Kan, P. Brucker, Complexity of machine scheduling problems, Annals of Operations Research, 1 (1977) 343–362.
   Google Scholar
• C. Rajendran, H. Ziegler, An efficient heuristic for scheduling in a flowshop to minimize total weighted flowtime of jobs, European Journal of Operational Research, 103 (1997) 129–138.
   Web of Science ®Google Scholar
• D.S. Woo, H.S. Yim, A heuristic algorithm for mean flowtime objective in scheduling, Computers and Operations Research, 25 (1998) 175–182.
   Web of Science ®Google Scholar
• J.M. Framinan, R Leisten, An efficient constructive heuristic for flowtime minimisation in permutation flow shops, Omega: The International Journal of Management Science, 31 (2003) 311–317.
   Web of Science ®Google Scholar
• M. Nawaz, E.E. Enscore, I. Ham, A heuristic algorithm for the m-machine, n-job sequencing problem, Omega: The International Journal of Management Science, 11 (1983) 91–95.
   Web of Science ®Google Scholar
• J. Demšar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, 7 (2006) 1–30.
   Web of Science ®Google Scholar
• S. García, F. Herrera, An extension on Statistical Comparisons of Classifiers over Multiple Data Sets for all pairwise comparisons, Journal of Machine Learning Research, 9 (2008) 2677–2694.
   Web of Science ®Google Scholar
• S. García, A. Fernaández, J. Luengo, F. Herrera, A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability, Soft Computing, 13 (2009) 959–977.
   Web of Science ®Google Scholar
• S. García, D. Molina, M. Lozano, F. Herrera, A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter, Optimization, Journal of Heuristics, 15 (2009) 617–644.
   Web of Science ®Google Scholar
• S. García, A. Fernaández, J. Luengo, F. Herrera, Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power, Information Sciences, 180 (2010) 2044–2064.
   Web of Science ®Google Scholar
• J. Derrac, S. García, D. Molina, F. Herrera, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm and Evolutionary Computation, 1 (2011) 3–18.
   Web of Science ®Google Scholar
• J. Derrac, S. García, S. Hui, P.N. Suganthan, F. Herrera, Analyzing convergence performance of evolutionary algorithms: A statistical approach, Information Sciences, 289 (2014) 41–58.
   Web of Science ®Google Scholar
• J. Luengo, S. García, F. Herrera, A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests, Expert Systems with Applications, 36 (2009) 7798–7808.
   Web of Science ®Google Scholar
• F. Della Croce, V., Narayan, R. Tadei, The two-machine total completion time flow shop problem, European Journal of Operational Research, 90 (1996) 227–237.
   Web of Science ®Google Scholar
• M.A. Ahmadi, S.R., Shadizadeh, RETRACTED ARTICLE: Intelligent approach for prediction of minimum miscible pressure by evolving genetic algorithm and neural network, Neural Computing and Applications, 23(2) (2013) 569–569.
   Google Scholar
• S.H. Cheng, M.C. Chen, Y.C. Liou, Artificial chromosomes with genetic algoritm 2 (ACGA2) for single machine scheduling problems with sequence-, Applied Soft Computing, 17 (2014) 167–175.
   Web of Science ®Google Scholar
• O.J. Mengshoel, S.F. Galán, A. de Dios, Adaptive generalized crowding for genetic algoritms, Information Sciences, 258 (2014) 140–159
   Web of Science ®Google Scholar
• F. Tao, Y. Feng, L. Zhang, T.W. Liao, CLPS-GA: A case library and Pareto solution-based hybrid genetic algoritm for energy-aware cloud service scheduling, Applied Soft Computing, 19 (2014) 264–279.
   Web of Science ®Google Scholar
• F. Valdez, P. Melin, O. Castillo, Modular Neural Networks architecture optimization with a new nature inspired method using a fuzzy combination of Particle, Information Sciences, 270 (2014) 143–153.
   Web of Science ®Google Scholar
• Y. Xu, K., Li, J., Hu, K. Li, A genetic algoritm for task scheduling on heterogeneous computing systems using , Information Sciences, 270 (2014) 255–287.
   Web of Science ®Google Scholar
• C.-C. Wu, Y. Yin, W.H. Wu, H.M. Chen, S.R. Cheng, Using a branch-and-bound and a genetic algorithm for a single-machine total late work scheduling problem, Soft Computing, 20 (2016) 1329–1339.
   Web of Science ®Google Scholar
• J.C. Bean, Genetic algorithms and random keys for sequencing and optimization, ORSA Journal of Computing, 6 (1964) 154–160.
   Google Scholar
• C. Reeves, Heuristics for scheduling a single machine subject to unequal job release times, European Journal of Operational Research, 80 (1995) 397–403.
   Web of Science ®Google Scholar
• C.L. Chen, V.S. Vempati, N. Aljaber, An application of genetic algorithms for flow shop problems, European Journal of Operations Research, 80 (1995) 389–396.
   Web of Science ®Google Scholar
• O. Etiler, B. Toklu, M. Atak, J. Wilson, (2004) A generic algorithm for flow shop scheduling problems, Journal of Operations Research Society, 55(8) (2004) 830–835.
   Web of Science ®Google Scholar
• C.-C. Wu, P.-H. Hsu, J.-C. Chen, N.-S. Wang, Genetic algorithm for minimizing the total weighted completion time scheduling problem with learning and release times, Computers & Operations Research, 38(7) (2011) 1025–1034.
   Web of Science ®Google Scholar
• E. Falkenauer, S. Bouffoix, A genetic algorithm for job shop. Proceedings of the 1991 IEEE International Conference on Robotics and Automation, (1991).
   Google Scholar
• O. Etiler, B. Toklu, Comparison of genetic crossover operators using in scheduling problems, Journal Inst Technology, Gazi University, Turkey, 14 (2001) 21–32.
   Google Scholar