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Using heuristic algorithms to solve the scheduling problems with job-dependent and machine-dependent learning effects

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Abstract

The multi-machine scheduling problems with job-dependent and machine-dependent learning effects are proposed in this paper. Since it is almost impossible to obtain the analytic results for this complicated multi-machine scheduling problems with learning effects, four heuristic algorithms are used to solve this newly proposed model, where the variants of well-known genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO) and particle swarm optimization (PSO) are coded in the commercial software MATLAB. The objective is to minimize the makespan of this new model. For this kind of scheduling problem, the numerical experiments show that the GA and SA outperform ACO and PSO.

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References

  • Arnaout, J.-P., Rabadi, G., & Musa, R. (2010). A two-stage ant colony optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times. Journal of Intelligent Manufacturing, 21, 693–701.

    Article  Google Scholar 

  • Bachman, A., & Janiak, A. (2004). Scheduling jobs with position-dependent processing times. Journal of Operational Research Society, 55, 257–264.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Biskup, D. (1999). Single-machine scheduling with learning considerations. European Journal of Operational Research, 115, 173–178.

    Article  Google Scholar 

  • Cheng, T. C. E., Wu, C. C., & Lee, W. C. (2008). Some scheduling problems with sum-of-processing-times-based and job-position-based learning effects. Information Sciences, 178, 2476–2487.

    Article  Google Scholar 

  • Cheng, T. C. E., & Wang, G. (2000). Single machine scheduling with learning effect considerations. Annals of Operations Research, 98, 273–290.

    Article  Google Scholar 

  • Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Cambridge: MIT Press.

    Book  Google Scholar 

  • Eberhard, R. C., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan (pp. 39–43).

  • Gonzalez, T., & Sahni, S. (1978). Flowshop and jobshop schedule: Complexity and approximation. Operations Research, 26, 36–52.

    Article  Google Scholar 

  • Hu, H., & Li, Z. (2009a). Modeling and scheduling for manufacturing grid workflows using timed Petri nets. The International Journal of Advanced Manufacturing Technology, 42, 553–568.

    Article  Google Scholar 

  • Hu, H., & Li, Z. (2009b). Liveness enforcing supervision in video streaming systems using siphons. Journal of Information Science and Engineering, 25, 1863–1884.

    Google Scholar 

  • Hu, H., & Li, Z. (2009c). Local and global deadlock prevention policies for resource allocation systems using partially generated reachability graphs. Computers and Industrial Engineering, 57, 1168–1181.

    Article  Google Scholar 

  • Hu, H., & Li, Z. (2010). Synthesis of liveness enforcing supervisor for automated manufacturing systems. Journal of Intelligent Manufacturing, 21, 555–567.

    Article  Google Scholar 

  • Hu, H., Li, Z., & Al-Ahmari, A. (2011). Reversed fuzzy Petri nets and their application for fault diagnosis. Computers and Industrial Engineering, 60, 505–510.

    Article  Google Scholar 

  • Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671–680.

    Article  Google Scholar 

  • Koulamas, C., & Kyparisis, G. J. (2007). Single-machine and two-machine flowshop scheduling with general learning functions. European Journal of Operational Research, 178, 402–407.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kuo, W.-H., & Yang, D.-L. (2006b). Minimizing the total completion time in a single-machine scheduling problem with a time-dependent learning effect. European Journal of Operational Research, 174, 1184–1190.

    Article  Google Scholar 

  • Kuo, W.-H., & Yang, D.-L. (2006c). Minimizing the makespan in a single machine scheduling problem with a time-based learning effect. Information Processing Letter, 97, 64–67.

    Article  Google Scholar 

  • Lai, P.-J., & Lee, W.-C. (2011). Single-machine scheduling with general sum-of-processing time-based and position-based learning effects. Omega, 39, 467–471.

    Article  Google Scholar 

  • Lai, P.-J., & Wu, H.-C. (2008). Using genetic algorithms to solve fuzzy flow shop scheduling problems based on possibility and necessity measures. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 16, 409–433.

    Article  Google Scholar 

  • Lai, P.-J., & Wu, H.-C. (2009). Using ant colony optimization to minimize the fuzzy makespan and total weighted fuzzy completion time in flow shop scheduling problems. The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 17, 559–584.

    Article  Google Scholar 

  • Lee, W. C. (2004). A note on deteriorating jobs and learning in single-machine scheduling problems. International Journal of Business and Economics, 3, 83–89.

    Google Scholar 

  • Lee, W.-C., & Wu, C.-C. (2004). Minimizing total completion time in a two-machine flowshop with a learning effect. International Journal of Production Economics, 88, 85–93.

    Article  Google Scholar 

  • Lee, W.-C., Wu, C.-C., & Sung, H.-J. (2004). A bi-criterion single-machine scheduling problem with learning considerations. Acta Informatica, 40, 303–315.

    Article  Google Scholar 

  • Lee, W.-C., & Wu, C.-C. (2009). Some single-machine and m-machine flowshop scheduling problems with learning considerations. Information Sciences, 179, 3885–3892.

    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, 965–978.

    Article  Google Scholar 

  • Moshieov, G. (2001). Scheduling problems with a learning effect. European Journal of Operational Research, 132, 687–693.

    Article  Google Scholar 

  • Moshieov, G. (2001). Parallel machine scheduling with a learning effect. Journal of Operational Research Society, 52, 1165–1169.

    Article  Google Scholar 

  • Moshieov, G., & Sidney, J. B. (2003). Scheduling with general job-dependent learning curves. European Journal of Operational Research, 147, 665–670.

    Article  Google Scholar 

  • Moshieov, G., & Sidney, J. B. (2005). Note on scheduling with general learning curves to minimize the number of tardy jobs. Journal of Operational Research Society, 56, 110–112.

    Article  Google Scholar 

  • Nearchou, A. C. (2004). Flow-shop sequencing using hybrid simulated annealing. Journal of Intelligent Manufacturing, 15, 317–328.

    Article  Google Scholar 

  • Solano-Charris, E. L., Montoya-Torres, J. R., & Paternina-Arboleda, C. D. (2011). Ant colony optimization algorithm for a bi-criteria 2-stage hybrid flowshop scheduling problem. Journal of Intelligent Manufacturing, 22, 815–822.

    Article  Google Scholar 

  • Speras, W. M. & DeJong, K. A. (1991). On the virtues of parameterized uniform crossover. In: Proceedings of the fourth international conference genetic algorithms (pp. 230–236).

  • Tasgetiren, M. F., Liang, Y.-C., Sevkli, M., & Gencyilmaz, G. (2007). A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. European Journal of Operational Research, 177, 1930–1947.

    Article  Google Scholar 

  • Toksari, M. D., & Güner, E. (2010). Parallel machine scheduling problem to minimize the earliness/tardiness costs with learning effect and deteriorating jobs. Journal of Intelligent Manufacturing, 21, 843851.

    Article  Google Scholar 

  • Wang, J.-B. (2007). Single-machine scheduling problems with the effects of learning and deterioration. Omega, 35, 397–402.

    Article  Google Scholar 

  • Wang, J.-B., & Xia, Z.-Q. (2005). Flow shop scheduling with a learning effect. Journal of Operational Research Society, 56, 1325–1330.

    Article  Google Scholar 

  • Wang, X., & Cheng, T. C. E. (2007). Single-machine scheduling with deteriorating jobs and learning effects to minimize the makespan. European Journal of Operational Research, 178, 57–70.

    Article  Google Scholar 

  • Wu, C.-C. (2006). The development of a solution to the single-machine total weighted completion time problem with a learning effect. International Journal of Management, 23, 113–116.

    Google Scholar 

  • Wu, C.-C., Lee, W.-C., & Wang, W.-C. (2007). A two-machine flowshop maximum tardiness scheduling problem with a learning effect. International Journal of Advanced Manufacturing Technology, 31, 743–750.

    Article  Google Scholar 

  • Wu, W.-H., Cheng, S.-R., Wu, C.-C., & Yin, Y. (2012). Ant colony algorithms for a two-agent scheduling with sum-Of processing times-based learning and deteriorating considerations. Journal of Intelligent Manufacturing, 23, 1985–1993.

    Article  Google Scholar 

  • Yin, Y.-Q., Xu, D.-H., Sun, K.-B., & Li, H.-X. (2009). Some scheduling problems with general position-dependent and time-dependent learning effects. Information Sciences, 179, 2416–2425.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics, National Kaohsiung Normal University, Kaohsiung , 802, Taiwan

    Peng-Jen Lai & Hsien-Chung Wu

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  1. Peng-Jen Lai
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  2. Hsien-Chung Wu
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Correspondence to Hsien-Chung Wu.

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Lai, PJ., Wu, HC. Using heuristic algorithms to solve the scheduling problems with job-dependent and machine-dependent learning effects. J Intell Manuf 26, 691–701 (2015). https://doi.org/10.1007/s10845-013-0827-x

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  • DOI: https://doi.org/10.1007/s10845-013-0827-x

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