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How to cite this paper
APA: Shi, S & Gao, S. (2023). Multi-objective optimization of simultaneous buffer and service rate allocation in manufacturing systems based on a data-driven hybrid approach. International Journal of Industrial Engineering Computations, 14(4), 707-722.
Chicago/Turabian: Shi, S & Gao, S. 2023. "Multi-objective optimization of simultaneous buffer and service rate allocation in manufacturing systems based on a data-driven hybrid approach." International Journal of Industrial Engineering Computations 14, no. 4 (2023): 707-722.
AMA: Shi, S & Gao, S. Multi-objective optimization of simultaneous buffer and service rate allocation in manufacturing systems based on a data-driven hybrid approach. International Journal of Industrial Engineering Computations. 2023;14(4):707-722.
References
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Gao, S. (2022). A Bottleneck Detection-Based Tabu Search Algorithm for the Buffer Allocation Problem in Manufacturing Systems. IEEE Access, 10, 60507–60520. https://doi.org/10.1109/ACCESS.2022.3181134
Gao, S., Higashi, T., Kobayashi, T., Taneda, K., Rubrico, J. I. U., & Ota, J. (2020). Buffer Allocation via Bottleneck-Based Variable Neighborhood Search. Applied Sciences, 10(23). https://doi.org/10.3390/app10238569
Gao, S., Kobayashi, T., Tajiri, A., & Ota, J. (2021). Throughput analysis of conveyor systems involving multiple materials based on capability decomposition. Computers in Industry, 132. https://doi.org/10.1016/j.compind.2021.103526
Gao, S., & Liu, H. (2023). A data-driven ensemble algorithm of black widow optimizer and simulated annealing algorithms for multi-objective buffer allocation in production lines. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 095440542311571. https://doi.org/10.1177/09544054231157153
Gao, S., Rubrico, J. I. U., Higashi, T., Kobayashi, T., Taneda, K., & Ota, J. (2019). Efficient Throughput Analysis of Production Lines Based on Modular Queues. IEEE Access, 7. https://doi.org/10.1109/ACCESS.2019.2928309
Glover, F., Campos, V., & Martí, R. (2021). Tabu search tutorial. A Graph Drawing Application. TOP, 29(2), 319–350. https://doi.org/10.1007/s11750-021-00605-1
Huang, C.-L. (1999). The construction of production performance prediction system for semiconductor manufacturing with artificial neural networks. International Journal of Production Research, 37(6), 1387–1402. https://doi.org/10.1080/002075499191319
Jiao, Y., Xing, X., Zhang, P., Xu, L., & Liu, X.-R. (2018). Multi-objective storage location allocation optimization and simulation analysis of automated warehouse based on multi-population genetic algorithm. Concurrent Engineering, 26(4), 367–377. https://doi.org/10.1177/1063293X18796365
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30.
Kerbache, L., & Smith, J. M. (1988). Asymptotic behavior of the expansion method for open finite queueing networks. Computers & Operations Research, 15(2), 157-169. https://doi.org/10.1016/0305-0548(88)90008-1
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Nahas, N., Nourelfath, M., & Gendreau, M. (2014). Selecting machines and buffers in unreliable assembly/disassembly manufacturing networks. International Journal of Production Economics, 154, 113–126. https://doi.org/10.1016/j.ijpe.2014.04.011
Ng, A. H., Shaaban, S., & Bernedixen, J. (2017). Studying unbalanced workload and buffer allocation of production systems using multi-objective optimisation. International Journal of Production Research, 55(24), 7435-7451. https://doi.org/10.1080/00207543.2017.1362121
Oljira, D. G., Abeya, T. G., Ofgera, G., & Gopal, M. (2020). Manufacturing System Modeling and Performance Analysis of Mineral Water Production Line using ARENA Simulation. International Journal of Engineering and Advanced Technology, 9(5), 312–317. https://doi.org/10.35940/ijeat.d8033.069520
Papadopoulos, C. T., O’Kelly, M. E. J., & Tsadiras, A. K. (2013). A DSS for the buffer allocation of production lines based on a comparative evaluation of a set of search algorithms. International Journal of Production Research, 51(14), 4175-4199. https://doi.org/10.1080/00207543.2012.752585
Shaaban, S., & McNamara, T. (2009). Improving the efficiency of unpaced production lines by unbalancing service time means. International Journal of Operational Research, 4(3), 346-361. https://doi.org/10.1504/IJOR.2009.023288
Shi, C., & Gershwin, S. B. (2016). A segmentation approach for solving buffer allocation problems in large production systems. International Journal of Production Research, 54(20), 6121-6141. https://doi.org/10.1080/00207543.2014.991842
Sklearn. (n.d.). https://scikit-learn.org/stable/
Smith, J. M. (2018). Simultaneous buffer and service rate allocation in open finite queueing networks. IISE Transactions, 50(3). https://doi.org/10.1080/24725854.2017.1300359
Song, D., Xing, W., & Sun, Y. (1998). Optimal service rate allocation policy of an unreliable manufacturing system with random demands. Kongzhi Lilun Yu Yingyong/Control Theory and Applications.
Song, Z., & Moon, Y. (2019). Performance analysis of CyberManufacturing Systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233(5), 1362–1376. https://doi.org/10.1177/0954405417706996
Spinellis, D., Vidalis, M. J., O’Kelly, M. E. J., & Papadopoulos, C. T. (2009). Analysis and Design of Discrete Part Production Lines (Vol. 31). Springer New York. https://doi.org/10.1007/978-0-387-89494-2
Su, C., Shi, Y., & Dou, J. (2017). Multi-objective optimization of buffer allocation for remanufacturing system based on TS-NSGAII hybrid algorithm. Journal of Cleaner Production, 166. https://doi.org/10.1016/j.jclepro.2017.08.064
Tsadiras, A. K., Papadopoulos, C. T., & O’Kelly, M. E. J. (2013). An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines. Computers & Industrial Engineering, 66(4), 1150–1162. https://doi.org/10.1016/j.cie.2013.07.024
Weiss, S., Schwarz, J. A., & Stolletz, R. (2019). The buffer allocation problem in production lines: Formulations, solution methods, and instances. IISE Transactions, 51(5), 456-485. https://doi.org/10.1080/24725854.2018.1442031
Xi, S., Smith, J. M., Chen, Q., Mao, N., Zhang, H., & Yu, A. (2022). Simultaneous machine selection and buffer allocation in large unbalanced series-parallel production lines. International Journal of Production Research, 60(7), 2103-2125. https://doi.org/10.1080/00207543.2021.1884306
Zeid, I. B., Doh, H.-H., Shin, J.-H., & Lee, D.-H. (2021). Fast and meta heuristics for part selection in flexible manufacturing systems with controllable processing times. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 235(4), 650–662. https://doi.org/10.1177/0954405420968172
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. https://doi.org/10.1109/4235.996017
Gao, S. (2022). A Bottleneck Detection-Based Tabu Search Algorithm for the Buffer Allocation Problem in Manufacturing Systems. IEEE Access, 10, 60507–60520. https://doi.org/10.1109/ACCESS.2022.3181134
Gao, S., Higashi, T., Kobayashi, T., Taneda, K., Rubrico, J. I. U., & Ota, J. (2020). Buffer Allocation via Bottleneck-Based Variable Neighborhood Search. Applied Sciences, 10(23). https://doi.org/10.3390/app10238569
Gao, S., Kobayashi, T., Tajiri, A., & Ota, J. (2021). Throughput analysis of conveyor systems involving multiple materials based on capability decomposition. Computers in Industry, 132. https://doi.org/10.1016/j.compind.2021.103526
Gao, S., & Liu, H. (2023). A data-driven ensemble algorithm of black widow optimizer and simulated annealing algorithms for multi-objective buffer allocation in production lines. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 095440542311571. https://doi.org/10.1177/09544054231157153
Gao, S., Rubrico, J. I. U., Higashi, T., Kobayashi, T., Taneda, K., & Ota, J. (2019). Efficient Throughput Analysis of Production Lines Based on Modular Queues. IEEE Access, 7. https://doi.org/10.1109/ACCESS.2019.2928309
Glover, F., Campos, V., & Martí, R. (2021). Tabu search tutorial. A Graph Drawing Application. TOP, 29(2), 319–350. https://doi.org/10.1007/s11750-021-00605-1
Huang, C.-L. (1999). The construction of production performance prediction system for semiconductor manufacturing with artificial neural networks. International Journal of Production Research, 37(6), 1387–1402. https://doi.org/10.1080/002075499191319
Jiao, Y., Xing, X., Zhang, P., Xu, L., & Liu, X.-R. (2018). Multi-objective storage location allocation optimization and simulation analysis of automated warehouse based on multi-population genetic algorithm. Concurrent Engineering, 26(4), 367–377. https://doi.org/10.1177/1063293X18796365
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30.
Kerbache, L., & Smith, J. M. (1988). Asymptotic behavior of the expansion method for open finite queueing networks. Computers & Operations Research, 15(2), 157-169. https://doi.org/10.1016/0305-0548(88)90008-1
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Nahas, N., Nourelfath, M., & Gendreau, M. (2014). Selecting machines and buffers in unreliable assembly/disassembly manufacturing networks. International Journal of Production Economics, 154, 113–126. https://doi.org/10.1016/j.ijpe.2014.04.011
Ng, A. H., Shaaban, S., & Bernedixen, J. (2017). Studying unbalanced workload and buffer allocation of production systems using multi-objective optimisation. International Journal of Production Research, 55(24), 7435-7451. https://doi.org/10.1080/00207543.2017.1362121
Oljira, D. G., Abeya, T. G., Ofgera, G., & Gopal, M. (2020). Manufacturing System Modeling and Performance Analysis of Mineral Water Production Line using ARENA Simulation. International Journal of Engineering and Advanced Technology, 9(5), 312–317. https://doi.org/10.35940/ijeat.d8033.069520
Papadopoulos, C. T., O’Kelly, M. E. J., & Tsadiras, A. K. (2013). A DSS for the buffer allocation of production lines based on a comparative evaluation of a set of search algorithms. International Journal of Production Research, 51(14), 4175-4199. https://doi.org/10.1080/00207543.2012.752585
Shaaban, S., & McNamara, T. (2009). Improving the efficiency of unpaced production lines by unbalancing service time means. International Journal of Operational Research, 4(3), 346-361. https://doi.org/10.1504/IJOR.2009.023288
Shi, C., & Gershwin, S. B. (2016). A segmentation approach for solving buffer allocation problems in large production systems. International Journal of Production Research, 54(20), 6121-6141. https://doi.org/10.1080/00207543.2014.991842
Sklearn. (n.d.). https://scikit-learn.org/stable/
Smith, J. M. (2018). Simultaneous buffer and service rate allocation in open finite queueing networks. IISE Transactions, 50(3). https://doi.org/10.1080/24725854.2017.1300359
Song, D., Xing, W., & Sun, Y. (1998). Optimal service rate allocation policy of an unreliable manufacturing system with random demands. Kongzhi Lilun Yu Yingyong/Control Theory and Applications.
Song, Z., & Moon, Y. (2019). Performance analysis of CyberManufacturing Systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233(5), 1362–1376. https://doi.org/10.1177/0954405417706996
Spinellis, D., Vidalis, M. J., O’Kelly, M. E. J., & Papadopoulos, C. T. (2009). Analysis and Design of Discrete Part Production Lines (Vol. 31). Springer New York. https://doi.org/10.1007/978-0-387-89494-2
Su, C., Shi, Y., & Dou, J. (2017). Multi-objective optimization of buffer allocation for remanufacturing system based on TS-NSGAII hybrid algorithm. Journal of Cleaner Production, 166. https://doi.org/10.1016/j.jclepro.2017.08.064
Tsadiras, A. K., Papadopoulos, C. T., & O’Kelly, M. E. J. (2013). An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines. Computers & Industrial Engineering, 66(4), 1150–1162. https://doi.org/10.1016/j.cie.2013.07.024
Weiss, S., Schwarz, J. A., & Stolletz, R. (2019). The buffer allocation problem in production lines: Formulations, solution methods, and instances. IISE Transactions, 51(5), 456-485. https://doi.org/10.1080/24725854.2018.1442031
Xi, S., Smith, J. M., Chen, Q., Mao, N., Zhang, H., & Yu, A. (2022). Simultaneous machine selection and buffer allocation in large unbalanced series-parallel production lines. International Journal of Production Research, 60(7), 2103-2125. https://doi.org/10.1080/00207543.2021.1884306
Zeid, I. B., Doh, H.-H., Shin, J.-H., & Lee, D.-H. (2021). Fast and meta heuristics for part selection in flexible manufacturing systems with controllable processing times. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 235(4), 650–662. https://doi.org/10.1177/0954405420968172