How to cite this paper
Fu, G., Yu, Y., Sun, W & Kaku, I. (2023). To reduce maximum tardiness by Seru Production: model, cooperative algorithm combining reinforcement learning and insights.International Journal of Industrial Engineering Computations , 14(1), 65-82.
Refrences
Allahverdi, A. (2004). A new heuristic for m-machine flowshop scheduling problem with bicriteria of makespan and maximum tardiness. Computers & Operations Research, 31(2), 157-180.
Arviv, K., Stern, H., & Edan, Y. (2016). Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem. International Journal of Production Research, 54(4), 1196-1209.
Assarzadegan, P., & Rasti-Barzoki, M. (2016). Minimizing sum of the due date assignment costs, maximum tardiness and distribution costs in a supply chain scheduling problem. Applied Soft Computing, 47, 343-356.
Aydilek, H., Aydilek, A., Allahverdi, M., & Allahverdi, A. (2022). More effective heuristics for a two-machine no-wait flowshop to minimize maximum lateness. International Journal of Industrial Engineering Computations, 13(4), 543-556.
Aydin, M. E., & Öztemel, E. (2000). Dynamic job-shop scheduling using reinforcement learning agents. Robotics and Autonomous Systems, 33(2-3), 169-178.
Bai, D., Bai, X., Yang, J., Zhang, X., Ren, T., Xie, C., & Liu, B. (2021). Minimization of maximum lateness in a flowshop learning effect scheduling with release dates. Computers & Industrial Engineering, 158, 107309.
Berahhou, A., Benadada, Y., & Bouanane, K. (2022). Memetic algorithm for the dynamic vehicle routing problem with simultaneous delivery and pickup. International Journal of Industrial Engineering Computations, 13(4), 587-600.
Beyer, H. G., & Deb, K. (2001). On self-adaptive features in real-parameter evolutionary algorithms. IEEE Transactions on evolutionary computation, 5(3), 250-270.
Chakravarthy, K., & Rajendran, C. (1999). A heuristic for scheduling in a flowshop with the bicriteria of makespan and maximum tardiness minimization. Production Planning & Control, 10(7), 707-714.
Chen, R., Yang, B., Li, S., & Wang, S. (2020). A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem. Computers & Industrial Engineering, 149, 106778.
Chen, R., Yuan, J., Ng, C. T., & Cheng, T. C. E. (2021). Bicriteria scheduling to minimize total late work and maximum tardiness with preemption. Computers & Industrial Engineering, 159, 107525.
Davis, L. (1985, August). Applying adaptive algorithms to epistatic domains. In IJCAI (Vol. 85, pp. 162-164).
Fu, G., Han, C., Yu, Y., Sun, W., & Kaku, I. (2022). A phased intelligent algorithm for dynamic seru production considering seru formation changes. Applied Intelligence, 1-22. https://doi.org/10.1007/s10489-022-03579-0
Guinet, A. G. P., & Solomon, M. M. (1996). Scheduling hybrid flowshops to minimize maximum tardiness or maximum completion time. International journal of production research, 34(6), 1643-1654.
Kaku, I., Gong, J., Tang, J., & Yin, Y. (2009). Modeling and numerical analysis of line-cell conversion problems. International Journal of Production Research, 47(8), 2055-2078. https://doi.org/10.1080/00207540802275889
Li, Z., Wei, X., Jiang, X., & Pang, Y. (2021). A kind of reinforcement learning to improve genetic algorithm for multiagent task scheduling. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/1796296
Lian, J., Liu, C., Li, W., & Yin, Y. (2018). A multi-skilled worker assignment problem in seru production systems considering the worker heterogeneity. Computers & Industrial Engineering, 118, 366-382. https://doi.org/10.1016/j.cie.2018.02.035
Liu, C., Li, W., Lian, J., & Yin, Y. (2012). Reconfiguration of assembly systems: From conveyor assembly line to serus. Journal of Manufacturing Systems, 31(3), 312-325. https://doi.org/10.1016/j.jmsy.2012.02.003
Liu, C., Lian, J., Yin, Y., & Li, W. (2010). Seru Seisan‐an innovation of the production management Mode in Japan. Asian Journal of Technology Innovation, 18(2), 89-113. https://doi.org/10.1080/19761597.2010.9668694
Liu, C., Stecke, K. E., Lian, J., & Yin, Y. (2014). An implementation framework for seru production. International Transactions in Operational Research, 21(1), 1-19. https://doi.org/10.1111/itor.12014
Liu, C., Yang, N., Li, W., Lian, J., Evans, S., & Yin, Y. (2013). Training and assignment of multi-skilled workers for implementing seru production systems. The International Journal of Advanced Manufacturing Technology, 69(5), 937-959.
Liu, F., Fang, K., Tang, J., & Yin, Y. (2022). Solving the rotating seru production problem with dynamic multi-objective evolutionary algorithms. Journal of Management Science and Engineering, 7(1), 48-66.
Liu, F., Niu, B., Xing, M., Wu, L., & Feng, Y. (2021). Optimal cross-trained worker assignment for a hybrid seru production system to minimize makespan and workload imbalance. Computers & Industrial Engineering, 160, 107552.
Ni, F., Hao, J., Lu, J., Tong, X., Yuan, M., Duan, J., ... & He, K. (2021, August). A Multi-Graph Attributed Reinforcement Learning Based Optimization Algorithm for Large-Scale Hybrid Flow Shop Scheduling Problem. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3441-3451).
Pundoor, G., & Chen, Z. L. (2005). Scheduling a production–distribution system to optimize the tradeoff between delivery tardiness and distribution cost. Naval Research Logistics (NRL), 52(6), 571-589.
Ren, J., Ye, C., & Yang, F. (2021). Solving flow-shop scheduling problem with a reinforcement learning algorithm that generalizes the value function with neural network. Alexandria Engineering Journal, 60(3), 2787-2800.
Ren, Z., Pang, B., Wang, M., Feng, Z., Liang, Y., Chen, A., & Zhang, Y. (2019). Surrogate model assisted cooperative coevolution for large scale optimization. Applied Intelligence, 49(2), 513-531.
Rostami, M., Kheirandish, O., & Ansari, N. (2015). Minimizing maximum tardiness and delivery costs with batch delivery and job release times. Applied Mathematical Modelling, 39(16), 4909-4927. https://doi.org/10.1016/j.apm.2015.03.052
Ruiz, R., & Allahverdi, A. (2009). Minimizing the bicriteria of makespan and maximum tardiness with an upper bound on maximum tardiness. Computers & Operations Research, 36(4), 1268-1283.
Sbihi, M., & Varnier, C. (2008). Single-machine scheduling with periodic and flexible periodic maintenance to minimize maximum tardiness. Computers & Industrial Engineering, 55(4), 830-840. https://doi.org/10.1016/j.cie.2008.03.005
Shahrabi, J., Adibi, M. A., & Mahootchi, M. (2017). A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Computers & Industrial Engineering, 110, 75-82. https://doi.org/10.1016/j.cie.2017.05.026
Shang, R., Wang, Y., Wang, J., Jiao, L., Wang, S., & Qi, L. (2014). A multi-population cooperative coevolutionary algorithm for multi-objective capacitated arc routing problem. Information Sciences, 277, 609-642.
Stecke, K. E., Yin, Y., Kaku, I., & Murase, Y. (2012). Seru: the organizational extension of JIT for a super-talent factory. International Journal of Strategic Decision Sciences (IJSDS), 3(1), 106-119. https://doi.org/10.4018/jsds.2012010104
Sun, W., Wu, Y., Lou, Q., & Yu, Y. (2019). A cooperative coevolution algorithm for the seru production with minimizing makespan. IEEE Access, 7, 5662-5670.
Sun, W., Yu, Y., Lou, Q., Wang, J., & Guan, Y. (2020). Reducing the total tardiness by Seru production: model, exact and cooperative coevolution solutions. International Journal of Production Research, 58(21), 6441-6452.
Tang, J., Yang, Y., & Qi, Y. (2018). A hybrid algorithm for urban transit schedule optimization. Physica A: Statistical Mechanics and its Applications, 512, 745-755.
Tang, R. (2017). Decentralizing and coevolving differential evolution for large-scale global optimization problems. Applied Intelligence, 47(4), 1208-1223. https://doi.org/10.1007/s10489-017-0953-9
Wang, H., Sarker, B. R., Li, J., & Li, J. (2021). Adaptive scheduling for assembly job shop with uncertain assembly times based on dual Q-learning. International Journal of Production Research, 59(19), 5867-5883.
Wei, Y., & Zhao, M. (2004). Composite rules selection using reinforcement learning for dynamic job-shop scheduling, in: IEEE Conference on Robotics, Automation and Mechatronics, 2004. IEEE, pp. 1083–1088.
Wu, Y., Wang, L., & Chen, J. F. (2021). A cooperative coevolution algorithm for complex hybrid seru-system scheduling optimization. Complex & Intelligent Systems, 7(5), 2559-2576.
Ying, K. C., & Tsai, Y. J. (2017). Minimising total cost for training and assigning multiskilled workers in seru production systems. International Journal of Production Research, 55(10), 2978-2989.
Ke, F., Zhao, D., Sun, G., & Feng, W. (2019, June). Precise Evaluation for Continuous Action Control in Reinforcement Learning. In Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference (pp. 67-70).
Yılmaz, Ö. F. (2020a). Operational strategies for seru production system: a bi-objective optimisation model and solution methods. International Journal of Production Research, 58(11), 3195-3219.
Yılmaz, Ö. F. (2020). Attaining flexibility in seru production system by means of Shojinka: An optimization model and solution approaches. Computers & Operations Research, 119, 104917.
Yu, Y., Gong, J., Tang, J., Yin, Y., & Kaku, I. (2012). How to carry out assembly line–cell conversion? A discussion based on factor analysis of system performance improvements. International Journal of Production Research, 50(18), 5259-5280.
Yu, Y., Sun, W., Tang, J., & Wang, J. (2017). Line-hybrid seru system conversion: Models, complexities, properties, solutions and insights. Computers & Industrial Engineering, 103, 282-299.
Yu, Y., Tang, J., Gong, J., Yin, Y., & Kaku, I. (2014). Mathematical analysis and solutions for multi-objective line-cell conversion problem. European Journal of Operational Research, 236(2), 774-786.
Yu, Y., Tang, J., Sun, W., Yin, Y., & Kaku, I. (2013). Reducing worker (s) by converting assembly line into a pure cell system. International Journal of Production Economics, 145(2), 799-806.
Yu, Y., Wang, J., Ma, K., & Sun, W. (2018). Seru system balancing: Definition, formulation, and exact solution. Computers & Industrial Engineering, 122, 318-325.
Zhan, R., Zhang, J., Cui, Z., Peng, J., & Li, D. (2021). An Automatic Heuristic Design Approach for Seru Scheduling Problem with Resource Conflicts. Discrete Dynamics in Nature and Society, 2021. https://doi.org/10.1155/2021/8166343
Zhang, Z., Song, X., Huang, H., Zhou, X., & Yin, Y. (2022). Logic-based Benders decomposition method for the seru scheduling problem with sequence-dependent setup time and DeJong’s learning effect. European Journal of Operational Research, 297(3), 866-877. https://doi.org/10.1016/j.ejor.2021.06.017
Arviv, K., Stern, H., & Edan, Y. (2016). Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem. International Journal of Production Research, 54(4), 1196-1209.
Assarzadegan, P., & Rasti-Barzoki, M. (2016). Minimizing sum of the due date assignment costs, maximum tardiness and distribution costs in a supply chain scheduling problem. Applied Soft Computing, 47, 343-356.
Aydilek, H., Aydilek, A., Allahverdi, M., & Allahverdi, A. (2022). More effective heuristics for a two-machine no-wait flowshop to minimize maximum lateness. International Journal of Industrial Engineering Computations, 13(4), 543-556.
Aydin, M. E., & Öztemel, E. (2000). Dynamic job-shop scheduling using reinforcement learning agents. Robotics and Autonomous Systems, 33(2-3), 169-178.
Bai, D., Bai, X., Yang, J., Zhang, X., Ren, T., Xie, C., & Liu, B. (2021). Minimization of maximum lateness in a flowshop learning effect scheduling with release dates. Computers & Industrial Engineering, 158, 107309.
Berahhou, A., Benadada, Y., & Bouanane, K. (2022). Memetic algorithm for the dynamic vehicle routing problem with simultaneous delivery and pickup. International Journal of Industrial Engineering Computations, 13(4), 587-600.
Beyer, H. G., & Deb, K. (2001). On self-adaptive features in real-parameter evolutionary algorithms. IEEE Transactions on evolutionary computation, 5(3), 250-270.
Chakravarthy, K., & Rajendran, C. (1999). A heuristic for scheduling in a flowshop with the bicriteria of makespan and maximum tardiness minimization. Production Planning & Control, 10(7), 707-714.
Chen, R., Yang, B., Li, S., & Wang, S. (2020). A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem. Computers & Industrial Engineering, 149, 106778.
Chen, R., Yuan, J., Ng, C. T., & Cheng, T. C. E. (2021). Bicriteria scheduling to minimize total late work and maximum tardiness with preemption. Computers & Industrial Engineering, 159, 107525.
Davis, L. (1985, August). Applying adaptive algorithms to epistatic domains. In IJCAI (Vol. 85, pp. 162-164).
Fu, G., Han, C., Yu, Y., Sun, W., & Kaku, I. (2022). A phased intelligent algorithm for dynamic seru production considering seru formation changes. Applied Intelligence, 1-22. https://doi.org/10.1007/s10489-022-03579-0
Guinet, A. G. P., & Solomon, M. M. (1996). Scheduling hybrid flowshops to minimize maximum tardiness or maximum completion time. International journal of production research, 34(6), 1643-1654.
Kaku, I., Gong, J., Tang, J., & Yin, Y. (2009). Modeling and numerical analysis of line-cell conversion problems. International Journal of Production Research, 47(8), 2055-2078. https://doi.org/10.1080/00207540802275889
Li, Z., Wei, X., Jiang, X., & Pang, Y. (2021). A kind of reinforcement learning to improve genetic algorithm for multiagent task scheduling. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/1796296
Lian, J., Liu, C., Li, W., & Yin, Y. (2018). A multi-skilled worker assignment problem in seru production systems considering the worker heterogeneity. Computers & Industrial Engineering, 118, 366-382. https://doi.org/10.1016/j.cie.2018.02.035
Liu, C., Li, W., Lian, J., & Yin, Y. (2012). Reconfiguration of assembly systems: From conveyor assembly line to serus. Journal of Manufacturing Systems, 31(3), 312-325. https://doi.org/10.1016/j.jmsy.2012.02.003
Liu, C., Lian, J., Yin, Y., & Li, W. (2010). Seru Seisan‐an innovation of the production management Mode in Japan. Asian Journal of Technology Innovation, 18(2), 89-113. https://doi.org/10.1080/19761597.2010.9668694
Liu, C., Stecke, K. E., Lian, J., & Yin, Y. (2014). An implementation framework for seru production. International Transactions in Operational Research, 21(1), 1-19. https://doi.org/10.1111/itor.12014
Liu, C., Yang, N., Li, W., Lian, J., Evans, S., & Yin, Y. (2013). Training and assignment of multi-skilled workers for implementing seru production systems. The International Journal of Advanced Manufacturing Technology, 69(5), 937-959.
Liu, F., Fang, K., Tang, J., & Yin, Y. (2022). Solving the rotating seru production problem with dynamic multi-objective evolutionary algorithms. Journal of Management Science and Engineering, 7(1), 48-66.
Liu, F., Niu, B., Xing, M., Wu, L., & Feng, Y. (2021). Optimal cross-trained worker assignment for a hybrid seru production system to minimize makespan and workload imbalance. Computers & Industrial Engineering, 160, 107552.
Ni, F., Hao, J., Lu, J., Tong, X., Yuan, M., Duan, J., ... & He, K. (2021, August). A Multi-Graph Attributed Reinforcement Learning Based Optimization Algorithm for Large-Scale Hybrid Flow Shop Scheduling Problem. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3441-3451).
Pundoor, G., & Chen, Z. L. (2005). Scheduling a production–distribution system to optimize the tradeoff between delivery tardiness and distribution cost. Naval Research Logistics (NRL), 52(6), 571-589.
Ren, J., Ye, C., & Yang, F. (2021). Solving flow-shop scheduling problem with a reinforcement learning algorithm that generalizes the value function with neural network. Alexandria Engineering Journal, 60(3), 2787-2800.
Ren, Z., Pang, B., Wang, M., Feng, Z., Liang, Y., Chen, A., & Zhang, Y. (2019). Surrogate model assisted cooperative coevolution for large scale optimization. Applied Intelligence, 49(2), 513-531.
Rostami, M., Kheirandish, O., & Ansari, N. (2015). Minimizing maximum tardiness and delivery costs with batch delivery and job release times. Applied Mathematical Modelling, 39(16), 4909-4927. https://doi.org/10.1016/j.apm.2015.03.052
Ruiz, R., & Allahverdi, A. (2009). Minimizing the bicriteria of makespan and maximum tardiness with an upper bound on maximum tardiness. Computers & Operations Research, 36(4), 1268-1283.
Sbihi, M., & Varnier, C. (2008). Single-machine scheduling with periodic and flexible periodic maintenance to minimize maximum tardiness. Computers & Industrial Engineering, 55(4), 830-840. https://doi.org/10.1016/j.cie.2008.03.005
Shahrabi, J., Adibi, M. A., & Mahootchi, M. (2017). A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Computers & Industrial Engineering, 110, 75-82. https://doi.org/10.1016/j.cie.2017.05.026
Shang, R., Wang, Y., Wang, J., Jiao, L., Wang, S., & Qi, L. (2014). A multi-population cooperative coevolutionary algorithm for multi-objective capacitated arc routing problem. Information Sciences, 277, 609-642.
Stecke, K. E., Yin, Y., Kaku, I., & Murase, Y. (2012). Seru: the organizational extension of JIT for a super-talent factory. International Journal of Strategic Decision Sciences (IJSDS), 3(1), 106-119. https://doi.org/10.4018/jsds.2012010104
Sun, W., Wu, Y., Lou, Q., & Yu, Y. (2019). A cooperative coevolution algorithm for the seru production with minimizing makespan. IEEE Access, 7, 5662-5670.
Sun, W., Yu, Y., Lou, Q., Wang, J., & Guan, Y. (2020). Reducing the total tardiness by Seru production: model, exact and cooperative coevolution solutions. International Journal of Production Research, 58(21), 6441-6452.
Tang, J., Yang, Y., & Qi, Y. (2018). A hybrid algorithm for urban transit schedule optimization. Physica A: Statistical Mechanics and its Applications, 512, 745-755.
Tang, R. (2017). Decentralizing and coevolving differential evolution for large-scale global optimization problems. Applied Intelligence, 47(4), 1208-1223. https://doi.org/10.1007/s10489-017-0953-9
Wang, H., Sarker, B. R., Li, J., & Li, J. (2021). Adaptive scheduling for assembly job shop with uncertain assembly times based on dual Q-learning. International Journal of Production Research, 59(19), 5867-5883.
Wei, Y., & Zhao, M. (2004). Composite rules selection using reinforcement learning for dynamic job-shop scheduling, in: IEEE Conference on Robotics, Automation and Mechatronics, 2004. IEEE, pp. 1083–1088.
Wu, Y., Wang, L., & Chen, J. F. (2021). A cooperative coevolution algorithm for complex hybrid seru-system scheduling optimization. Complex & Intelligent Systems, 7(5), 2559-2576.
Ying, K. C., & Tsai, Y. J. (2017). Minimising total cost for training and assigning multiskilled workers in seru production systems. International Journal of Production Research, 55(10), 2978-2989.
Ke, F., Zhao, D., Sun, G., & Feng, W. (2019, June). Precise Evaluation for Continuous Action Control in Reinforcement Learning. In Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference (pp. 67-70).
Yılmaz, Ö. F. (2020a). Operational strategies for seru production system: a bi-objective optimisation model and solution methods. International Journal of Production Research, 58(11), 3195-3219.
Yılmaz, Ö. F. (2020). Attaining flexibility in seru production system by means of Shojinka: An optimization model and solution approaches. Computers & Operations Research, 119, 104917.
Yu, Y., Gong, J., Tang, J., Yin, Y., & Kaku, I. (2012). How to carry out assembly line–cell conversion? A discussion based on factor analysis of system performance improvements. International Journal of Production Research, 50(18), 5259-5280.
Yu, Y., Sun, W., Tang, J., & Wang, J. (2017). Line-hybrid seru system conversion: Models, complexities, properties, solutions and insights. Computers & Industrial Engineering, 103, 282-299.
Yu, Y., Tang, J., Gong, J., Yin, Y., & Kaku, I. (2014). Mathematical analysis and solutions for multi-objective line-cell conversion problem. European Journal of Operational Research, 236(2), 774-786.
Yu, Y., Tang, J., Sun, W., Yin, Y., & Kaku, I. (2013). Reducing worker (s) by converting assembly line into a pure cell system. International Journal of Production Economics, 145(2), 799-806.
Yu, Y., Wang, J., Ma, K., & Sun, W. (2018). Seru system balancing: Definition, formulation, and exact solution. Computers & Industrial Engineering, 122, 318-325.
Zhan, R., Zhang, J., Cui, Z., Peng, J., & Li, D. (2021). An Automatic Heuristic Design Approach for Seru Scheduling Problem with Resource Conflicts. Discrete Dynamics in Nature and Society, 2021. https://doi.org/10.1155/2021/8166343
Zhang, Z., Song, X., Huang, H., Zhou, X., & Yin, Y. (2022). Logic-based Benders decomposition method for the seru scheduling problem with sequence-dependent setup time and DeJong’s learning effect. European Journal of Operational Research, 297(3), 866-877. https://doi.org/10.1016/j.ejor.2021.06.017