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Scheduling problem in seru production system considering DeJong’s learning effect and job splitting

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Abstract

Seru is a relatively new type of Japanese production mode originated from the electronic assembly industry. In practice, seru production has been proven to be efficient, flexible, response quickly, and can cope with the fluctuating production demands in a current volatile market. This paper focuses on scheduling problems in seru production system. Motivated by the realty of labor-intensive assembly industry, we consider learning effect of workers and job splitting with the objective of minimizing the total completion time. A nonlinear integer programming model for the seru scheduling problem is provided, and it is proved to be polynomial solvable. Therefore, a branch and bound algorithm is designed for small sized seru scheduling problems, while a local search-based hybrid genetic algorithm employing shortest processing time rule is provided for large sized problems. Finally, computational experiments are conducted, and the results demonstrate the practicability of the proposed seru scheduling model and the efficiency of our solution methods.

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References

  • Al-Hakim, L. (2001). An analogue genetic algorithm for solving job shop scheduling problems. International Journal of Production Research, 39(7), 1537–1548.

    Google Scholar 

  • Ayough, A., Hosseinzadeh, M., & Motameni, A. (2020). Job rotation scheduling in the seru system: Shake enforced invasive weed optimization approach. Assembly Automation, 40(3), 461–474.

    Google Scholar 

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

    Google Scholar 

  • Biskup, D. (1999). Single-machine scheduling with learning considerations. European Journal of Operational Research, 115(1), 173–178.

    Google Scholar 

  • Biskup, D. (2008). A state-of-the-art review on scheduling with effects. European Journal of Operational Research, 188, 315–329.

    Google Scholar 

  • Chen, T., Cheng, C., & Chou, Y. (2020). Multi-objective genetic algorithm for energy-efficient hybrid flow shop scheduling with lot streaming. Annals of Operations Research, 290, 813–836.

    Google Scholar 

  • Cheng, T., Kuo, W., & Yang, D. (2013). Scheduling with a position-weighted learning effect based on sum-of-logarithm-processing-times and job position. Information Sciences, 221, 490–500.

    Google Scholar 

  • Clausen, J., & Perregaard, M. (1999). On the best search strategy in parallel branch-and-bound: Best-first search versus lazy depth-first search. Annals of Operations Research, 90, 1–17.

    Google Scholar 

  • Defersha, F., & Rooyani, D. (2020). An efficient two-stage genetic algorithm for a flexible job-shop scheduling problem with sequence dependent attached/detached setup, machine release date and lag-time. Computers& Industrial Engineering, 147, 106605.

    Google Scholar 

  • D & M Nikkei Mechanical. (2003). The challenge of Canon-Part 3. 588, 70–73.

  • Hardy, G., Littlewood, J., & Polya, G. (1967). Inequalities. Cambridge: Cambridge University Press.

    Google Scholar 

  • Hisashi, S. (2006). The change of consciousness and company by cellular manufacturing in Canon Way. Tokyo: JMAM. (in Japanese).

    Google Scholar 

  • Huang, R. (2010). Multi-objective job-shop scheduling with lot-splitting production. International Journal of Production Economics, 124(1), 206–213.

    Google Scholar 

  • Huang, R., & Yu, T. (2017). An effective ant colony optimization algorithm for multi-objective job-shop scheduling with equal-size lot-splitting. Applied Soft Computing, 57, 642–656.

    Google Scholar 

  • Janiak, A., Kovalyov, M., & Lichtenstein, M. (2013). Strong NP-hardness of scheduling problems with learning or aging effect. Annals of Operations Research, 206(1), 577–583.

    Google Scholar 

  • Ji, M., & Cheng, T. (2010). Scheduling with job-dependent learning effects and multiple rate-modifying activities. Information Processing Letters, 110, 460–463.

    Google Scholar 

  • Jiang, Y., Zhang, Z., Gong, X., & Yin, Y. (2021). An exact solution method for solving seru scheduling problems with past-sequence-dependent setup time and learning effect. Computers& Industrial Engineering, 158, 107354.

    Google Scholar 

  • Kaku, I. (2017). Is seru a sustainable manufacturing system? Procedia Manufacturing, 8, 723–730.

    Google Scholar 

  • Kaku, I., Gong, J., Tang, J., & Yin, Y. (2009). Modelling and numerical analysis of line-cell conversion problems. International Journal of Production Research, 47(8), 2055–2078.

    Google Scholar 

  • Kim, H. (2018). Bounds for parallel machine scheduling with predefined parts of jobs and setup time. Annals of Operations Research, 261, 401–412.

    Google Scholar 

  • Kim, Y., & Kim, R. (2020). Insertion of new idle time for unrelated parallel machine scheduling with job splitting and machine breakdowns. Computers& Industrial Engineering, 147, 106630.

    Google Scholar 

  • Kimura, T., & Yoshita, M. (2004). Remaining the current situation is dangerous: Seru Seisan. Nikkei Monozukuri, 7, 38–61. (In Japanese).

    Google Scholar 

  • Kono, H. (2004). The aim of the special issue on seru manufacturing. IE Review, 45(1), 4–5.

    Google Scholar 

  • Li, X., & Gao, L. (2016). An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. International Journal of Production Economics, 174, 93–110.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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. International Journal of Advanced Manufacturing Technology, 69(5–8), 937–959.

    Google Scholar 

  • Liu, C., Stecke, K., Lian, J., & Yin, Y. (2014a). An implementation framework for seru production. International Transactions in Operational Research, 21(1), 1–19.

  • Liu, C., Wang, C., Zhang, Z., & Zheng, L. (2014b). Scheduling with job-splitting considering learning and the vital-few law. Computers & Operations Research, 90, 264–274.

  • Luo, L., Zhang, Z., & Yin, Y. (2016). Seru loading with worker-operation assignment in single period. In 2016 IEEE international conference on industrial engineering and engineering management (IEEM) (pp. 1055–1058).

  • Luo, L., Zhang, Z., & Yin, Y. (2017). Modelling and numerical analysis of seru loading problem under uncertainty. European Journal of Industrial Engineering, 11(2), 185–204.

    Google Scholar 

  • Luo, L., Zhang, Z., & Yin, Y. (2021). Simulated annealing and genetic algorithm based method for a bi-level seru loading problem with worker assignment in seru production systems. Journal of Industrial and Management Optimization, 17(2), 779–803.

    Google Scholar 

  • Mosheiov, G. (2001). Scheduling problems with a learning effect. European Journal of Operational Research, 132(3), 687–693.

    Google Scholar 

  • Mosheiov, G., & Sidney, J. (2003). Scheduling with general job-dependent learning curves. European Journal of Operational Research, 147(3), 665–670.

    Google Scholar 

  • Nessah, R., & Chu, C. (2010). Infinite split scheduling: a new lower bound of total weighted completion time on parallel machines with job release dates and unavailability periods. Annals of Operations Research, 181, 359–375.

    Google Scholar 

  • Nikkei-Business. (2016). How to handle Prius: Its delivery time is more than half a year. Tokyo: Nikkei Business. (in Japanese).

  • Noguchi, H. (2003). Production innovation in Japan. Nikkan Kogyo Shimbun. (in Japanese).

  • Pei, J., Cheng, B., Liu, X., Pardalos, P., & Kong, M. (2019). Single-machine and parallel-machine serial-batching scheduling problems with position-based learning effect and linear setup time. Annals of Operations Research, 272, 217–241.

    Google Scholar 

  • Rostami, M., Nikravesh, S., & Shahin, M. (2020). Minimizing total weighted completion and batch delivery times with machine deterioration and learning effect: A case study from wax production. Operational Research, 20(3), 1255–1287.

    Google Scholar 

  • Roth, A., Singhal, J., Singhal, K., & Tang, C. (2016). Knowledge creation and dissemination in operations and supply chain management. Production and Operations Management, 25(9), 1473–1488.

    Google Scholar 

  • Shao, L., Zhang, Z., & Yin, Y. (2016). A bi-objective combination optimisation model for line-seru conversion based on queuing theory. International Journal of Manufacturing Research, 11(4), 322–338.

    Google Scholar 

  • Stecke, K., Yin, Y., Kaku, I., & Murase, Y. (2012). Seru: The organizational extension of JIT for a super-talent factory. International Journal of Strategic Decision Sciences, 3(1), 105–118.

    Google Scholar 

  • Sun, L., Cui, K., Chen, J., Wang, J., & He, X. (2013). Some results of the worst-case analysis for flow shop scheduling with a learning effect. Annals of Operations Research, 211, 481–490.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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 Reseaech, 58(21), 6441–6452.

    Google Scholar 

  • Treville, S., Ketokivi, M., & Singhal, V. (2017). Competitive manufacturing in a high-cost environment: Introduction to the special issue. Journal of Operations Management, 49–51, 1–5.

    Google Scholar 

  • Wang, L., Zhang, Z., & Yin, Y. (2019). Order acceptance and scheduling considering lot-spitting in seru production system. In Proceeding of 2019 IEEE international conference on industrial engineering and engineering management (pp. 1305-1309).

  • Wang, J., Liu, C., & Zhou, M. (2020). Improved bacterial foraging algorithm for cell formation and product scheduling considering learning and forgetting factors in cellular manufacturing systems. IEEE Systems Journal, 14(2), 3047–3056.

    Google Scholar 

  • Wright, T. (1936). Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 3, 122–128.

    Google Scholar 

  • Yamada, H. (2009). Waste reduction. Tokyo: Gentosha. (in Japanese).

    Google Scholar 

  • Yin, Y., Stecke, K., Swink, M., & Kaku, I. (2017). Lessons from seru, production on manufacturing competitively in a high cost environment. Journal of Operations Management, 49–51, 67–76.

    Google Scholar 

  • Yin, Y., Stecke, K. E., Li, D. (2018). The evolution of production systems from Industry 2.0 through Industry 4.0. International Journal of Production Research, 56(1&2), 848–861.

  • Yılmaz, Ö. (2020a). Attaining flexibility in seru production system by means of Shojinka: An optimization model and solution approaches. Computers & Operations Research, 119, 104917.

  • Yılmaz, Ö. (2020b). Operational strategies for seru production system: A bi-objective optimisation model and solution methods. International Journal of Production Research, 58(11), 3195–3219.

  • Yu, Y., & Tang, J. (2019). Review of seru production. Frontiers of Engineering Management, 6(2), 183–192.

    Google Scholar 

  • 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.

    Google Scholar 

  • Yu, Y., Tang, T., Yin, Y., & Kaku, I. (2013). Reducing worker(s) by converting assembly line into a pure cell system. International Journal of Production Economics, 145, 799–806.

    Google Scholar 

  • Yu, Y., Tang, T., Yin, Y., & Kaku, I. (2014). Mathematical analysis and solutions for multi-objective line-cell conversion problem. European Journal of Operational Research, 236, 774–786.

    Google Scholar 

  • 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.

    Google Scholar 

  • Zhang, Y., Li, X., & Wang, Q. (2009). Hybrid genetic algorithm for permutation flowshop scheduling problems with total flowtime minimization. European Journal of Operational Research, 193, 869–876.

    Google Scholar 

  • Zhang, X., Liu, C., Li, W., Evans, S., & Yin, Y. (2017). Effects of key enabling technologies for seru production on sustainable performance. Omega, 66, 290–307.

    Google Scholar 

  • Zhang, Z., Wang, L., Song, X., Huang, H., & Yin, Y. (2021). Improved genetic-simulated annealing algorithm for seru loading problem with downward substitution under stochastic environment. Journal of the Operational Research Society. https://doi.org/10.1080/01605682.2021.1939172

    Article  Google Scholar 

  • 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, 866–877.

    Google Scholar 

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Acknowledgements

This research was sponsored by National Natural Science Foundation of China (Grant Nos. 71401075, 71801129), the Natural Science Foundation of Jiangsu Province (Grant No. BK20180452), and the Fundamental Research Funds for the Central Universities (Grant No. 30920010021). We would like to give our great appreciation to all the reviewers and editors who contributed this research.

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Authors and Affiliations

  1. School Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Zhe Zhang, Xiaoling Song & Huijun Huang

  2. Graduate School of Business, Doshisha University, Karasuma-Imadegawa, Kamigyo-ku, Kyoto, 602-8580, Japan

    Yong Yin

  3. Drexel University, Philadelphia, PA, 19104, USA

    Benjamin Lev

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  1. Zhe Zhang
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Correspondence to Zhe Zhang.

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Zhang, Z., Song, X., Huang, H. et al. Scheduling problem in seru production system considering DeJong’s learning effect and job splitting. Ann Oper Res 312, 1119–1141 (2022). https://doi.org/10.1007/s10479-021-04515-0

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