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How to cite this paper
APA: Renna, P. (2019). Flexible job-shop scheduling with learning and forgetting effect by Multi-Agent System. International Journal of Industrial Engineering Computations, 10(4), 521-534.
Chicago/Turabian: Renna, P. 2019. "Flexible job-shop scheduling with learning and forgetting effect by Multi-Agent System." International Journal of Industrial Engineering Computations 10, no. 4 (2019): 521-534.
AMA: Renna, P. Flexible job-shop scheduling with learning and forgetting effect by Multi-Agent System. International Journal of Industrial Engineering Computations. 2019;10(4):521-534.
References
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Azzouz, A., Ennigrou, M., & Ben Said, L. (2018). Scheduling problems under learning effects: classification and cartography. International Journal of Production Research, 56(4), 1642-1661.
Bai, D., Tang, M., Zhang, Z. H., & Santibanez-Gonzalez, E. D. (2018). Flow shop learning effect scheduling problem with release dates. Omega, 78, 21-38.
Biel, K., & Glock, C.H. (2018). Governing the dynamics of multi- stage production systems subject to learning and forgetting effects: A simulation study. International Journal of Production Research, 56(10), 3439-3461
Biskup, D. (2008). A state-of-the-art review on scheduling with learning effects. European Journal of Operational Research, 188(2), 315-329.
Carlson, J. G., & Rowe, A. J. (1976). How much does forgetting cost. Industrial Engineering, 8(9), 40-47.
Gao, F., Liu, M., Wang, J. J., & Lu, Y. Y. (2018). No-wait two-machine permutation flow shop scheduling problem with learning effect, common due date and controllable job processing times. International Journal of Production Research, 56(6), 2361-2369.
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Globerson, S., Levin, N., & Shtub, A. (1989). The impact of breaks on forgetting when performing a repetitive task. IIE transactions, 21(4), 376-381.
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Heydarian, D., & Jolai, F. (2018). Simulation optimization of operator allocation problem with learning effects and server breakdown under uncertainty. Production & Manufacturing Research, 6(1), 396-415.
Lee, W-C , Wu, C-C., & Hsu, P-H. (2011). A single-machine learning effect scheduling problem with release times. Omega, 38(1), 3–11.
Leitão, P., Colombo, A. W., & Karnouskos, S. (2016). Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Computers in Industry, 81, 11-25.
Li, L., Yan, P., Ji, P., & Wang, J. B. (2018a). Scheduling jobs with simultaneous considerations of controllable processing times and learning effect. Neural computing and applications, 29(11), 1155-1162.
Li, X., Jiang, Y., & Ruiz, R. (2018). Methods for scheduling problems considering experience, learning, and forgetting effects. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(5), 743-754.
Liu, C., Wang, J., & Leung, J. Y. T. (2016). Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm. Computers & Industrial Engineering, 96, 162-179.
Nembhard, D. A., & Shafer, S. M. (2008). The effects of workforce heterogeneity on productivity in an experiential learning environment. International journal of production research, 46(14), 3909-3929.
Ranasinghe, T., Senanayake, C. D., & Perera, K. (2018, May). Effects of Non-Homogeneous Learning on the Performance of Serial Production Systems-A Simulation Study. In 2018 Moratuwa Engineering Research Conference (MERCon) (pp. 162-166). IEEE.
Rustogi, K., & Strusevich, V. A. (2014). Combining time and position dependent effects on a single machine subject to rate-modifying activities. Omega, 42(1), 166-178.
Shafer, S. M., Nembhard, D. A., & Uzumeri, M. V. (2001). The effects of worker learning, forgetting, and heterogeneity on assembly line productivity. Management Science, 47(12), 1639-1653.
Tayebi Araghi, M. E., Jolai, F., & Rabiee, M. (2014). Incorporating learning effect and deterioration for solving a SDST flexible job-shop scheduling problem with a hybrid meta-heuristic approach. International Journal of Computer Integrated Manufacturing, 27(8), 733-746.
Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158-168.
Wang, J. B. (2007). Single-machine scheduling problems with the effects of learning and deterioration. Omega, 35(4), 397-402.
Wright, T. P. (1936). Factors Affecting the Cost of Airplanes. Journal of the Aeronautical Sciences, 3(4), 122–128.
Yelle, L. E. (1979). The Learning Curve: Historical Review and Comprehensive Survey. Decision Sciences, 10(2), 302–328.
Azadeh, A., Habibnejad-Ledari, H., Abdolhossein Zadeh, S., and Hosseinabadi Farahani, M. (2017). A Single-machine Scheduling Problem with Learning Effect, Deterioration and Non-monotonic Time-dependent Processing times. International Journal of Computer Integrated Manufacturing, 30(2–3): 292–304.
Azzouz, A., Ennigrou, M., & Ben Said, L. (2018). Scheduling problems under learning effects: classification and cartography. International Journal of Production Research, 56(4), 1642-1661.
Bai, D., Tang, M., Zhang, Z. H., & Santibanez-Gonzalez, E. D. (2018). Flow shop learning effect scheduling problem with release dates. Omega, 78, 21-38.
Biel, K., & Glock, C.H. (2018). Governing the dynamics of multi- stage production systems subject to learning and forgetting effects: A simulation study. International Journal of Production Research, 56(10), 3439-3461
Biskup, D. (2008). A state-of-the-art review on scheduling with learning effects. European Journal of Operational Research, 188(2), 315-329.
Carlson, J. G., & Rowe, A. J. (1976). How much does forgetting cost. Industrial Engineering, 8(9), 40-47.
Gao, F., Liu, M., Wang, J. J., & Lu, Y. Y. (2018). No-wait two-machine permutation flow shop scheduling problem with learning effect, common due date and controllable job processing times. International Journal of Production Research, 56(6), 2361-2369.
Outlook, G. M. (2015). Preparing for battle: Manufacturers get ready for transformation. KPMG.—2015.—34 p.[Web resource].—link: https://www. kpmg. com/CN/en/IssuesAndInsights/ArticlesPublications/Documents/Global-Manufacturing-Outlook-O-201506. pdf.
Globerson, S., Levin, N., & Shtub, A. (1989). The impact of breaks on forgetting when performing a repetitive task. IIE transactions, 21(4), 376-381.
Glock, C. H., Grosse, E. H., Jaber, M. Y., & Smunt, T. L. (2018). Applications of learning curves in production and operations management: A systematic literature review. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2018.10.030.
Heydarian, D., & Jolai, F. (2018). Simulation optimization of operator allocation problem with learning effects and server breakdown under uncertainty. Production & Manufacturing Research, 6(1), 396-415.
Lee, W-C , Wu, C-C., & Hsu, P-H. (2011). A single-machine learning effect scheduling problem with release times. Omega, 38(1), 3–11.
Leitão, P., Colombo, A. W., & Karnouskos, S. (2016). Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Computers in Industry, 81, 11-25.
Li, L., Yan, P., Ji, P., & Wang, J. B. (2018a). Scheduling jobs with simultaneous considerations of controllable processing times and learning effect. Neural computing and applications, 29(11), 1155-1162.
Li, X., Jiang, Y., & Ruiz, R. (2018). Methods for scheduling problems considering experience, learning, and forgetting effects. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(5), 743-754.
Liu, C., Wang, J., & Leung, J. Y. T. (2016). Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm. Computers & Industrial Engineering, 96, 162-179.
Nembhard, D. A., & Shafer, S. M. (2008). The effects of workforce heterogeneity on productivity in an experiential learning environment. International journal of production research, 46(14), 3909-3929.
Ranasinghe, T., Senanayake, C. D., & Perera, K. (2018, May). Effects of Non-Homogeneous Learning on the Performance of Serial Production Systems-A Simulation Study. In 2018 Moratuwa Engineering Research Conference (MERCon) (pp. 162-166). IEEE.
Rustogi, K., & Strusevich, V. A. (2014). Combining time and position dependent effects on a single machine subject to rate-modifying activities. Omega, 42(1), 166-178.
Shafer, S. M., Nembhard, D. A., & Uzumeri, M. V. (2001). The effects of worker learning, forgetting, and heterogeneity on assembly line productivity. Management Science, 47(12), 1639-1653.
Tayebi Araghi, M. E., Jolai, F., & Rabiee, M. (2014). Incorporating learning effect and deterioration for solving a SDST flexible job-shop scheduling problem with a hybrid meta-heuristic approach. International Journal of Computer Integrated Manufacturing, 27(8), 733-746.
Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158-168.
Wang, J. B. (2007). Single-machine scheduling problems with the effects of learning and deterioration. Omega, 35(4), 397-402.
Wright, T. P. (1936). Factors Affecting the Cost of Airplanes. Journal of the Aeronautical Sciences, 3(4), 122–128.
Yelle, L. E. (1979). The Learning Curve: Historical Review and Comprehensive Survey. Decision Sciences, 10(2), 302–328.