How to cite this paper
Shan, H., Shan, X., Zhang, L., Qin, M., Peng, P & Meng, Z. (2024). A case study of whale optimization algorithm for scheduling in C2M model.International Journal of Industrial Engineering Computations , 15(2), 387-414.
Refrences
Anzanello, M. J., & Fogliatto, F. S. (2011). Learning curve models and applications: Literature review and research directions. International Journal of Industrial Ergonomics, 41(5), 573-583.
Azizi, N., Zolfaghari, S., & Liang, M. (2010). Modeling job rotation in manufacturing systems: The study of employee's boredom and skill variations. International Journal of Production Economics, 123(1), 69-85.
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.
Cavagnini, R., Hewitt, M., & Maggioni, F. (2020). Workforce production planning under uncertain learning rates. International Journal of Production Economics, 225(7), 107590.
Chakraborty, S., Sharma, S., Saha, A. K., & Saha, A. (2022). A novel improved whale optimization algorithm to solve numerical optimization and real-world applications. Artificial Intelligence Review, 55(6), 4605-4716.
Cohen, Y., & Ezey Dar-El, M. (1998). Optimizing the number of stations in assembly lines under learning for limited production. Production Planning & Control, 9(3), 230-240.
Corominas, A., Pastor, R., & Plans, J. (2008). Balancing assembly line with skilled and unskilled workers. Omega, 36(6), 1126-1132.
De Jong, J. R. (1957). The Effects of Increasing Skill on Cycle Time and Its Consequences for Time Standards. Ergonomics, 1(1), 51-60.
El-Dabah, M., Ebrahim, M. A., El-Sehiemy, R. A., Alaas, Z., & Ramadan, M. M. (2022). A Modified Whale Optimizer for Single- and Multi-Objective OPF Frameworks [Article]. Energies (19961073), 15(7), 2378-N.PAG.
Emmons, H., & Fuh, D.-S. (1997). Sizing and scheduling a full-time and part-time workforce with off-day and off-weekend constraints. Annals of Operations Research, 70(0), 473-492.
Erdem, Y. (2011). Workforce Planning for Seasonal Demands (Publication Number 28554014) Marmara Universitesi (Turkey)]. ProQuest Dissertations and Theses Full-text Search Platform.
Fichera, S., Costa, A., & Cappadonna, F. A. (2017). Heterogeneous workers with learning ability assignment in a cellular manufacturing system. International Journal of Industrial Engineering Computations, 8(4), 427-440.
Foote, D. A., & Folta, T. B. (2002). Temporary workers as real options. Human Resource Management Review, 12(4), 579-597.
Karaoz, M., & Albeni, M. (2005). Dynamic technological learning trends in Turkish manufacturing industries. Technological Forecasting and Social Change, 72(7), 866-885.
Kim, D., Moon, D. H., & Moon, I. (2018). Balancing a mixed-model assembly line with unskilled temporary workers: algorithm and case study. ASSEMBLY AUTOMATION, 38(4), 511-523.
Lee, B., Lee, H. S., Lee, S., & Park, M. (2015). Influence Factors of Learning-Curve Effect in High-Rise Building Constructions. Journal of Construction Engineering and Management, 141(8), Article 04015019.
Lev, B., & Withers, D. (2002). Human Learning: From Learning Curves to Learning Organizations [Book Review]. Interfaces, 32(1), 95-96.
Li, Q., Sun, Q., Tao, S., & Gao, X. (2019). Multi-skill project scheduling with skill evolution and cooperation effectiveness. Engineering, Construction and Architectural Management, 27(8), 2023-2045.
Li, Y., Yang, X., & Yang, Z. (2019). Uncertain learning curve and its application in scheduling. Computers & Industrial Engineering, 131, 534-541.
Liu, C., Wang, J., & Leung, J. (2016). Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm. Computers & Industrial Engineering, 96(C), 162-179.
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.
Liu, M., Liu, Z., Chu, F., Liu, R., Zheng, F., & Chu, C. (2022). Risk-averse assembly line worker assignment and balancing problem with limited temporary workers and moving workers. International Journal of Production Research, 60(23), 7074-7092.
Liu, R., Liu, M., Chu, F., Zheng, F., & Chu, C. (2021). Eco-friendly multi-skilled worker assignment and assembly line balancing problem. Computers & Industrial Engineering, 151, 106944.
Lohmann, M., Anzanello, M. J., Fogliatto, F. S., & da Silveira, G. C. (2019a). Grouping workers with similar learning profiles in mass customization production lines. Computers & Industrial Engineering, 131(5), 542-551.
Lohmann, M., Anzanello, M. J., Fogliatto, F. S., & da Silveira, G. C. (2019b). Grouping workers with similar learning profiles in mass customization production lines. Computers & Industrial Engineering, 131, 542-551.
Mathur, K., & Süer, G. A. (2013). Math modeling and GA approach to simultaneously make overtime decisions, load cells and sequence products. Computers & Industrial Engineering, 66(3), 614-624.
Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in engineering software, 95, 51-67.
Neidigh, R. O., & Harrison, T. P. (2010). Optimising lot sizing and order scheduling with non-linear production rates. International Journal of Production Research, 48(7-8), 2279-2295.
Paul, C., Kumar Roy, P., & Mukherjee, V. (2023). Study of wind‐solar based combined heat and power economic dispatch problem using quasi‐oppositional‐based whale optimization technique. Optimal Control Applications and Methods, 44(2), 480-507.
Pilati, F., Faccio, M., & Cohen, Y. (2021). Absenteeism and Turnover as Motivation Factors for Segmenting Assembly Lines. IFAC-PapersOnLine, 54(1), 613-616.
Pinker, E. J., & Larson, R. C. (2003). Optimizing the use of contingent labor when demand is uncertain. European Journal of Operational Research, 144(1), 39-55.
Prasad, P., Wallace, L., Navidi, M., & Phillips, A. W. (2022). Learning curves in minimally invasive esophagectomy: A systematic review and evaluation of benchmarking parameters. Surgery, 171(5), 1247-1256.
Ruihan, C. J. R. H. Z. Z. L. (2021). An Adaptive Evolutionary Whale Optimization Algorithm. College of Information Science & Technology, Beijing University of Chemical Technology;Systems Engineering Research Institute.
Schott, J. R. (1995). Fault tolerant design using single and multicriteria genetic algorithm optimization Massachusetts Institute of Technology.
Shi, S., Xiong, H., & Li, G. (2023). A no-tardiness job shop scheduling problem with overtime consideration and the solution approaches. Computers & Industrial Engineering, 178, 109115.
Singh, S., & Singh, D. (2023). A Bio-inspired VM Migration using Re-initialization and Decomposition Based-Whale Optimization. ICT Express, 9(1), 92-99.
Stratman, J. K., Roth, A. V., & Gilland, W. G. (2004). The deployment of temporary production workers in assembly operations: a case study of the hidden costs of learning and forgetting. Journal of Operations Management, 21(6), 689-707.
Sun, G., Shang, Y., & Zhang, R. (2022). An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems. Electronics, 11(1475), 1475.
Tai, H.-W., Chen, J.-H., Cheng, J.-Y., Hsu, S.-C., & Wei, H.-H. (2021). Learn Curve for Precast Component Productivity in Construction. International Journal of Civil Engineering, 19(10), 1179-1194.
Tang, B., Liang, Y., Shi, J., & Li, T. (2022). Learning Curve of Robotic Right Hemicolectomy. Journal of Gastrointestinal Surgery, 26(10), 2215-2217.
Tian, Z., Jiang, X., Liu, W., & Li, Z. (2023). Dynamic energy-efficient scheduling of multi-variety and small batch flexible job-shop: A case study for the aerospace industry. Computers & Industrial Engineering, 178, 109111.
Valsamis, E. M., Chouari, T., O'Dowd-Booth, C., Rogers, B., & Ricketts, D. (2018). Learning curves in surgery: variables, analysis and applications. POSTGRADUATE MEDICAL JOURNAL, 94(1115), 525-530.
Van Veldhuizen, D. A., & Lamont, G. B. (2000). Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art. Evolutionary Computation 8(2), 125-147.
Wright, T. P. (1936). Factors Affecting the Cost of Airplanes. Journal of the Aeronautical Sciences (Institute of the Aeronautical Sciences), 3(4), 122-128.
Zhi, R., & Xu, Y. (2019). Optimal workforce and machine scheduling to maximize profit and worker satisfaction. Glorious Sun School of Business and Management, Donghua University, Shanghai, China Glorious Sun School of Business and Management, Donghua University, Shanghai, China.
Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. EVOLUTIONARY COMPUTATION, 8(2), 173-195.
Azizi, N., Zolfaghari, S., & Liang, M. (2010). Modeling job rotation in manufacturing systems: The study of employee's boredom and skill variations. International Journal of Production Economics, 123(1), 69-85.
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.
Cavagnini, R., Hewitt, M., & Maggioni, F. (2020). Workforce production planning under uncertain learning rates. International Journal of Production Economics, 225(7), 107590.
Chakraborty, S., Sharma, S., Saha, A. K., & Saha, A. (2022). A novel improved whale optimization algorithm to solve numerical optimization and real-world applications. Artificial Intelligence Review, 55(6), 4605-4716.
Cohen, Y., & Ezey Dar-El, M. (1998). Optimizing the number of stations in assembly lines under learning for limited production. Production Planning & Control, 9(3), 230-240.
Corominas, A., Pastor, R., & Plans, J. (2008). Balancing assembly line with skilled and unskilled workers. Omega, 36(6), 1126-1132.
De Jong, J. R. (1957). The Effects of Increasing Skill on Cycle Time and Its Consequences for Time Standards. Ergonomics, 1(1), 51-60.
El-Dabah, M., Ebrahim, M. A., El-Sehiemy, R. A., Alaas, Z., & Ramadan, M. M. (2022). A Modified Whale Optimizer for Single- and Multi-Objective OPF Frameworks [Article]. Energies (19961073), 15(7), 2378-N.PAG.
Emmons, H., & Fuh, D.-S. (1997). Sizing and scheduling a full-time and part-time workforce with off-day and off-weekend constraints. Annals of Operations Research, 70(0), 473-492.
Erdem, Y. (2011). Workforce Planning for Seasonal Demands (Publication Number 28554014) Marmara Universitesi (Turkey)]. ProQuest Dissertations and Theses Full-text Search Platform.
Fichera, S., Costa, A., & Cappadonna, F. A. (2017). Heterogeneous workers with learning ability assignment in a cellular manufacturing system. International Journal of Industrial Engineering Computations, 8(4), 427-440.
Foote, D. A., & Folta, T. B. (2002). Temporary workers as real options. Human Resource Management Review, 12(4), 579-597.
Karaoz, M., & Albeni, M. (2005). Dynamic technological learning trends in Turkish manufacturing industries. Technological Forecasting and Social Change, 72(7), 866-885.
Kim, D., Moon, D. H., & Moon, I. (2018). Balancing a mixed-model assembly line with unskilled temporary workers: algorithm and case study. ASSEMBLY AUTOMATION, 38(4), 511-523.
Lee, B., Lee, H. S., Lee, S., & Park, M. (2015). Influence Factors of Learning-Curve Effect in High-Rise Building Constructions. Journal of Construction Engineering and Management, 141(8), Article 04015019.
Lev, B., & Withers, D. (2002). Human Learning: From Learning Curves to Learning Organizations [Book Review]. Interfaces, 32(1), 95-96.
Li, Q., Sun, Q., Tao, S., & Gao, X. (2019). Multi-skill project scheduling with skill evolution and cooperation effectiveness. Engineering, Construction and Architectural Management, 27(8), 2023-2045.
Li, Y., Yang, X., & Yang, Z. (2019). Uncertain learning curve and its application in scheduling. Computers & Industrial Engineering, 131, 534-541.
Liu, C., Wang, J., & Leung, J. (2016). Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm. Computers & Industrial Engineering, 96(C), 162-179.
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.
Liu, M., Liu, Z., Chu, F., Liu, R., Zheng, F., & Chu, C. (2022). Risk-averse assembly line worker assignment and balancing problem with limited temporary workers and moving workers. International Journal of Production Research, 60(23), 7074-7092.
Liu, R., Liu, M., Chu, F., Zheng, F., & Chu, C. (2021). Eco-friendly multi-skilled worker assignment and assembly line balancing problem. Computers & Industrial Engineering, 151, 106944.
Lohmann, M., Anzanello, M. J., Fogliatto, F. S., & da Silveira, G. C. (2019a). Grouping workers with similar learning profiles in mass customization production lines. Computers & Industrial Engineering, 131(5), 542-551.
Lohmann, M., Anzanello, M. J., Fogliatto, F. S., & da Silveira, G. C. (2019b). Grouping workers with similar learning profiles in mass customization production lines. Computers & Industrial Engineering, 131, 542-551.
Mathur, K., & Süer, G. A. (2013). Math modeling and GA approach to simultaneously make overtime decisions, load cells and sequence products. Computers & Industrial Engineering, 66(3), 614-624.
Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in engineering software, 95, 51-67.
Neidigh, R. O., & Harrison, T. P. (2010). Optimising lot sizing and order scheduling with non-linear production rates. International Journal of Production Research, 48(7-8), 2279-2295.
Paul, C., Kumar Roy, P., & Mukherjee, V. (2023). Study of wind‐solar based combined heat and power economic dispatch problem using quasi‐oppositional‐based whale optimization technique. Optimal Control Applications and Methods, 44(2), 480-507.
Pilati, F., Faccio, M., & Cohen, Y. (2021). Absenteeism and Turnover as Motivation Factors for Segmenting Assembly Lines. IFAC-PapersOnLine, 54(1), 613-616.
Pinker, E. J., & Larson, R. C. (2003). Optimizing the use of contingent labor when demand is uncertain. European Journal of Operational Research, 144(1), 39-55.
Prasad, P., Wallace, L., Navidi, M., & Phillips, A. W. (2022). Learning curves in minimally invasive esophagectomy: A systematic review and evaluation of benchmarking parameters. Surgery, 171(5), 1247-1256.
Ruihan, C. J. R. H. Z. Z. L. (2021). An Adaptive Evolutionary Whale Optimization Algorithm. College of Information Science & Technology, Beijing University of Chemical Technology;Systems Engineering Research Institute.
Schott, J. R. (1995). Fault tolerant design using single and multicriteria genetic algorithm optimization Massachusetts Institute of Technology.
Shi, S., Xiong, H., & Li, G. (2023). A no-tardiness job shop scheduling problem with overtime consideration and the solution approaches. Computers & Industrial Engineering, 178, 109115.
Singh, S., & Singh, D. (2023). A Bio-inspired VM Migration using Re-initialization and Decomposition Based-Whale Optimization. ICT Express, 9(1), 92-99.
Stratman, J. K., Roth, A. V., & Gilland, W. G. (2004). The deployment of temporary production workers in assembly operations: a case study of the hidden costs of learning and forgetting. Journal of Operations Management, 21(6), 689-707.
Sun, G., Shang, Y., & Zhang, R. (2022). An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems. Electronics, 11(1475), 1475.
Tai, H.-W., Chen, J.-H., Cheng, J.-Y., Hsu, S.-C., & Wei, H.-H. (2021). Learn Curve for Precast Component Productivity in Construction. International Journal of Civil Engineering, 19(10), 1179-1194.
Tang, B., Liang, Y., Shi, J., & Li, T. (2022). Learning Curve of Robotic Right Hemicolectomy. Journal of Gastrointestinal Surgery, 26(10), 2215-2217.
Tian, Z., Jiang, X., Liu, W., & Li, Z. (2023). Dynamic energy-efficient scheduling of multi-variety and small batch flexible job-shop: A case study for the aerospace industry. Computers & Industrial Engineering, 178, 109111.
Valsamis, E. M., Chouari, T., O'Dowd-Booth, C., Rogers, B., & Ricketts, D. (2018). Learning curves in surgery: variables, analysis and applications. POSTGRADUATE MEDICAL JOURNAL, 94(1115), 525-530.
Van Veldhuizen, D. A., & Lamont, G. B. (2000). Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art. Evolutionary Computation 8(2), 125-147.
Wright, T. P. (1936). Factors Affecting the Cost of Airplanes. Journal of the Aeronautical Sciences (Institute of the Aeronautical Sciences), 3(4), 122-128.
Zhi, R., & Xu, Y. (2019). Optimal workforce and machine scheduling to maximize profit and worker satisfaction. Glorious Sun School of Business and Management, Donghua University, Shanghai, China Glorious Sun School of Business and Management, Donghua University, Shanghai, China.
Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. EVOLUTIONARY COMPUTATION, 8(2), 173-195.