How to cite this paper
Rossit, D., Toncovich, A., Rossit, D & Nesmachnow, S. (2021). Solving a flow shop scheduling problem with missing operations in an Industry 4.0 production environment.Journal of Project Management, 6(1), 33-44.
Refrences
Almada-Lobo, F. (2016). The industry 4.0 revolution and the future of manufacturing execution systems (MES). Journal of Innovation Management, 3(4), 16–21.
Altiparmak, F., Gen, M., Lin, L., & Karaoglan, I. (2009). A steady-state genetic algorithm for multi-product supply chain net-work design. Computers & Industrial Engineering, 56(2), 521-537.
Benavides, A. J., & Ritt, M. (2016). Two simple and effective heuristics for minimizing the makespan in non-permutation flow shops. Computers & Operations Research, 66, 160-169.
Benavides, A. J., & Ritt, M. (2018). Fast heuristics for minimizing the makespan in non-permutation flow shops. Computers & Operations Research, 100, 230-243.
Dios, M., Fernandez-Viagas, V., & Framinan, J. M. (2018). Efficient heuristics for the hybrid flow shop scheduling problem with missing operations. Computers & Industrial Engineering, 115, 88-99.
Dolgui, A., Ivanov, D., Sethi, S. P., & Sokolov, B. (2019). Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications. International Journal of Production Research, 57(2), 411-432.
Durillo, J. J., Nebro, A. J., Luna, F., Dorronsoro, B., & Alba, E. (2006). jMetal: A java framework for developing multi-objective optimization metaheuristics. Departamento de Lenguajes y Ciencias de la Computación, University of Málaga, ETSI Informática, Campus de Teatinos, Tech. Rep. ITI-2006-10.
Framinan, J. M., Perez-Gonzalez, P., & Escudero, V. F. V. (2017, December). The value of real-time data in stochastic flow-shop scheduling: A simulation study for makespan. In 2017 Winter Simulation Conference (WSC) (pp. 3299-3310). IEEE.
Framinan, J. M., Fernandez-Viagas, V., & Perez-Gonzalez, P. (2019). Using real-time information to reschedule jobs in a flow-shop with variable processing times. Computers & Industrial Engineering, 129, 113-125.
Fu, Y., Ding, J., Wang, H., & Wang, J. (2018). Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system. Applied Soft Computing, 68, 847-855.
Garey, M. R., Johnson, D. S., & Sethi, R. (1976). The complexity of flowshop and jobshop scheduling. Mathematics of Opera-tions Research, 1(2), 117-129.
Glass, C. A., Gupta, J. N., & Potts, C. N. (1999). Two-machine no-wait flow shop scheduling with missing opera-tions. Mathematics of Operations Research, 24(4), 911-924.
Graham, R. L., Lawler, E. L., Lenstra, J. K., & Kan, A. R. (1979). Optimization and approximation in deterministic sequenc-ing and scheduling: a survey. In Annals of discrete mathematics (Vol. 5, pp. 287-326). Elsevier.
Henneberg, M., & Neufeld, J. S. (2016). A constructive algorithm and a simulated annealing approach for solving flowshop problems with missing operations. International Journal of Production Research, 54(12), 3534-3550.
Hermann, M., Pentek, T., & Otto, B. (2016, January). Design Principles for Industrie 4.0 Scenarios. In 2016 49th Hawaii In-ternational Conference on System Sciences (HICSS) (pp. 3928-3937). IEEE.
Ivanov, D., Sethi, S., Dolgui, A., & Sokolov, B. (2018). A survey on control theory applications to operational systems, sup-ply chain management, and Industry 4.0. Annual Reviews in Control, 46, 134-147
Kellegöz, T., Toklu, B., & Wilson, J. (2010). Elite guided steady-state genetic algorithm for minimizing total tardiness in flow-shops. Computers & Industrial Engineering, 58(2), 300-306.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.
Lee, E. A. (2008, May). Cyber physical systems: Design challenges. In Object oriented real-time distributed computing (isorc), 2008 11th ieee international symposium on (pp. 363-369). IEEE
Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing sys-tems. Manufacturing Letters, 3, 18-23.
Liu, Y., Wang, L., Wang, X. V., Xu, X., & Zhang, L. (2018). Scheduling in cloud manufacturing: state-of-the-art and research challenges. International Journal of Production Research, 1-26.
Low, C. (2005). Simulated annealing heuristic for flow shop scheduling problems with unrelated parallel machines. Computers & Operations Research, 32(8), 2013-2025.
Lu, Y., Peng, T., & Xu, X. (2019). Energy-efficient cyber-physical production network: Architecture and technolo-gies. Computers & Industrial Engineering, 129, 56-66.
Lu, Y., & Xu, X. (2019). Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services. Robotics and Computer-Integrated Manufacturing, 57, 92-102.
Luo, H., Fang, J., & Huang, G. Q. (2015). Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environ-ment. Computers & Industrial Engineering, 84, 12-23.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087-1092.
Monostori, L. (2014). Cyber-physical production systems: Roots, expectations and R&D challenges. Procedia CIRP, 17, 9-13.
Nesmachnow, S. (2014). An overview of metaheuristics: accurate and efficient methods for optimisation. International Journal of Metaheuristics, 3(4), 320-347.
Osman, I. H., & Potts, C. N. (1989). Simulated annealing for permutation flow-shop scheduling. Omega, 17(6), 551-557.
Ponnambalam, S. G., & Reddy, M. (2003). A GA-SA multiobjective hybrid search algorithm for integrating lot sizing and se-quencing in flow-line scheduling. The International Journal of Advanced Manufacturing Technology, 21(2), 126-137.
Pugazhendhi, S., Thiagarajan, S., Rajendran, C., & Anantharaman, N. (2003). Performance enhancement by using non-permutation schedules in flowline-based manufacturing systems. Computers & Industrial Engineering, 44(1), 133-157.
Pugazhendhi, S., Thiagarajan, S., Rajendran, C., & Anantharaman, N. (2004 a). Relative performance evaluation of permuta-tion and non-permutation schedules in flowline-based manufacturing systems with flowtime objective. The International Journal of Advanced Manufacturing Technology, 23(11-12), 820-830.
Pugazhendhi, S., Thiagarajan, S., Rajendran, C., & Anantharaman, N. (2004 b). Generating non-permutation schedules in flowline-based manufacturing sytems with sequence-dependent setup times of jobs: a heuristic approach. The International Journal of Advanced Manufacturing Technology, 23(1-2), 64-78.
Rajendran, C. (1994). A heuristic for scheduling in flowshop and flowline-based manufacturing cell with multi-criteria. The In-ternational Journal of Production Research, 32(11), 2541-2558.
Rajendran, C., & Ziegler, H. (2001). A performance analysis of dispatching rules and a heuristic in static flowshops with miss-ing operations of jobs. European Journal of Operational Research, 131(3), 622-634.
Ribas, I., Leisten, R., & Framiñan, J. M. (2010). Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective. Computers & Operations Research, 37(8), 1439-1454.
Rossit, D. & Tohmé, F. (2018). Scheduling research contributions to Smart manufacturing. Manufacturing Letters, 15 (B), 111-114.
Rossit, D. A., Tohmé, F., & Frutos, M. (2018). The non-permutation flow-shop scheduling problem: a literature re-view. Omega, 77, 143-153.
Rossit, D. A., Tohmé, F., & Frutos, M. (2019a). Industry 4.0: Smart Scheduling. International Journal of Production Research, 57(12), 3802-3813.
Rossit, D. A., Vásquez, Ó. C., Tohmé, F., Frutos, M., & Safe, M. D. (2019b). A combinatorial analysis of the permutation and non-permutation flow shop scheduling problems. European Journal of Operational Research. doi.org/10.1016/j.ejor.2019.07.055
Rossit, D. A., Tohmé, F., & Frutos, M. (2019c). Production planning and scheduling in Cyber-Physical Production Systems: a review. International Journal of Computer Integrated Manufacturing, 32(4-5), 385-395.
Rossit, D. A., Tohmé, F., & Frutos, M. (2019d). An Industry 4.0 approach to assembly line resequencing. The International Journal of Advanced Manufacturing Technology, 105(9), 3619-3630.
Rossit, D., Tohmé, F., Frutos, M., & Safe, M. (2020). Critical paths of non-permutation and permutation flow shop schedul-ing problems. International Journal of Industrial Engineering Computations, 11(2), 281-298.
Ruiz, R., & Stützle, T. (2008). An Iterated Greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. European Journal of Operational Research, 187(3), 1143-1159.
Shim, S. O., Park, K., & Choi, S. (2017). Innovative production scheduling with customer satisfaction based measurement for the sustainability of manufacturing firms. Sustainability, 9(12), 2249.
Simpson, T. W., Siddique, Z., & Jiao, R. J. (Eds.). (2006). Product platform and product family design: methods and applica-tions. Springer Science & Business Media.
Toncovich, A., Rossit, D. A., Frutos, M. & Rossit, D. G. (2019). Solving a multi-objective manufacturing cell scheduling problem with the consideration of warehouses using a simulated annealing based procedure. International Journal of Indus-trial Engineering Computations, 10(1), 1-16.
Tseng, C. T., Liao, C. J., & Liao, T. X. (2008). A note on two-stage hybrid flowshop scheduling with missing opera-tions. Computers & Industrial Engineering, 54(3), 695-704.
Uhlmann, I. R., & Frazzon, E. M. (2018). Production rescheduling review: Opportunities for industrial integration and practi-cal applications. Journal of Manufacturing Systems, 49, 186-193.
Vahedi Nouri, B., Fattahi, P., & Ramezanian, R. (2013). Hybrid firefly-simulated annealing algorithm for the flow shop problem with learning effects and flexible maintenance activities. International Journal of Production Research, 51(12), 3501-3515.
Venkataramanaiah, S. (2008). Scheduling in cellular manufacturing systems: an heuristic approach. International Journal of Production Research, 46(2), 429-449.
Vollmann, Thomas E., Berry, William L., Whybark, D. C. & Jacobs R. (2005). Manufacturing Planning and Control for Sup-ply Chain Management. McGraw-Hill/Irwin. 5th Edition
Wang, M., Zhong, R. Y., Dai, Q., & Huang, G. Q. (2016). A MPN-based scheduling model for IoT-enabled hybrid flow shop manufacturing. Advanced Engineering Informatics, 30(4), 728-736.
Wang, Y., Zheng, P., Xu, X., Yang, H., & Zou, J. (2019). Production planning for cloud-based additive manufacturing—A computer vision-based approach. Robotics and Computer-Integrated Manufacturing, 58, 145-157.
Yao, X., & Lin, Y. (2016). Emerging manufacturing paradigm shifts for the incoming industrial revolution. The International Journal of Advanced Manufacturing Technology, 85(5-8), 1665-1676.
Yu, C., Mou, S., Ji, Y., Xu, X., & Gu, X. (2018). A delayed product differentiation model for cloud manufacturing. Computers & Industrial Engineering, 117, 60-70.
Zheng, P., Yu, S., Wang, Y., Zhong, R. Y., & Xu, X. (2017). User-experience based product development for mass personali-zation: A case study. Procedia CIRP, 63, 2-7.
Zheng, P., Lin, Y., Chen, C. H., & Xu, X. (2019). Smart, connected open architecture product: an IT-driven co-creation para-digm with lifecycle personalization concerns. International Journal of Production Research, 57(8), 2571-2584.
Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: a re-view. Engineering, 3(5), 616-630.
Altiparmak, F., Gen, M., Lin, L., & Karaoglan, I. (2009). A steady-state genetic algorithm for multi-product supply chain net-work design. Computers & Industrial Engineering, 56(2), 521-537.
Benavides, A. J., & Ritt, M. (2016). Two simple and effective heuristics for minimizing the makespan in non-permutation flow shops. Computers & Operations Research, 66, 160-169.
Benavides, A. J., & Ritt, M. (2018). Fast heuristics for minimizing the makespan in non-permutation flow shops. Computers & Operations Research, 100, 230-243.
Dios, M., Fernandez-Viagas, V., & Framinan, J. M. (2018). Efficient heuristics for the hybrid flow shop scheduling problem with missing operations. Computers & Industrial Engineering, 115, 88-99.
Dolgui, A., Ivanov, D., Sethi, S. P., & Sokolov, B. (2019). Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications. International Journal of Production Research, 57(2), 411-432.
Durillo, J. J., Nebro, A. J., Luna, F., Dorronsoro, B., & Alba, E. (2006). jMetal: A java framework for developing multi-objective optimization metaheuristics. Departamento de Lenguajes y Ciencias de la Computación, University of Málaga, ETSI Informática, Campus de Teatinos, Tech. Rep. ITI-2006-10.
Framinan, J. M., Perez-Gonzalez, P., & Escudero, V. F. V. (2017, December). The value of real-time data in stochastic flow-shop scheduling: A simulation study for makespan. In 2017 Winter Simulation Conference (WSC) (pp. 3299-3310). IEEE.
Framinan, J. M., Fernandez-Viagas, V., & Perez-Gonzalez, P. (2019). Using real-time information to reschedule jobs in a flow-shop with variable processing times. Computers & Industrial Engineering, 129, 113-125.
Fu, Y., Ding, J., Wang, H., & Wang, J. (2018). Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system. Applied Soft Computing, 68, 847-855.
Garey, M. R., Johnson, D. S., & Sethi, R. (1976). The complexity of flowshop and jobshop scheduling. Mathematics of Opera-tions Research, 1(2), 117-129.
Glass, C. A., Gupta, J. N., & Potts, C. N. (1999). Two-machine no-wait flow shop scheduling with missing opera-tions. Mathematics of Operations Research, 24(4), 911-924.
Graham, R. L., Lawler, E. L., Lenstra, J. K., & Kan, A. R. (1979). Optimization and approximation in deterministic sequenc-ing and scheduling: a survey. In Annals of discrete mathematics (Vol. 5, pp. 287-326). Elsevier.
Henneberg, M., & Neufeld, J. S. (2016). A constructive algorithm and a simulated annealing approach for solving flowshop problems with missing operations. International Journal of Production Research, 54(12), 3534-3550.
Hermann, M., Pentek, T., & Otto, B. (2016, January). Design Principles for Industrie 4.0 Scenarios. In 2016 49th Hawaii In-ternational Conference on System Sciences (HICSS) (pp. 3928-3937). IEEE.
Ivanov, D., Sethi, S., Dolgui, A., & Sokolov, B. (2018). A survey on control theory applications to operational systems, sup-ply chain management, and Industry 4.0. Annual Reviews in Control, 46, 134-147
Kellegöz, T., Toklu, B., & Wilson, J. (2010). Elite guided steady-state genetic algorithm for minimizing total tardiness in flow-shops. Computers & Industrial Engineering, 58(2), 300-306.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.
Lee, E. A. (2008, May). Cyber physical systems: Design challenges. In Object oriented real-time distributed computing (isorc), 2008 11th ieee international symposium on (pp. 363-369). IEEE
Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing sys-tems. Manufacturing Letters, 3, 18-23.
Liu, Y., Wang, L., Wang, X. V., Xu, X., & Zhang, L. (2018). Scheduling in cloud manufacturing: state-of-the-art and research challenges. International Journal of Production Research, 1-26.
Low, C. (2005). Simulated annealing heuristic for flow shop scheduling problems with unrelated parallel machines. Computers & Operations Research, 32(8), 2013-2025.
Lu, Y., Peng, T., & Xu, X. (2019). Energy-efficient cyber-physical production network: Architecture and technolo-gies. Computers & Industrial Engineering, 129, 56-66.
Lu, Y., & Xu, X. (2019). Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services. Robotics and Computer-Integrated Manufacturing, 57, 92-102.
Luo, H., Fang, J., & Huang, G. Q. (2015). Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environ-ment. Computers & Industrial Engineering, 84, 12-23.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087-1092.
Monostori, L. (2014). Cyber-physical production systems: Roots, expectations and R&D challenges. Procedia CIRP, 17, 9-13.
Nesmachnow, S. (2014). An overview of metaheuristics: accurate and efficient methods for optimisation. International Journal of Metaheuristics, 3(4), 320-347.
Osman, I. H., & Potts, C. N. (1989). Simulated annealing for permutation flow-shop scheduling. Omega, 17(6), 551-557.
Ponnambalam, S. G., & Reddy, M. (2003). A GA-SA multiobjective hybrid search algorithm for integrating lot sizing and se-quencing in flow-line scheduling. The International Journal of Advanced Manufacturing Technology, 21(2), 126-137.
Pugazhendhi, S., Thiagarajan, S., Rajendran, C., & Anantharaman, N. (2003). Performance enhancement by using non-permutation schedules in flowline-based manufacturing systems. Computers & Industrial Engineering, 44(1), 133-157.
Pugazhendhi, S., Thiagarajan, S., Rajendran, C., & Anantharaman, N. (2004 a). Relative performance evaluation of permuta-tion and non-permutation schedules in flowline-based manufacturing systems with flowtime objective. The International Journal of Advanced Manufacturing Technology, 23(11-12), 820-830.
Pugazhendhi, S., Thiagarajan, S., Rajendran, C., & Anantharaman, N. (2004 b). Generating non-permutation schedules in flowline-based manufacturing sytems with sequence-dependent setup times of jobs: a heuristic approach. The International Journal of Advanced Manufacturing Technology, 23(1-2), 64-78.
Rajendran, C. (1994). A heuristic for scheduling in flowshop and flowline-based manufacturing cell with multi-criteria. The In-ternational Journal of Production Research, 32(11), 2541-2558.
Rajendran, C., & Ziegler, H. (2001). A performance analysis of dispatching rules and a heuristic in static flowshops with miss-ing operations of jobs. European Journal of Operational Research, 131(3), 622-634.
Ribas, I., Leisten, R., & Framiñan, J. M. (2010). Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective. Computers & Operations Research, 37(8), 1439-1454.
Rossit, D. & Tohmé, F. (2018). Scheduling research contributions to Smart manufacturing. Manufacturing Letters, 15 (B), 111-114.
Rossit, D. A., Tohmé, F., & Frutos, M. (2018). The non-permutation flow-shop scheduling problem: a literature re-view. Omega, 77, 143-153.
Rossit, D. A., Tohmé, F., & Frutos, M. (2019a). Industry 4.0: Smart Scheduling. International Journal of Production Research, 57(12), 3802-3813.
Rossit, D. A., Vásquez, Ó. C., Tohmé, F., Frutos, M., & Safe, M. D. (2019b). A combinatorial analysis of the permutation and non-permutation flow shop scheduling problems. European Journal of Operational Research. doi.org/10.1016/j.ejor.2019.07.055
Rossit, D. A., Tohmé, F., & Frutos, M. (2019c). Production planning and scheduling in Cyber-Physical Production Systems: a review. International Journal of Computer Integrated Manufacturing, 32(4-5), 385-395.
Rossit, D. A., Tohmé, F., & Frutos, M. (2019d). An Industry 4.0 approach to assembly line resequencing. The International Journal of Advanced Manufacturing Technology, 105(9), 3619-3630.
Rossit, D., Tohmé, F., Frutos, M., & Safe, M. (2020). Critical paths of non-permutation and permutation flow shop schedul-ing problems. International Journal of Industrial Engineering Computations, 11(2), 281-298.
Ruiz, R., & Stützle, T. (2008). An Iterated Greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. European Journal of Operational Research, 187(3), 1143-1159.
Shim, S. O., Park, K., & Choi, S. (2017). Innovative production scheduling with customer satisfaction based measurement for the sustainability of manufacturing firms. Sustainability, 9(12), 2249.
Simpson, T. W., Siddique, Z., & Jiao, R. J. (Eds.). (2006). Product platform and product family design: methods and applica-tions. Springer Science & Business Media.
Toncovich, A., Rossit, D. A., Frutos, M. & Rossit, D. G. (2019). Solving a multi-objective manufacturing cell scheduling problem with the consideration of warehouses using a simulated annealing based procedure. International Journal of Indus-trial Engineering Computations, 10(1), 1-16.
Tseng, C. T., Liao, C. J., & Liao, T. X. (2008). A note on two-stage hybrid flowshop scheduling with missing opera-tions. Computers & Industrial Engineering, 54(3), 695-704.
Uhlmann, I. R., & Frazzon, E. M. (2018). Production rescheduling review: Opportunities for industrial integration and practi-cal applications. Journal of Manufacturing Systems, 49, 186-193.
Vahedi Nouri, B., Fattahi, P., & Ramezanian, R. (2013). Hybrid firefly-simulated annealing algorithm for the flow shop problem with learning effects and flexible maintenance activities. International Journal of Production Research, 51(12), 3501-3515.
Venkataramanaiah, S. (2008). Scheduling in cellular manufacturing systems: an heuristic approach. International Journal of Production Research, 46(2), 429-449.
Vollmann, Thomas E., Berry, William L., Whybark, D. C. & Jacobs R. (2005). Manufacturing Planning and Control for Sup-ply Chain Management. McGraw-Hill/Irwin. 5th Edition
Wang, M., Zhong, R. Y., Dai, Q., & Huang, G. Q. (2016). A MPN-based scheduling model for IoT-enabled hybrid flow shop manufacturing. Advanced Engineering Informatics, 30(4), 728-736.
Wang, Y., Zheng, P., Xu, X., Yang, H., & Zou, J. (2019). Production planning for cloud-based additive manufacturing—A computer vision-based approach. Robotics and Computer-Integrated Manufacturing, 58, 145-157.
Yao, X., & Lin, Y. (2016). Emerging manufacturing paradigm shifts for the incoming industrial revolution. The International Journal of Advanced Manufacturing Technology, 85(5-8), 1665-1676.
Yu, C., Mou, S., Ji, Y., Xu, X., & Gu, X. (2018). A delayed product differentiation model for cloud manufacturing. Computers & Industrial Engineering, 117, 60-70.
Zheng, P., Yu, S., Wang, Y., Zhong, R. Y., & Xu, X. (2017). User-experience based product development for mass personali-zation: A case study. Procedia CIRP, 63, 2-7.
Zheng, P., Lin, Y., Chen, C. H., & Xu, X. (2019). Smart, connected open architecture product: an IT-driven co-creation para-digm with lifecycle personalization concerns. International Journal of Production Research, 57(8), 2571-2584.
Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: a re-view. Engineering, 3(5), 616-630.