Abstract
The production scheduling has attracted a lot of researchers for many years, however most of the approaches are not targeted to deal with real manufacturing environments, and those that are, are very particular for the case study. It is crucial to consider important features related with the factories, such as products and machines characteristics and unexpected disturbances, but also information such as when the parts arrive to the factory and when should be delivered. So, the purpose of this paper is to identify some important characteristics that have been considered independently in a lot of studies and that should be considered together to develop a generic scheduling framework to be used in a real manufacturing environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Çaliş, B., Bulkan, S.: A research survey: review of AI solution strategies of job shop scheduling problem. J. Intell. Manuf. 26(5), 961–973 (2015)
Koren, Y., Shpitalni, M.: Design of reconfigurable manufacturing systems. J. Manuf. Syst. 29(4), 130–141 (2010)
ElMaraghy, H., ElMaraghy, W.: Smart adaptable assembly systems. Procedia CIRP 44, 4–13 (2016)
Koren, Y., et al.: Reconfigurable manufacturing systems. CIRP Ann. 48(2), 527–540 (1999)
Ribeiro, L., Barata, J.: Re-thinking diagnosis for future automation systems: an analysis of current diagnostic practices and their applicability in emerging IT based production paradigms. Comput. Ind. 62(7), 639–659 (2011)
Gao, Q., Shi, R., Wang, G.: Construction of intelligent manufacturing workshop based on lean management. Procedia CIRP 56, 599–603 (2016)
Lee, J., Kao, H.A., Yang, S.: Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP 16, 3–8 (2014)
Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T.: Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3(5), 616–630 (2017)
Tao, F., Cheng, Y., Zhang, L., Nee, A.Y.C.: Advanced manufacturing systems: socialization characteristics and trends. J. Intell. Manuf. 28(5), 1079–1094 (2017)
Koren, Y., Gu, X., Badurdeen, F., Jawahir, I.S.: sustainable living factories for next generation manufacturing. Procedia Manuf. 21, 26–36 (2018)
Kaplanoǧlu, V.: Multi-agent based approach for single machine scheduling with sequence-dependent setup times and machine maintenance. Appl. Soft Comput. J. 23, 165–179 (2014)
Yao, X., Zhou, J., Lin, Y., Li, Y., Yu, H., Liu, Y.: Smart manufacturing based on cyber-physical systems and beyond. J. Intell. Manuf. 1–13 (2017). https://doi.org/10.1007/s10845-017-1384-5
Karimi, S., Ardalan, Z., Naderi, B., Mohammadi, M.: Scheduling flexible job-shops with transportation times: mathematical models and a hybrid imperialist competitive algorithm. Appl. Math. Model. 41, 667–682 (2016)
Hongying, F., Qian, L., Dan, S.: A survey of recent research on optimization models and algorithms for operations management from the process view. Sci. Program. 2017, 1–19 (2017)
Chou, Y.C., Cao, H., Cheng, H.H.: A bio-inspired mobile agent-based integrated system for flexible autonomic job shop scheduling. J. Manuf. Syst. 32(4), 752–763 (2013)
Shahrabi, J., Adibi, M.A., Mahootchi, M.: A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Comput. Ind. Eng. 110, 75–82 (2017)
Zhang, Y., et al.: The ‘Internet of Things’ enabled real-time scheduling for remanufacturing of automobile engines. J. Clean. Prod. 185, 562–575 (2018)
Liu, Q., Dong, M., Chen, F.F.: Single-machine-based joint optimization of predictive maintenance planning and production scheduling. Robot. Comput. Integr. Manuf. 51(January), 238–247 (2018)
Shen, L., Dauzère-Pérès, S., Neufeld, J.S.: Solving the flexible job shop scheduling problem with sequence-dependent setup times. Eur. J. Oper. Res. 265(2), 503–516 (2018)
Lu, P.-H., Wu, M.-C., Tan, H., Peng, Y.-H., Chen, C.-F.: A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems. J. Intell. Manuf. 29(1), 19–34 (2018)
Chang, H.-C., Liu, T.-K.: Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms. J. Intell. Manuf. 28(8), 1973–1986 (2017)
Han, L., Xing, K., Chen, X., Xiong, F.: A Petri net-based particle swarm optimization approach for scheduling deadlock-prone flexible manufacturing systems. J. Intell. Manuf. 29(5), 1083–1096 (2015)
Alotaibi, A., Lohse, N., Vu, T.M.: Dynamic agent-based bi-objective robustness for tardiness and energy in a dynamic flexible job shop. Procedia CIRP 57, 728–733 (2016)
Petrović, M., Vuković, N., Mitić, M., Miljković, Z.: Integration of process planning and scheduling using chaotic particle swarm optimization algorithm. Expert Syst. Appl. 64, 569–588 (2016)
Jia, H.Z., Fuh, J.Y.H., Nee, A.Y.C., Zhang, Y.F.: Integration of genetic algorithm and Gantt chart for job shop scheduling in distributed manufacturing systems. Comput. Ind. Eng. 53(2), 313–320 (2007)
Yazdani, M., Aleti, A., Khalili, S.M., Jolai, F.: Optimizing the sum of maximum earliness and tardiness of the job shop scheduling problem. Comput. Ind. Eng. 107, 12–24 (2017)
Bürgy, R., Bülbül, K.: The job shop scheduling problem with convex costs. Eur. J. Oper. Res. 268(1), 82–100 (2018)
Kuhpfahl, J., Bierwirth, C.: A study on local search neighborhoods for the job shop scheduling problem with total weighted tardiness objective. Comput. Oper. Res. 66, 44–57 (2016)
Do Chung, B., Kim, B.S.: A hybrid genetic algorithm with two-stage dispatching heuristic for a machine scheduling problem with step-deteriorating jobs and rate-modifying activities. Comput. Ind. Eng. 98, 113–124 (2016)
Helo, P., Phuong, D., Hao, Y.: Cloud manufacturing – scheduling as a service for sheet metal manufacturing. Comput. Oper. Res. 0, 1–12 (2018)
Zarook, Y., Abedi, M.: JIT-scheduling in unrelated parallel-machine environment with aging effect and multi-maintenance activities. Int. J. Serv. Oper. Manag. 18(1), 99 (2014)
Zhang, L., Gao, L., Li, X.: A hybrid genetic algorithm and tabu search for a multi-objective dynamic job shop scheduling problem. Int. J. Prod. Res. 51(12), 3516–3531 (2013)
Nikolakis, N., Kousi, N., Michalos, G., Makris, S.: Dynamic scheduling of shared human-robot manufacturing operations. Procedia CIRP 72, 9–14 (2018)
Xiong, H., Fan, H., Jiang, G., Li, G.: A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints. Eur. J. Oper. Res. 257(1), 13–24 (2017)
Chan, F.T.S., Choy, K.L., Bibhushan, : A genetic algorithm-based scheduler for multiproduct parallel machine sheet metal job shop. Expert Syst. Appl. 38(7), 8703–8715 (2011)
Azami, A., Demirli, K., Bhuiyan, N.: Scheduling in aerospace composite manufacturing systems: a two-stage hybrid flow shop problem. Int. J. Adv. Manuf. Technol. 95(9–12), 3259–3274 (2018)
Gao, K., Yang, F., Zhou, M., Pan, Q.: Flexible job-shop rescheduling for new job insertion by using discrete Jaya algorithm. IEEE Trans. Cybern. 49(5), 1944–1955 (2019)
Lei, H., Xing, K., Han, L., Gao, Z.: Hybrid heuristic search approach for deadlock-free scheduling of flexible manufacturing systems using Petri nets. Appl. Soft Comput. 18(2), 240–245 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 IFIP International Federation for Information Processing
About this paper
Cite this paper
Alemão, D., Rocha, A.D., Barata, J. (2019). Production Scheduling Requirements to Smart Manufacturing. In: Camarinha-Matos, L., Almeida, R., Oliveira, J. (eds) Technological Innovation for Industry and Service Systems. DoCEIS 2019. IFIP Advances in Information and Communication Technology, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-030-17771-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-17771-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17770-6
Online ISBN: 978-3-030-17771-3
eBook Packages: Computer ScienceComputer Science (R0)