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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Production Scheduling Requirements to Smart Manufacturing

verfasst von : Duarte Alemão, André Dionísio Rocha, José Barata

Erschienen in: Technological Innovation for Industry and Service Systems

Verlag: Springer International Publishing

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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.

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Literatur
1.
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)CrossRef
2.
Koren, Y., Shpitalni, M.: Design of reconfigurable manufacturing systems. J. Manuf. Syst. 29(4), 130–141 (2010)CrossRef
3.
ElMaraghy, H., ElMaraghy, W.: Smart adaptable assembly systems. Procedia CIRP 44, 4–13 (2016)CrossRef
4.
Koren, Y., et al.: Reconfigurable manufacturing systems. CIRP Ann. 48(2), 527–540 (1999)CrossRef
5.
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)CrossRef
6.
Gao, Q., Shi, R., Wang, G.: Construction of intelligent manufacturing workshop based on lean management. Procedia CIRP 56, 599–603 (2016)CrossRef
7.
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)CrossRef
8.
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)CrossRef
9.
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)CrossRef
10.
Koren, Y., Gu, X., Badurdeen, F., Jawahir, I.S.: sustainable living factories for next generation manufacturing. Procedia Manuf. 21, 26–36 (2018)CrossRef
11.
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)CrossRef
12.
13.
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)MathSciNetCrossRef
14.
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)
15.
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)CrossRef
16.
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)CrossRef
17.
Zhang, Y., et al.: The ‘Internet of Things’ enabled real-time scheduling for remanufacturing of automobile engines. J. Clean. Prod. 185, 562–575 (2018)CrossRef
18.
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)CrossRef
19.
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)MathSciNetCrossRef
20.
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)CrossRef
21.
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)CrossRef
22.
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)CrossRef
23.
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)CrossRef
24.
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)CrossRef
25.
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)CrossRef
26.
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)CrossRef
27.
Bürgy, R., Bülbül, K.: The job shop scheduling problem with convex costs. Eur. J. Oper. Res. 268(1), 82–100 (2018)MathSciNetCrossRef
28.
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)MathSciNetCrossRef
29.
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)CrossRef
30.
Helo, P., Phuong, D., Hao, Y.: Cloud manufacturing – scheduling as a service for sheet metal manufacturing. Comput. Oper. Res. 0, 1–12 (2018)
31.
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)
32.
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)CrossRef
33.
Nikolakis, N., Kousi, N., Michalos, G., Makris, S.: Dynamic scheduling of shared human-robot manufacturing operations. Procedia CIRP 72, 9–14 (2018)CrossRef
34.
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)MathSciNetCrossRef
35.
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)CrossRef
36.
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)CrossRef
37.
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)CrossRef
38.
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)
Metadaten
Titel
Production Scheduling Requirements to Smart Manufacturing
verfasst von
Duarte Alemão
André Dionísio Rocha
José Barata
Copyright-Jahr
2019
Buch
Technological Innovation for Industry and Service Systems

Print ISBN: 978-3-030-17770-6

Electronic ISBN: 978-3-030-17771-3

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-17771-3_19

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