23, 2018, 23-26
Reihenfolgeplanung im Zeitalter von Industrie 4.0: Optimierung in der Werkstattfertigung

Bibtex

Cite as text

						@Article{Gronau+Weber,
							 
							Year= "2018", 
							Number= "1", 
							 Volume= "23", 
							Pages= "23-26", 
							Journal   = "Fabriksoftware",
							 Title= "Reihenfolgeplanung im Zeitalter von Industrie 4.0: Optimierung in der Werkstattfertigung", 
							Author= "Norbert {Gronau} und Edzard {Weber}
", 
							Doi= "https://doi.org/10.30844/3_2018-1_23-26", 
							 Abstract= "Für eine effiziente Produktion sind Optimierungsverfahren unverzichtbar. Durch die Ausrichtung der Produktion auf Konzepte wie Industrie 4.0 verändern sich aber die Rahmenbedingungen für den richtigen Einsatz dieser Verfahren. Dieser Beitrag stellt die vorhandene Vielfalt an Optimierungsverfahren vor und diskutiert ihre Eignung für Industrie 4.0.", 
							 Keywords= "Reihenfolgeplanung, Industrie 4.0, Werkstattfertigung", 
							}
					
Norbert {Gronau} und Edzard {Weber}(2018): Reihenfolgeplanung im Zeitalter von Industrie 4.0: Optimierung in der Werkstattfertigung. 231(2018), S. 23-26. Online: https://doi.org/10.30844/3_2018-1_23-26 (Abgerufen 20.12.24)

Abstract

Abstract

Für eine effiziente Produktion sind Optimierungsverfahren unverzichtbar. Durch die Ausrichtung der Produktion auf Konzepte wie Industrie 4.0 verändern sich aber die Rahmenbedingungen für den richtigen Einsatz dieser Verfahren. Dieser Beitrag stellt die vorhandene Vielfalt an Optimierungsverfahren vor und diskutiert ihre Eignung für Industrie 4.0.

Keywords

Schlüsselwörter

Reihenfolgeplanung, Industrie 4.0, Werkstattfertigung

References

Referenzen

[1] N. Gronau, H. Theuer: Determination of the optimal degree of autonomy in a cyber-physical production system.accepted paper, CIRP-CMS Stuttgart 2016
[2] N. Gronau: Der Einfluss von Cyber-Physical Systems auf die Gestaltung von Produktionssystemen. Industrie Management 3 / 2015, S. 16-20
[3] M. Medo: Kontinuierliche Planung der Fließfertigung von Varianten. Schriftenreihe des IFU, Band 15, Braunschweig 2010
[4] J. Blazewicz, K. H. Ecker, E. Pesch, G. Schmidt, J. Weglarz: Handbook on Scheduling – From Theory to Applications. Berlin Heidelberg 2007
[5] H. Kühnle: Produktionsmengen- und -Terminplanung bei mehrstufiger Linienfertigung. Berlin Heidelberg 1987
[6] H. Seelbach: Ablaufplanung. Würzburg Wien 1975
[7] T. Siegel: Optimale Maschinenbelegungsplanung: Zweckmäßigkeit der Zielkriterien und Verfahren zur Lösung des Reihenfolgeproblems. Berlin 1974
[8] M. Junge: Simulationsgestütze Entwicklung und Optimierung einer energieeffizienten Produktionssteuerung. In: J. Hesselbach (Hrsg.): Produktion und Energie, Band 1, Kassel 2007
[9] M. Teichner: Analyse der Wirksamkeit ausgewählter Verfahren der Reihenfolgeplanung für die Erreichung ökonomischer Ziele. Aachen 2010
[10] J.K. Lenstra, K. A. H. G. Rimnooy, P. Brucker, Complexity of Machine Scheduling Problems. Annals of Discrete Mathematics (1) 1977, S. 343-362
[11] L. Hall: Approximability of flow shop scheduling. Mathematical Programming (82), 1998, S. 175-190
[12] M.A. Kubzin, V.A. Strusevich: Planning Machine Maintenance in Two-Machine Shop Scheduling. Operations Research (54) 4, 2006, S. 789-800
[13] M.R. Garey, D.S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. New York 1979
[14] D. Bertsimas, D. Gamarnik, J. Sethuraman: From Fluid Relaxations to Practical Algorithms for High-Multiplicity Job-Shop Scheduling: The Holding Cost Objective. Operations Research (51) 5, 2003, S. 798-813
[15] H.-H. Doh, J.-M. Yu, J.-S. Kim, D.-H. Lee, S.-H. Nam: A priority scheduling approach for flexible job shops with multiple process plans. International Journal of Production Research (51)12, 2013, S. 3748-3764
[16] J. Caffrey, G. Hitchings: Makespan distributions in flow shop scheduling. International Journal of Operations & Production Management (15) No. 3, 1995
[17] W. R. Newman, M. J. Maffei: Managing the job shop: simulating the effects of flexibility, order release mechanisms and sequencing rules. Integrated Manufacturing Systems (10) 5, 1999, S. 266-275
[18] W. Krug: Modelling, Simulation and Optimisation for manufacturing, organisational and logistical processes. Delft 2002
[19] F. Huq, Z. Huq: The sensitivity of rule combinations for scheduling in a hybrid job shop. International Journal of Operations & Production Management (15) 3, 1995, S. 59-75
[20] J. Payman, R. Leachman: Coordinated Multistage Scheduling of Parallel Batch-Processing Machines Under Multiresource Constraints. Operations Research (58) 4-1, 2010, S. 933-947
[21] S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi: Optimization by Simulated Annealing. Science, New Series (220), 1983, S. 671-680
[22] K. Kurbel, T. Rohmann: Ein Vergleich von Verfahren zur Maschinenbelegungsplanung: Simulated Annealing, Genetische Algorithmen und mathematische Optimierung. Wirtschaftsinformatik (37) 6, 1995, S.581-593
[23] A.S. Manne: On the Job-Shop Scheduling Problem. Operations Research (8) 8, 1960, S. 219-223
[24] P.D. Martin: A Time-Oriented Approach to Computing Optimal Schedules for the Job-Shop Scheduling Problem, Ph. D. Thesis, School of Operations Research & Industrial Engineering, Cornell University, Ithaca 1996
[25] T. Yunes, A. Ionu, J. Hooker: An Integrated Solver for Optimization Problems. Operations Research (58) 2, 2010, S. 342-356
[26] A. Garrido, M. A. Salido, F. Barber, M. A. López: Heuristic Methods for Solving Job-Shop Scheduling Problems, Intelligent Planning & Scheduling Group, Polytechnical University of Valencia, 2000, http://users.dsic.upv.es/~agarridot/index_archivos/papers/garrido00b.ps (letzter Abruf 12.08.2016)
[27] A. Fink und F. Rothlauf: Heuristische Optimierungsverfahren in der Wirtschaftsinformatik, Arbeitsbericht: Working Papers in Information Systems 1, 10/2006, Department of Information Systems 1, Universität Mannheim, 2006
[28] D. de Farias, B. van Roy: The Linear Programming Approach to Approximate Dynamic Programming. Operations Research (51) 6, 2003, S. 850-865
[29] S. Binato, W. J. Hery, D. M. Loewenstern, M. G. C. Resende, A: GRASP for Job Shop Scheduling. In: Essays and surveys on metaheuristics, Alphen aan den Rijn, 2001, S. 59-79
[30] C.C. Ribeiro, P. Hansen, Essays and surveys in Metaheuristics. Alphen aan den Rijn 2002
[31] J.P. Watson, A. E. Howe, L.D. Whitley: An Analysis of Iterated Local Search for Job-Shop Scheduling. In: T. Ibaraki, Y Yoshitomi (Hrsg.): Fifth Metaheuristics International Conference, 2003
[32] H.R. Lourenço, O. Martin, T. Stützle, Iterated Local Search. Handbook of Metaheuristics (57), 2002, S. 321-353
[33] P.J.M. van Laarhoven, E.H.L. Aarts: Simulated annealing: theory and applications. Dordrecht 1987
[34] M. Steinrücke: Fuzzy Sets und ihre konzeptionelle Anwendung in der Produktionsplanung. Wiesbaden 1997
[35] V. Sels, N. Gheysen, M. Vanhoucke: A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions. International Journal of Production Research (50) 15, 2012, S. 4255-4270
[36] J. Heuer: Neuronale Netze in der Industrie: Einführung – Analyse – Einsatzmöglichkeiten. Wiesbaden 1997
[37] P. Priore, D. de la Fuente, R. Pino: Dynamic scheduling of flexible manufacturing systems using neural networks and inductive learning. Integrated Manufacturing Systems (14) 2, 2003, S. 160-168
[38] K. G. Anilkumar, T. Tanprasert: Neural Network Based Generalized Job-Shop Scheduler. In: Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications, Malaysia 2006
[39] K. Peters und C. Scharff: Intelligente Produktionssteuerung mit verspätetem Feedback. PPS Management (14) 1, 2009, S. 33-36
[40] Golmohammadi, D.: A neural network decision-making model for job-shop scheduling. International Journal of Production Research (51) 17, 2013, S. 5142-5157
[41] A. Aamodt, E. Plaza: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications 7 (1), 1994, S. 39-59
[42] G. Schmidt: Case-based Reasoning for Production Scheduling. International Journal of Production Economics (56-57) 1998, S. 537-546
[43] S. Gulati und S. S. Iyengar: Nonlinear Neural Networks for Deterministic Scheduling. In: IEEEICNN – Proccedings of the IEEE First International Conference on Neural Networks, IEEE Piscataway, 1987, Band 4, S. 745-752
[44] Y.-P. S. Foo und Y. Takefuji: Integer Linear Programming Neural Networks for Job-Shop Scheduling. In: Proceedings of the Second International Conference on Neural Networks. IEEE 1988, Band 2, S. 341-348
[45] P. V. Hayes, R. T. Rue und S. I. Sayegh: Fault Isolation in Units Under Test: A Neural Network Approach. In: Proceedings of the Artificial Neural Networks in Engineering (ANNIE`92) Conference, New York 1992, S. 757-762
[46] J. Wang, H. Zhao, J. Du, T. Yu, W. Wang: Neural Network Model Based Job Scheduling and Its Implementation in Networked Manufacturing. In: 4th International Conference on Natural Computation 2008
[47] B. Scholz-Reiter, T. Hamann, N. Gronau, J. Bogen: Fallbasierte neuronale Produktionsregelung: Nutzung des Case-Based Reasoning zur Produktionsregelung mit neuronalen Netzen. wt Werkstattstechnik online (95) 4, 2005, S. 293-298
[48] T. Hamann: Lernfähige intelligente Produktionsregelung. In: B. Scholz-Reiter (Hrsg.): Informationstechnische Systeme und Organisation von Produktion und Logistik, Band 7, Berlin 2008
[49] D. Whitley , N. Yoo: Modeling Simple Genetic Algorithms for Permutation Problems. Foundations of Genetic Algorithms (3) 1994, S. 163-184
[50] T. Yamada, R. Nakano: Genetic Algorithms for Job-Shop Scheduling Problems. In: Proceedings of Modern Heuristic for Decision Support 1997
[51] C. Bierwirth, D. C. Mattfeld: Production Scheduling and Rescheduling with Genetic Algorithms. Evolutionary Computation Archive (7) 1, 1999
[52] D.C. Mattfeld, C. Bierwirth: A search space analysis of the Job Shop Scheduling Problem. Annals of Operations Research (86), 1999, S. 441-453
[53] A. Fekih, O. Jellouli, A.B. Hadj-Alouane: Flexible job-shop scheduling problem by genetic algorithm and learning by partial injection of sequences. International Journal of Engineering Management and Economics (3) 1/2, 2012, S. 22-39
[54] S. Maqsood, S. Noor, M.K. Khan, A. Wood: Hybrid Genetic Algorithm (GA) for job shop scheduling problems and its sensitivity analysis. International Journal of Intelligent Systems Technologies and Applications (11) 1/2, 2012, S. 49-62
[55] A.-D.D. Ngoc, S.H. Lee, I. Moon: Hybrid genetic algorithm for test bed scheduling problems. International Journal of Production Research (52) 4, 2014, S. 1074-1089
[56] L. Barbulescu, J.-P. Watson, L. D. Whitley: Dynamic Representations and Escaping Local Optima: Improving Genetic Algorithms and Local Search. In: Proceedings of the 17th National Conference on Artificial Intelligence 2000, S. 879-884
[57] C. Bierwirth: A generalized permutation approach to job shop scheduling with genetic algorithms. OR Spectrum (17) 1995, S. 87-92
[58] Mattfeld, D.C.; Bierwirth, C.: An Efficient Genetic Algorithm for Job Shop Scheduling with Tardiness Objectives. European Journal of Operational Research 155 (2004), S. 616-630
[59] D. Montana, M. Brinn, S. Moore, G. Bidwell: Genetic Algorithms for Complex Real-Time Scheduling. In: IEEE Conference on Systems, Man. and Cybernetics, 1998. http://davidmontana.net/papers/smc98.pdf (Letzter Abruf 10.12.2014)
[60] R. Braune, S. Wagner, M. Affenzeller: Applying Genetic Algorithms to the Optimization of Production Planning in a Real-World Manufacturing Environment. In: R. Trappl (Hrsg.): Cybernetics and Systems (1) 2004, S. 41-46
[61] M. Vázquez , L. D. Whitley: A Comparison of Genetic Algorithms for the Dynamic Job Shop Scheduling Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference, 2000, S. 303-312
[62] S. Chen, S. F. Smith: Improving Genetic Algorithms by Search Space Reductions (with Applications to Flow Shop Scheduling). In: Proceedings of the Genetic and Evolutionary Computation Conference, Orlando 1999
[63] J.F. Gonçalves, J. J. de Magalhães Mendes: A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem, AT&T Labs Research Technical Report TD-5EAL6J, 2002
[64] C.F. Tsai, F.-C. Lin: A New Hybrid Heuristic Technique for Solving Job-shop Scheduling Problem. In: IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2003
[65] Khouki: Prototyped genetic search: a cybernetical approach to job-shop scheduling problems, Vol. 31 No. 1, pp. 96-114, 2002
[66] K. Belkadi, M. Gougand, M. Benyettou, A .Aribi: Sequential and Parallel Genetic Algorithms for the Hybrid Flow Shop Scheduling Problem. Journal of Applied Science (6), 2006, S. 775-778
[67] F. Manlig: Optimierung von Fertigungsprozessen mit Rechnersimulation – Ein Analysebericht. IPT, PAS-Forschungsergebnisbericht TU Dresden 2001
[68] S. Horn, G. Weigert, E. Beier: Heuristic optimization strategies for scheduling of manufacturing processes. In: Proceedings of Electronics Technology ISSE 2006, S. 422-427, DOI: 10.1109/ISSE.2006.365142
[69] S. Hong Choi, F. Yu Yan: A filter search algorithm based on machine-order space for job-shop scheduling problems. Journal of Manufacturing Technology Management (17) 3, 2006, S. 376-392
[70] G. Schmidt: Scheduling with limited machine availability. European Journal of Operational Research, 121(3), 2000, S. 1-15
[71] S. Albers, G. Schmidt: Scheduling with unexpected machine breakdowns. Discrete Applied Mathematics, (110) 2-3, 2001, S. 85-99
[72] W. Kubiak, J. Blazewicz, P. Formanowicz, J. Breit, G. Schmidt: Two-machine flow shops with limited machine availability. European Journal of Operational Research 136 (3), 2002, S. 528-540
[73] S. Wang, L. Wang, Y. Xu, M. Liu: An effective estimation of distribution algorithm for the flexible job-shop scheduling problem with fuzzy processing time. International Journal of Production Research (51) 12, 2013, S. 3778-3793
[74] X. Zhang, Y. Deng, F.T.S. Chan, P. Xu, S. Mahadevan, Y. Hu: IFSJSP: A novel methodology for the Job-Shop Scheduling Problem based on intuitionistic fuzzy sets. International Journal of Production Research (51) 17, 2013, S. 5100-5119
[75] D. Lin, C.K.M. Lee, Z. Wu: Integrating analytical hierarchy process to genetic algorithm for re-entrant flow shop scheduling problem. International Journal of Production Research (50) 7, 2012, S. 1813-1824
[76] M. Mitchell , S. Forrest , J.H. Holland: The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance. In: Proceedings of the First European Conference on Artificial Life 1991
[77] J. Czogalla, A. Fink: Fitness Landscape Analysis for the Resource Constrained Project Scheduling Problem. In: Lecture Notes in Computer Science (5851) Berlin Heidelberg 2009, S. 104-118
[78] M. J. Geiger: On the distribution of Pareto Optimal Solutions in alternative space – the investigation of multi objective permutation flow shop scheduling problems. Technological and Economic Development of Economy. (12) 1, 2006, S. 23-29
[79] A.M. Sutton: An analysis of search landscape neutrality in scheduling problems. In: Proceedings of ICAPS 2007 Doctoral Consortium, Providence
[80] M. Roberts, L. D. Whitley, A. E. Howe, L. Barbulescu: Randomwalks and Neighbourhood Bias in oversubscribed scheduling. In: Multidisciplinary International Conference on Scheduling, 2005
[81] K. Schmidt: Using Tabu Search to Solve the Job Shop Scheduling Problem with Sequence Dependent Setup Times. ScM Thesis, Brown University, 2001
[82] C. Bierwirth, D. C. Mattfeld, J.-P. Watson: Landscape Regularity and Random Walks for the Job-Shop Scheduling Problem, Lecture Notes in Computer Science (3004) 2004, S. 21-30
[83] E.-G. Talbi: Metaheuristics – From Design to Implementation. New Jersey, 2009

GITO events | library.gito