Comparative Simulation Study of Production Scheduling in the Hybrid and the Parallel Flow

Open access

Abstract

Scheduling is one of the most important decisions in production control. An approach is proposed for supporting users to solve scheduling problems, by choosing the combination of physical manufacturing system configuration and the material handling system settings. The approach considers two alternative manufacturing scheduling configurations in a two stage product oriented manufacturing system, exploring the hybrid flow shop (HFS) and the parallel flow shop (PFS) environments. For illustrating the application of the proposed approach an industrial case from the automotive components industry is studied. The main aim of this research to compare results of study of production scheduling in the hybrid and the parallel flow, taking into account the makespan minimization criterion. Thus the HFS and the PFS performance is compared and analyzed, mainly in terms of the makespan, as the transportation times vary. The study shows that the performance HFS is clearly better when the work stations’ processing times are unbalanced, either in nature or as a consequence of the addition of transport times just to one of the work station processing time but loses advantage, becoming worse than the performance of the PFS configuration when the work stations’ processing times are balanced, either in nature or as a consequence of the addition of transport times added on the work stations’ processing times. This means that physical layout configurations along with the way transport time are including the work stations’ processing times should be carefully taken into consideration due to its influence on the performance reached by both HFS and PFS configurations.

[1] Varela M.L.R., Carmo-Silva S., An ontology for a model of manufacturing scheduling problems to be solved on the web, Innovation in Manufacturing Networks, Springer, pp. 197–204, 2008.

[2] Carmo-Silva S., Alves A.C., Detailed design of product oriented manufacturing systems, Proceedings of the International Conference on Group Technology/Cellular Manufacturing, 3, Groningen, 2006, Springer.

[3] Costa J., Varela M.L.R., Decision System for Supporting the Implementation of a Manufacturing Section on an Automotive Factory in Portugal, Online Proceedings on Trends in Innovative Computing (ICT 2012), 2012, ISSN: 2150-7996.

[4] Johnson S.M., Optimal two and three stage production schedules with setup times included, Naval research logistics quarterly, 1, 61–68, 1954.

[5] Garey M.R., Graham R.L., Johnson D.S., Some NP-complete geometric problems, Proceedings of the Eighth Annual ACM Symposium on Theory of Computing, pp. 10–22, 1976.

[6] Al-Salem A., A heuristic to minimize makespan in proportional parallel flow shops, International Journal of Computing & Information Sciences, 2, 98, 2004.

[7] Ruiz R., Vázquez-Rodríguez J.A., The hybrid flow shop scheduling problem, European Journal of Operational Research, 205, 1–18, 8/16/2010.

[8] Aneke N., Carrie A., A design technique for the layout of multi-product flowlines, International Journal of Production Research, 24, 471–481, 1986.

[9] Salvador M.S., A solution to a special class of flow shop scheduling problems, Symposium on the Theory of Scheduling and Its Applications, pp. 83–91, 1973.

[10] Johnson D.S., Garey M.R., Computers and Intractability-A Guide to the Theory of NP-Completeness, Freeman&Co, San Francisco, 1979.

[11] Ribas I., Leisten R., Framińan J.M., Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective, 37, 1439–1454, 2010.

[12] Linn R., Zhang W., Hybrid flow shop scheduling: a survey, Computers & Industrial Engineering, 37, 57–61, 1999.

[13] Haouari M., Hidri L., Gharbi A., Optimal scheduling of a two-stage hybrid flow shop, Mathematical Methods of Operations Research, 64, 1, 107–124, 2006.

[14] Ruiz R., Maroto C., A comprehensive review and evaluation of permutation flowshop heuristics, European Journal of Operational Research, 165, 479–494, September 2005.

[15] Naderi B., Ruiz R., Zandieh M., Algorithms for a realistic variant of flowshop scheduling, Comput. Oper. Res., 37, 236–246, 2010.

[16] Nowicki E., Smutnicki C., The flow shop with parallel machines: a tabu search approach, European Journal of Operational Research, 106, 226–253, 1998.

[17] Quadt D., Kuhn H., A taxonomy of flexible flow line scheduling procedures, European Journal of Operational Research, 178, 686–698, 2007.

[18] Cheng J., Karuno Y., Kise H., A shifting bottleneck approach for a parallel-machine flowshop scheduling problem, Journal of the Operations Research Society of Japan-Keiei Kagaku, 44, 140–156, 2001.

[19] Zhang X., Van de Velde S., Approximation algorithms for the parallel flow shop problem, European Journal of Operational Research, 216, 544–552, 2/1/2012.

[20] Cao D., Chen M., Parallel flowshop scheduling using Tabu search, International Journal of Production Research, 41, 3059–3073, 2003.

[21] Vairaktarakis G., Elhafsi M., The use of flowlines to simplify routing complexity in two-stage flowshops, IIE Transactions, 32, 687–699, 2000.

[22] Berliński A., Honczarenko J., Scheduling manufacturing tasks in EMS discrete programming methods, scientific work of the Institute of Production Engineering and Automation, University of Technology, Wroclaw University of Technology, No. 41, 2003.

[23] Pinedo M., Scheduling: theory, algorithms, and systems, Springer, 2012.

[24] Andresen M., Bräsel H., Engelhardt F., Werner F., LiSA-a Library of Scheduling Algorithms: Handbook for Version 3.0: Univ., Fak. für Mathematik, 2010.

[25] Baker K.R., Baker K.R., Introduction to sequencing and scheduling, Wiley New York, vol. 15, 1974.

[26] Conway R.W., Maxwell W.L., Miller L.W., Theory of Scheduling, England: Addison-Wesley Publishing Company, Inc., 1967.

[27] Lenstra J.K., Shmoys D.B., Tardos E., Approximation Algorithms for Scheduling Unrelated Parallel Machines, Mathematical Programming, 46, 256–271, 1990.

[28] Brucker P, Scheduling Algorithms, Springer-Verlag, 1995.

[29] Blazewicz J., Ecker K.H., Pesh E., Schmidt G., Weglarz J., Scheduling Computer and Manufacturing Process, Second Edition, Springer, 2001.

[30] Pinto T., Varela M.L.R., Comparing Extended Neighborhood Search Techniques Applied to Production Scheduling, The Romanian Review Precision Mechanics, Optics & Mechatronics, 20, 37, 139–146, 2010.

[31] Varela M.L.R., Ribeiro R.A., Evaluation of Simulated Annealing to solve fuzzy optimization problems, Journal of Intelligent and Fuzzy Systems, 14, 2, 59–71, 2003.

[32] Ribeiro R., Varela M.L.R., Fuzzy optimization using simulated annealing: An Example Set, Verdegay J-L., [Ed.], Fuzzy Sets Based Heuristics for Optimization, Studies in Fuzziness and Soft Computing Series, Springer, 126, pp. 159–180, 2003.

[33] Varela M.L.R., Aparício J.N., Carmo-Silva S., A web-based application for manufacturing scheduling, Proceedings of the IASTED International Conference on Intelligent Systems and Control, pp. 400–405, 2003.

[34] Magalhães R., Varela M.L.R., Carmo-Silva S., Web-based decision support system for industrial operations management, Romanian Review Precision Mechanics, Optics and Mechatronics, 37, 159–165, 2010.

[35] Varela M.L.R., Putnik G.D., Cruz-Cunha M.M., Web-based Technologies Integration for Distributed Manufacturing Scheduling in a Virtual Enterprise, International Journal of Web Portals, 4, 2, 19–39, 2012.

[36] Vieira G.G., Varela M.L.R., Putnik G., Technologies Integration for Distributed Manufacturing Scheduling in a Virtual Enterprise, Communications in Computer and Information Science, Springer, 248, 345–355, 2012.

[37] Varela M.L.R., Barbosa R., Putnik G., Experimental Platform for Collaborative Inter and Intra Cellular Fuzzy Scheduling in an Ubiquitous Manufacturing System, Communications in Computer and Information Science, Springer, 248, 227–236, 2012.

[38] Carvalho J.B., Varela M.L.R., Putnik G.D., Hernández J.E., Ribeiro R.A., A web-based decision support system for supply chain operations management-Towards an Integd Framework, Lecture Notes in Business Information Processing (LNBIP), Springer Book of Post Proceedings on “Impact of the Web of Things in Decision Support Systems for Global Environments“, Dargam F., Hernández J.E., Zaraté P., Liu S., Ribeiro R., Delibasic B., Papatanasiou J. [Eds.], Springer, 2014.

[39] Varela M.L.R., Ribeiro R.A., Distributed Manufacturing Scheduling based on a Dynamic Multi-Criteria Decision Model, Recent Developments and New Directions in Soft Computing, Zadeh L.A., Abbasov A.M., Yager R.R., Shahbazova Sh.N., Reformat M.Z. [Eds.], Studies in Fuzziness and Soft Computing, Springer, Germany, 317, 618–623, 2014.

[40] Madureira A., Ramos C., Carmo-Silva S., Resource-oriented scheduling for real world manufacturing systems, Assembly And Task Planning, Proceedings of the IEEE International Symposium, pp. 140–145, 10–11 July 2003, doi: 10.1109/ISATP.2003.1217201.

[41] Madureira A., Ramos C., Carmo-Silva S., A Coordination Mechanism for Real World Scheduling Problems Using Genetic Algorithms, IEEE CEC2002, Honolulu, Hawai, pp. 175–180, May 2002.

[42] Madureira A., Hybrid Meta-heuristics based System for Distributed Dynamic Scheduling, Encyclopedia of Artificial Intelligence, Rabuñal J.R., Dorado J., Pazos A. [Eds.], Idea Group Reference, Information Science Reference, ISBN: 978-1-59904-849-9, 2008.

[43] Madureira A., Santos J., Proposal of multi-agent based model for dynamic scheduling in manufacturing, WSEAS Transactions on Information Science & Applications, 2, 5, 600–605, 2005.

[44] Madureira A., Sousa N., Pereira I., Self-organization for Scheduling in Agile Manufacturing, 10th IEEE International Conference on Cybernetic Intelligent Systems 2011 (IEEE CIS 2011), London, UK, 1–2 September 2011.

[45] Madureira A., Pereira I., Intelligent Bio-Inspired System for Manufacturing Scheduling under Uncertainties, International Journal of Computer Information Systems and Industrial Management Applications, ISSN 2150-7988, 3, 072–079, 2011.

[46] Madureira A., Santos J., Pereira I., Hybrid Intelligent System For Distributed Dynamic Scheduling, published by Springer-Verlag in the series Natural Intelligence for Scheduling, Planning and Packing Problems, Series: Studies in Computational Intelligence, Vol. 250, Chiong R., Dhakal S. [Eds.], ISBN: 978-3-642-04038-2, 2009.

[47] Madureira A., Pereira I., Pereira P., Abraham A., Negotiation mechanism for self-organized scheduling system with collective intelligence, Neurocomputing, Elsevier, 132, 97–110, 2014.

[48] McPherson R.F., White K.P.Jr., Periodic flow line scheduling, International Journal of production Research, 36, 1, 51–73, 1998.

[49] Muhlemann A.P., Lockett A.G., Farn C.K., Job shop scheduling heuristics and frequency of scheduling, International Journal of Production Research, 20, 2, 227–241, 1982.

[50] Sun D., Lin L., A dynamic job shop scheduling framework: A backward approach, International Journal of Production Research, 32, 4, 967–985, 1994.

[51] Buchalski Z., Heuristic algorithm for scheduling in production systems with parallel machines under resource constraints, VIth Conference on Computer Integrated Management, Zakopane 2004, Scientific-Technical Publisher, 2004.

[52] Grabowski J., Wodecki M., New elements simulated annealing algorithm for the problem of flow, VIth Conference on Computer Integrated Management, Zakopane 2004, Scientific-Technical Publisher, 2004.

[53] Tang H.P., Wong T.N., Reactive multi-agent system for assembly cell control, Robotics and Computer-Integrated Manufacturing, 21, 2, 87–98, 2005.

[54] Susz S., Chebus E., Methodology of computer-aided simulation of production orders, VIth Conference on Computer Integrated Management, Zakopane 2004, Scientific-Technical Publisher, 2004.

[55] Makuchowski M., Simulated annealing in module problem with multi machines operations that use machine no simultaneously, VIth Conference on Computer Integrated Management, Zakopane 2004, Scientific-Technical Publisher, 2004.

[56] Knosala R., Applications of artificial intelligence, Scientific-Technical Publisher, 2002.

[57] Grabowski J., Pampera J., Doublemachines problem of flow of two operations on the second machine, VIth Conference on Computer Integrated Management, Zakopane 2004, Scientific-Technical Publisher, 2004.

[58] Janiak A., Kovalyov M., Portmann Y., Single machine group scheduling with resource dependent setup and processing times, European Journal of Operational Research, 162, 112–121, 2005.

[59] Cao D., Chen M., Wan G., Parallel machine selection and job scheduling to minimize machine cost and job tardiness, Computers & Operations Research, 32, 8, 1995–2012, 2005.

[60] Huang Y.-G., Kanal L.N., Tripathi S.K., Reactive scheduling for a single machine: Problem definition, analysis, and heuristic solution, International Journal of Computer Integrated Manufacturing, 3, 1, 6–12, 1990.

[61] Jain A.K., Elmaraghy H.A., Production scheduling/rescheduling in flexible manufacturing, International Journal of Production Research, 35, 281–309, 1997.

[62] Dhingra J.S., Musser K.L., Blankenship G.L., Realtime operations scheduling for flexible manufacturing systems, Proceeding of the 1992 Winter Simulation Conference, pp. 849–855, 1992.

[63] Henning G.P., Cerda J., An expert system for predictive and reactive scheduling of multiproduct batch plants, Latin American Applied Research, 25, 187–198, 1995.

[64] Mehta S.V., Uzsoy R.M., Predictable scheduling of a job shop subject to breakdowns, IEEE Transactions on Robotics and Automation, 14, 365–378, 1998.

[65] Szelke E., Kerr R., Knowledge-based reactive scheduling, Production Planning & Control, 5, 2, 124–145, 1994.

[66] Jain A.K., Elmaraghy H.A., Production scheduling/rescheduling in flexible manufacturing, International Journal of Production Research, 35, 281–309, 1997.

[67] Tabe T., Salvendy G., Toward a hybrid intelligent system for scheduling and rescheduling of FMS, International Jounal of Computer Integrated Manufacturing, 1, 3, 154–164, 1988.

[68] Yamamoto M., Nof S.Y., Scheduling/rescheduling in the manufacturing operating system environment, International Journal of Production Research, 23, 4, 705–722, 1985.

[69] Sabuncuoglu I., Karabuk S., Rescheduling frequency in an FMS with uncertain processing times and unreliable machines, Journal of Manufacturing Systems, 18, 4, 268–283, 1999.

[70] Dhingra J.S., Musser K.L., Blankenship G.L., Realtime operations scheduling for flexible manufacturing systems, Proceeding of the 1992 Winter Simulation Conference, pp. 849–855, 1992.

[71] Vieira G.E., Herrmann J.W., Lin E., Analytical models to predict the performance of a single-machine system under periodic and event-driven rescheduling strategies, International Journal of Production Research, 38, 8, 1899–1915, 2000.

[72] Huang Y.G., Kanal L.N., Tripathi S.K., Reactive scheduling for a single machine: Problem definition, analysis and heuristic solution, International Journal of Computer Integrated Manufacturing, 3, 1, 6–12, 1990.

[73] Ovacik I.M., Uzsoy R., Rolling horizon algorithms for a single-machine dynamic scheduling with sequence-dependent setup times, International Journal of Production Research, 32, 6, 1243–1263, 1994.

[74] Vieira G.E., Herrmann J.W., Lin E., Predicting the performance of rescheduling strategies for parallel machine systems, Journal of Manufacturing Systems, 19, 4, 256–266, 2000.

[75] Ovacik I.M., Uzsoy R., Rolling horizon procedures for dynamic parallel machine scheduling with sequence-dependent setup times, International Journal of Production Research, 33, 11, 3173–3192, 1995.

[76] Gahagan S.M., Simulation and optimization of production control for lean manufacturing transition, Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, 2008.

[77] Arrais-Castro A., Varela M., Putnik G., Ribeiro R., Dargam F., Collaborative negotiation platform using a dynamic multi-criteria decision model, International Journal of Decision Support System Technology, 7, 1, 1–14, 2015, doi: 10.4018/ijdsst.2015010101.

[78] Diering M., Hamrol A., Kujawińska A., Measurement system analysis Combined with Shewhart’s Approach, Key Engineering Materials, 637, 7–11, 2015, c Trans Tech Publications, Switzerland, doi: 10.4028/www.scientific.net/KEM.637.7

[79] Kujawinska, A., Rogalewicz, M., Diering, M., Piłacińska M., Hamrol A., Kochański A., Assessment of ductile iron casting process with the use of the DRSA method, Journal of Mining and Metallurgy Section B-Metallurgy, 52, 1, 25–34, 2016, doi: 10.2298/JMMB150806023K.

[80] Kujawińska A., Rogalewicz M., Diering M., Hamrol A., Statistical Approach to Making Decisions in Manufacturing Process of Floorboard, Proc. of 5th World Conf. on Information Systems and Technologies, Recent Advances in Information Systems and Technologies, Springer, 3, 499–508, 2017, doi: 10.1007/978-3-319-56541-5_51\

[81] Vieira G., Varela M., Putnik G., Machado J., An integrated framework for supporting fuzzy decision-making in networked manufacturing environments, Romanian Review Precision Mechanics, Optics and Mechatronics, 48, 85–91, 2015.

[82] Costa N.M.L., Varela M.L.R., Carmo-Silva S., Scheduling in product oriented manufacturing systems, Proceedings of the Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC), IEEE, pp. 196–201, 2014.

Management and Production Engineering Review

The Journal of Production Engineering Committee of Polish Academy of Sciences and Polish Association for Production Management

Journal Information


CiteScore 2016: 0.48

SCImago Journal Rank (SJR) 2016: 0.126
Source Normalized Impact per Paper (SNIP) 2016: 0.551

Cited By

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 60 60 9
PDF Downloads 27 27 7