A fuzzy model for load-oriented manufacturing control

Abstract This paper presents an original approach to load-oriented manufacturing control for job-shop scheduling, based on fuzzy theory. The model allows to cope with the pitfalls encountered by traditional approaches to job-shop scheduling in the definition of system parameters. In fact, traditional approaches to job-shop scheduling assume that system parameters are deterministically known ex-ante; on the contrary, the parameters values actually observed in the job-shop are often different due to the impact of unforeseen dynamics. As a consequence, the effectiveness of traditional approaches is undermined. In this paper the authors focus on the “machine output in the planning horizon” parameter and present a model allowing to represent that parameter as a neuro-fuzzy variable, whereas traditional approach represent it as a deterministic value. The case study carried out in a real manufacturing system and reported at the end of the paper shows the effectiveness of the proposed approach.

[1]  E. Ertugrul Karsak,et al.  A fuzzy multiple objective programming approach for the selection of a flexible manufacturing system , 2002 .

[2]  Mika Johnsson,et al.  Supporting Production Planning by Production Process Simulation , 1997 .

[3]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[4]  Wolfgang Bechte.  Load-oriented manufacturing control just-in-time production for job shops , 1994 .

[5]  T.C.E. Cheng,et al.  Survey of scheduling research involving due date determination decisions , 1989 .

[6]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[7]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[8]  I. Tatsiopoulos.  A microcomputer-based interactive system for managing production and marketing in small component-manufacturing firms using a hierarchical backlog control and lead time management methodology , 1983 .

[9]  G. Gaalman,et al.  The influence of shop characteristics on workload control , 2000 .

[10]  Gerard Gaalman,et al.  The performance of workload control concepts in job shops: improving the release method , 1996 .

[11]  Guido Bologna,et al.  Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks , 1998, Hybrid Neural Systems.

[12]  Rudolf Kruse,et al.  Data mining with neuro-fuzzy models , 2001 .

[13]  Stefan Wermter,et al.  A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning , 1998, Hybrid Neural Systems.

[14]  Abraham Kandel,et al.  Data Mining and Computational Intelligence , 2001 .

[15]  Don T. Phillips,et al.  A state-of-the-art survey of dispatching rules for manufacturing job shop operations , 1982 .

[16]  Shengxiang Yang,et al.  Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling , 2000, IEEE Trans. Neural Networks Learn. Syst..

[17]  I. Grossmann,et al.  A novel branch and bound algorithm for scheduling flowshop plants with uncertain processing times , 2002 .

[18]  Detlef Nauck,et al.  Foundations Of Neuro-Fuzzy Systems , 1997 .

[19]  Gerard Gaalman,et al.  Exploring applicability of the workload control concept , 2004 .

[20]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[21]  D. Nauck,et al.  Fuzzy data analysis: challenges and perspectives , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[22]  Ranga V. Ramasesh.  Dynamic job shop scheduling: A survey of simulation research , 1990 .

[23]  Hans-Peter Wiendahl,et al.  Load-Oriented Manufacturing Control , 1994 .

[24]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[25]  Didier Dubois,et al.  Fuzzy constraints in job-shop scheduling , 1995, J. Intell. Manuf..

[26]  Nicholas R. Jennings,et al.  Prioritised fuzzy constraint satisfaction problems: axioms, instantiation and validation , 2003, Fuzzy Sets Syst..

[27]  Z. Irani,et al.  Quantification of flexibility in advanced manufacturing systems using fuzzy concept , 2004 .

[28]  László Monostori,et al.  Soft Computing and Hybrid AI Approaches to Intelligent Manufacturing , 1998, IEA/AIE.

[29]  Jwm Will Bertrand,et al.  Production control and information systems for component-manufacturing shops , 1981 .

[30]  S. S. Panwalkar,et al.  A Survey of Scheduling Rules , 1977, Oper. Res..

[31]  Wolfgang Bechte.  Theory and practice of load-oriented manufacturing control , 1988 .