A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems

Dispatching rules are frequently used to schedule jobs in flexible manufacturing systems (FMSs) dynamically. A drawback, however, to using dispatching rules is that their performance is dependent on the state of the system, but no single rule exists that is superior to all the others for all the possible states the system might be in. This drawback would be eliminated if the best rule for each particular situation could be used. To do this, this paper presents a scheduling approach that employs machine learning. Using this latter technique, and by analysing the earlier performance of the system, 'scheduling knowledge' is obtained whereby the right dispatching rule at each particular moment can be determined. Three different types of machine-learning algorithms will be used and compared in the paper to obtain 'scheduling knowledge': inductive learning, backpropagation neural networks, and case-based reasoning (CBR). A module that generates new control attributes allowing better identification of the manufacturing system's state at any particular moment in time is also designed in order to improve the 'scheduling knowledge' that is obtained. Simulation results indicate that the proposed approach produces significant performance improvements over existing dispatching rules.

[1]  John M. Wilson.  An alternative formulation of the operation-allocation problem in flexible manufacturing systems , 1989 .

[2]  Kripa Shanker,et al.  A loading and dispatching problem in a random flexible manufacturing system , 1985 .

[3]  Richard H. Choi,et al.  Evaluation of FMS parameters on overall system performance , 1988 .

[4]  Yuehwern Yih,et al.  A study on decision rules of a scheduling model in an FMS , 1993 .

[5]  Philip J. Stone,et al.  Experiments in induction , 1966 .

[6]  J. R. Quinlan.  Discovering rules by induction from large collections of examples Intro-ductory readings in expert s , 1979 .

[7]  Kenneth R. Baker,et al.  Sequencing Rules and Due-Date Assignments in a Job Shop , 1984 .

[8]  Eric M. Malstrom,et al.  Evaluation of traditional work scheduling rules in a flexible manufacturing system with a physical simulator , 1988 .

[9]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[10]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[11]  Min-Hong Han,et al.  Real-time tool control and job dispatching in flexible manufacturing systems , 1989 .

[12]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[13]  Stanley B. Gershwin,et al.  Flow optimization in flexible manufacturing systems , 1985 .

[14]  Larry A. Rendell,et al.  A New Basis for State-Space Learning Systems and a Successful Implementation , 1983, Artif. Intell..

[15]  Kathryn E. Stecke,et al.  Loading and control policies for a flexible manufacturing system , 1981 .

[16]  Yuehwern Yih,et al.  Integration of inductive learning and neural networks for multi-objective FMS scheduling , 1998 .

[17]  Pius J. Egbelu,et al.  Characterization of automatic guided vehicle dispatching rules , 1984 .

[18]  Zbigniew Michalewicz,et al.  Genetic Algorithms Plus Data Structures Equals Evolution Programs , 1994 .

[19]  R. S. Lashkari,et al.  A new formulation of operation allocation problem in flexible manufacturing systems: mathematical modelling and computational experience , 1987 .

[20]  Richard A. Wysk,et al.  An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing , 1989 .

[21]  David R. Denzler,et al.  Experimental investigation of flexible manufacturing system scheduling decision rules , 1987 .

[22]  Arno Sprecher,et al.  An Artificial Intelligence Approach , 1994 .

[23]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[24]  Yeong-Dae Kim,et al.  A real-time scheduling mechanism for a flexible manufacturing system: Using simulation and dispatching rules , 1998 .

[25]  Alberto Gómez,et al.  A review of machine learning in dynamic scheduling of flexible manufacturing systems , 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[26]  Jerome H. Friedman,et al.  A Recursive Partitioning Decision Rule for Nonparametric Classification , 1977, IEEE Transactions on Computers.

[27]  Ivan Bratko,et al.  ASSISTANT 86: A Knowledge-Elicitation Tool for Sophisticated Users , 1987, EWSL.

[28]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[29]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[30]  Jim Hutchison,et al.  Scheduling approaches for random job shop flexible manufacturing systems , 1991 .

[31]  Yuehwern Yih,et al.  A competitive neural network approach to multi-objective FMS scheduling , 1998 .

[32]  Zbigniew Michalewicz,et al.  Genetic algorithms + data structures = evolution programs (3rd ed.) , 1996 .

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

[34]  Robert M. O'Keefe,et al.  Interaction between dispatching and next station selection rules in a dedicated flexible manufacturing system , 1992 .

[35]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[36]  J. Ross Quinlan,et al.  Learning Efficient Classification Procedures and Their Application to Chess End Games , 1983 .

[37]  Charles Leave.  Neural Networks: Algorithms, Applications and Programming Techniques , 1992 .

[38]  Michael J. Shaw,et al.  Intelligent Scheduling with Machine Learning Capabilities: The Induction of Scheduling Knowledge§ , 1992 .

[39]  Yuehwern Yih,et al.  Indentifying attributes for knowledge-based development in dynamic scheduling environments , 1996 .

[40]  Yeong-Dae Kim.  A comparison of dispatching rules for job shops with multiple identical jobs and alternative routeings , 1990 .

[41]  Kathryn E. Stecke,et al.  Formulation and Solution of Nonlinear Integer Production Planning Problems for Flexible Manufacturing Systems , 1983 .

[42]  Donald Michie,et al.  Expert systems in the micro-electronic age , 1979 .

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

[44]  David W. Aha,et al.  Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms , 1992, Int. J. Man Mach. Stud..

[45]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[46]  Shinichi Nakasuka,et al.  Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool , 1992 .

[47]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[48]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[49]  Bernard W. Taylor,et al.  A comparative analysis of the COVERT job sequencing rule using various shop performance measures , 1987 .

[50]  Hyunbo Cho,et al.  A robust adaptive scheduler for an intelligent workstation controller , 1993 .

[51]  Siddhartha Bhattacharyya,et al.  A review of machine learning in scheduling , 1994 .

[52]  L. N. Van Wassenhove,et al.  Analysis of Scheduling Rules for an FMS , 1990 .

[53]  Ari P. J. Vepsalainen.  Priority rules for job shops with weighted tardiness costs , 1987 .

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

[55]  J. P. Kelly,et al.  New advances for wedding optimization and simulation , 1999, WSC'99. 1999 Winter Simulation Conference Proceedings. 'Simulation - A Bridge to the Future' (Cat. No.99CH37038).

[56]  Minheekim,et al.  Simulation-based real-time scheduling in a flexible manufacturing system , 1994 .

[57]  B.F. Flanagan,et al.  THE ROLE OF SIMULATION IN THE DEVELOPMENT OF INTELLIGENCE FOR FLEXIBLE MANUFACTURING SYSTEMS , 2003 .

[58]  Joseph J. Talavage,et al.  A transient-based real-time scheduling algorithm in FMS , 1991 .