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Design and Implementation of Scheduling Systems: More Advanced Concepts

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Scheduling

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Abstract

This chapter focuses on a number of issues that have come up in recent years in the design, development, and implementation of scheduling systems. The first section discusses issues concerning uncertainty, robustness, and reactive decision-making. In practice, schedules often have to be changed because of random events. The more robust the original schedule is, the easier the rescheduling is. This section focuses on the generation of robust schedules as well as on the measurement of their robustness. The second section considers machine learning mechanisms. No system can consistently generate good solutions that are to the liking of the user. The decision-maker often has to tweak the schedule generated by the system in order to make it usable. A well-designed system can learn from past adjustments made by the user; the mechanism that enables the system to do this is called a learning mechanism. The third section focuses on the design of scheduling engines. An engine often contains an entire library of algorithms.

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References

  • R. Akkiraju, P. Keskinocak, S. Murthy, F. Wu (1998) “A New Decision Support System for Paper Manufacturing”, in Proceedings of the Sixth International Workshop on Project Management and Scheduling (1998), pp. 147–150, Bogazici University Printing Office, Istanbul, Turkey.

    Google Scholar 

  • R. Akkiraju, P. Keskinocak, S. Murthy, F. Wu (2001) “An Agent based Approach to Multi Machine Scheduling”, Journal of Applied Intelligence, Vol. 14, pp. 135–144.

    Article  MATH  Google Scholar 

  • H. Aytug, S. Bhattacharyya, G.J. Koehler and J.L. Snowdon (1994) “A Review of Machine Learning in Scheduling”, IEEE Transactions on Engineering Management, Vol. 41, pp. 165–171.

    Article  Google Scholar 

  • C. Bierwirth and D.C. Mattfeld (1999) “Production Scheduling and Rescheduling with Genetic Algorithms”, Evolutionary Computation, Vol. 7, pp. 1–17.

    Article  Google Scholar 

  • G. Booch (1994) Object-Oriented Analysis and Design with Applications (Second Edition), Benjamin/Cummings Scientific, Menlo Park, California.

    Google Scholar 

  • X. Chao and M. Pinedo (1992) “A Parametric Adjustment Method for Dispatching”, Technical Report, Department of Industrial Engineering and Operations Research, Columbia University, New York.

    Google Scholar 

  • A. Collinot, C. LePape and G. Pinoteau (1988) “SONIA: A Knowledge-Based Scheduling System”, Artificial Intelligence in Engineering, Vol. 2, pp. 86–94.

    Article  Google Scholar 

  • A. Elkamel and A. Mohindra (1999) “A Rolling Horizon Heuristic for Reactive Scheduling of Batch Process Operations”, Engineering Optimization, Vol. 31, pp. 763–792.

    Article  Google Scholar 

  • A. Feldman (1999) Scheduling Algorithms and Systems, PhD Thesis, Department of Industrial Engineering and Operations Research, Columbia University, New York.

    Google Scholar 

  • M.S. Fox and S.F. Smith (1984) “ISIS – A Knowledge-Based System for Factory Scheduling”, Expert Systems, Vol. 1, pp. 25–49.

    Article  Google Scholar 

  • N.G. Hall and M.E. Posner (2004) “Sensitivity Analysis for Scheduling Problems”, Journal of Scheduling, Vol. 7, pp. 49–83.

    Article  MathSciNet  MATH  Google Scholar 

  • V.J. Leon and S.D. Wu (1994) “Robustness Measures and Robust Scheduling for Job Shops”, IIE Transactions, Vol. 26, pp. 32–43.

    Article  Google Scholar 

  • V.J. Leon, S.D. Wu and R. Storer (1994) “A Game Theoretic Approach for Job Shops in the Presence of Random Disruptions”, International Journal of Production Research, Vol. 32, pp. 1451–1476.

    Article  MATH  Google Scholar 

  • J. Martin (1993) Principles of Object-Oriented Analysis and Design, Prentice-Hall, Englewood Cliffs, New Jersey.

    Google Scholar 

  • S.V. Mehta and R. Uzsoy (1999) “Predictable Scheduling of a Single Machine subject to Breakdowns”, International Journal of Computer Integrated Manufacturing, Vol. 12, pp. 15–38.

    Article  Google Scholar 

  • A. Oddi and N. Policella (2007) “Improving Robustness of Spacecraft Downlink Schedules”, IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews, Vol. 37, pp. 887–896.

    Article  Google Scholar 

  • S. Park, N. Raman and M.J. Shaw (1997) “Adaptive Scheduling in Dynamic Flexible Manufacturing Systems: A Dynamic Rule Selection Approach”, IEEE Transactions on Robotics and Automation, Vol. 13, pp. 486–502.

    Article  Google Scholar 

  • E. Pesch (1994) Learning in Automated Manufacturing - A Local Search Approach, Physica-Verlag (A Springer Company), Heidelberg, Germany.

    Google Scholar 

  • M. Pinedo and B. P.-C. Yen (1997) “On the Design and Development of Object-Oriented Scheduling Systems”, Annals of Operations Research, Vol. 70, C.-Y. Lee and L. Lei (eds.), pp. 359–378.

    Google Scholar 

  • N. Policella, A. Cesta, A. Oddi and S.F. Smith (2005) “Schedule Robustness through Broader Solve and Robustify Search for Partial Order Schedules”, in AI*IA-05 Advances in Artificial Intelligence - Proceedings of the 9th Congress of the Italian Association for Artificial Intelligence, (held in Milan, Italy, September 2005), S. Bandini and S. Manzoni (eds.), Springer, pp. 160–172.

    Google Scholar 

  • N. Policella, A. Cesta, A. Oddi and S.F. Smith (2007) “From Precedence Constraint Posting to Partial Order Schedules: A CSP Approach to Robust Scheduling”, AI Communications, Vol. 20, pp. 163–180.

    MathSciNet  MATH  Google Scholar 

  • P. Priore, D. de la Fuente, A. Gomez and J. Puente (2001) “A Review of Machine Learning in Dynamic Scheduling of Flexible Manufacturing Systems”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 15, pp. 251–263.

    Article  MATH  Google Scholar 

  • I. Sabuncuoglu and M. Bayiz (2000) “Analysis of Reactive Scheduling Problems in a Job Shop Environment”, European Journal of Operational Research, Vol. 126, pp. 567–586.

    Article  MATH  Google Scholar 

  • J. Sauer (1993) “Dynamic Scheduling Knowledge for Meta-Scheduling”, in Proceedings of the Sixth International Conference on Industrial Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE), Edinburgh, Scotland.

    Google Scholar 

  • M.J. Shaw (1988a) “Dynamic Scheduling in Cellular Manufacturing Systems; A Framework for Networked Decision Making”, Journal of Manufacturing Systems, Vol. 7, pp. 83–94.

    Article  Google Scholar 

  • M.J. Shaw (1988b) “Knowledge-Based Scheduling in Flexible Manufacturing Systems: An Integration of Pattern-Directed Inference and Heuristic Search”, International Journal of Production Research, Vol. 6, pp. 821–844.

    Article  Google Scholar 

  • M.J. Shaw, S. Park and N. Raman (1992) “Intelligent Scheduling with Machine Learning Capabilities: The Induction of Scheduling Knowledge”, IIE Transactions on Design and Manufacturing, Vol. 24, pp. 156–168.

    Google Scholar 

  • M.J. Shaw and A.B. Whinston (1989) “An Artificial Intelligence Approach to the Scheduling of Flexible Manufacturing Systems”, IIE Transactions, Vol. 21, pp. 170–183.

    Article  Google Scholar 

  • S.F. Smith (1992) “Knowledge-based Production Management: Approaches, Results and Prospects”, Production Planning and Control, Vol. 3, pp. 350–380.

    Article  Google Scholar 

  • S.F. Smith and O. Lassila (1994) “Configurable Systems for Reactive Production Management”, in Knowledge-Based Reactive Scheduling (B–15), E. Szelke and R.M. Kerr (eds.), Elsevier Science, North Holland, Amsterdam.

    Google Scholar 

  • S.F. Smith, N. Muscettola, D.C. Matthys, P.S. Ow and J. Y. Potvin (1990) “OPIS: An Opportunistic Factory Scheduling System”, Proceedings of the Third International Conference on Industrial and Expert Systems, (IEA/AIE 90), Charleston, South Carolina.

    Google Scholar 

  • G.E. Vieira, J.W. Herrmann, and E. Lin (2003) “Rescheduling Manufacturing Systems: A Framework of Strategies, Policies and Methods”, Journal of Scheduling, Vol. 6, pp. 39–62.

    Article  MathSciNet  MATH  Google Scholar 

  • S. Webster (2000) “Frameworks for Adaptable Scheduling Algorithms”, Journal of Scheduling, Vol. 3, pp. 21–50.

    Article  MathSciNet  MATH  Google Scholar 

  • S.D. Wu, E.S. Byeon and R.H. Storer (1999) “A Graph-Theoretic Decomposition of Job Shop Scheduling Problems to Achieve Schedule Robustness”, Operations Research, Vol. 47, pp. 113–124.

    Article  MathSciNet  MATH  Google Scholar 

  • S.D. Wu, R.H. Storer and P.C. Chang (1991) “A Rescheduling Procedure for Manufacturing Systems under Random Disruptions,” in New Directions for Operations Research in Manufacturing, T. Gulledge and A. Jones (eds.), Springer Verlag, Berlin.

    Google Scholar 

  • P.-C. Yen (1995) On the Architecture of an Object-Oriented Scheduling System, Ph.D. thesis, Department of Industrial Engineering and Operations Research, Columbia University, New York, New York.

    Google Scholar 

  • B.P.-C. Yen (1997) “Interactive Scheduling Agents on the Internet”, in Proceedings of the Hawaii International Conference on System Science (HICSS–30), Hawaii.

    Google Scholar 

  • Y. Yih (1990) “Trace-driven Knowledge Acquisition (TDKA) for Rule-Based Real Time Scheduling Systems”, Journal of Intelligent Manufacturing, Vol. 1, pp. 217–230.

    Article  Google Scholar 

  • E. Yourdon (1994) “Object-Oriented Design: An Integrated Approach”, Prentice-Hall, Englewood Cliffs, New Jersey.

    MATH  Google Scholar 

  • W. Zhang and T.G. Dietterich (1995) “A Reinforcement Learning Approach to Job-Shop Scheduling”, in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95), C.S. Mellish (ed.), pp. 1114–1120, Conference held in Montreal, Canada, Morgan Kaufmann Publishers, San Francisco, California.

    Google Scholar 

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Authors and Affiliations

  1. IOMS Dept Rm 8-59 KMC, NYU Stern School of Business, New York, NY, USA

    Michael L. Pinedo

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  1. Michael L. Pinedo
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Pinedo, M.L. (2016). Design and Implementation of Scheduling Systems: More Advanced Concepts. In: Scheduling. Springer, Cham. https://doi.org/10.1007/978-3-319-26580-3_18

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