A DYNAMIC APPROACH OF USING DISPATCHING RULES IN SCHEDULING
DOI:
https://doi.org/10.11113/jt.v78.8820Keywords:
Dispatching rule, genetic algorithm, predictive-reactive schedulingAbstract
Manufacturing system in reality has dynamic nature due to certain unexpected events occur in changing environment, which requires rescheduling. This does not mean that every decision is made in real time. Based on the state of the working environment, determining best rule at right time is one of the alternatives. This study focuses on selecting the dispatching rule that show best performance dynamically both in static and changing environment. Simulation is carried out by employing genetic algorithm on flow-shop and job-shop scheduling problems to compare the performance of the dispatching rules dynamically. Out of many rules proposed in the past, it has been observed that under certain conditions, the SPT (shortest processing time) performs best in both the environment, when the total processing time of a job is not high relatively. Â
References
Weirs, V. C. S.1999. A Review Of The Applicability Of OR And AI Scheduling Techniques In Practice. Omega International Journal of Management Science. 25(2): 145-153.
Jain A. S. and Meeran, S. 1999. Deterministic Job-Shop Scheduling: Past, Present And Future. European Journal of Operational Research. 113(2): 390-434.
Pinedo, M. 2002. Scheduling Theory, Algorithms And Systems. Second Edition, Prentice Hall.
Suresh, V., and Dipak Chaudhuri. 1993. Dynamic Scheduling—A Survey Of Research. International Journal of Production Economics. 32(1): 53-63.
Rhee, S. H., Bae, H., & Kim, Y. 2004. A Dispatching Rule For Efficient Workflow. Concurrent Engineering. 12(4): 305-318.
Chen, B., &Matis, T. I. 2013. A Flexible Dispatching Rule For Minimizing Tardiness In Job Shop Scheduling. International Journal of Production Economics. 141(1): 360-365.
Berd. R Jens H. 2009. Analysis and Comparison of Dispatching Rule Based Scheduling in Dual-Resource Constrained Shop-Floor Scenarios. Proceedings of the World Congress on Engineering and Computer Science. San Francisco. 20-22.
Blackstone, J. H. Phillips D., and Hogg, G. 1982. A State-Of The- Art Survey Of Dispatching Rules For Manufacturing Job Shop Operations. International Journal of Production Research. 20(1): 27-45.
Geiger, C. C, R. Uzsoy, and H. Aytu. 2006. Rapid Modeling And Discovery Of Priority Dispatching Rules: An Autonomous Learning Approach. Journal of Scheduling, 9(1): 7-34.
Lin, James T., F. K. Wang, and Y. M. Chang. 2006. A Hybrid Push/Pull-Dispatching Rule For A Photobay In A 300mm Wafer Fab. Robotics And Computer-Integrated Manufacturing. 22(1): 47-55.
Jayamohan, M. S., & Rajendran, C. 2004. Development And Analysis Of Cost-Based Dispatching Rules For Job Shop Scheduling. European Journal of Operational Research. 157(2): 307-321.
Zhang, H., Jiang, Z., & Guo, C. 2009. Simulation-Based Optimization Of Dispatching Rules For Semiconductor Wafer Fabrication System Scheduling By The Response Surface Methodology. The International Journal of Advanced Manufacturing Technology. 41(1-2): 110-121.
Sarin, S. C., Varadarajan, A., & Wang, L. 2011. A Survey Of Dispatching Rules For Operational Control In Wafer Fabrication. Production Planning and Control. 22(1): 4-24.
Qin, Xiao, and Hong Jiang. 2005. A Dynamic And Reliability-Driven Scheduling Algorithm For Parallel Real-Time Jobs Executing On Heterogeneous Clusters. Journal of Parallel and Distributed Computing. 65(8): 885-900.
Shaw, J. M. 1988. Dynamic Scheduling In Cellular Manufacturing Systems: A Framework For Network Decision Making. Journal of Manufacturing Systems. 7(2): 83-94.
Jensen, M. T.2001. Improving Robustness And Flexibility Of Tardiness And Total Flow-Time Job Shops Using Robustness Measures. Applied Soft Computing. 1(1): 35-52.
Leon, V. J., Wu, S. D., and Storer, R. H. 1994. Robustness Measures And Robust Scheduling For Job Shops. IIE Transactions. 26(5): 32-41.
Smith, F. S., Brown, D. and Scherer, W. T. 1995. Reactive Scheduling Systems, In Intelligent Scheduling Systems. Kluwer Academic Publisher.
Sun J. and Xue, D 2001. A Dynamic Reactive Scheduling Mechanism For Responding To Changes Of Production Orders & Manufacturing Resources. Computers in Industry. 46(2): 189-207.
Ouelhadj, Djamila, and Sanja Petrovic. 2009. A Survey Of Dynamic Scheduling In Manufacturing Systems. Journal of Scheduling. 12( 4): 17-431.
Rajendran, and O. Holthaus. 1999. A Comparative Study Of Dispatching Rules In Dynamic Flowshops And Jobshops. European Journal of Operational Research. 116(1): 156-170.
Cowling, P. I. and Johansson, M. 2002. Using Real-Time Information For Effective Dynamic Scheduling. European Journal of Operational Research. 139 (2): 230-244.
Laborie, Philippe. 2014. Algorithms For Propagating Resource Constraints In AI Planning And Scheduling: Existing Approaches And New Results. Sixth European Conference on Planning.
Rashid,Y., Kanji U., and Itsuo H. 1999. A Learning Based Methodology for Control of Scheduling in Changing Environment. Japan Society of Mechanical Engineering (JSME) – International Journal. 42(4): 1078-1084.
Sun, L., Cheng, X., & Liang, Y. 2010. Solving Job Shop Scheduling Problem Using Genetic Algorithm With Penalty Function. International Journal Of Intelligent Information Processing. 1(2): 65-77.
Muth, J. F. and Thompson, G. L. 1963. Industrial Scheduling. Prentice-Hall, Englewood Cliffs, New Jersey.
Billaut, J. C., and Roubellat F. 1996. A New Method for Workshop Real-time Scheduling. International Journal of Production Research. 34(6): 1555-1579.
Goldberg, D. E. 1989. Genetic Algorithms In Search, Optimization And Machine Learning. Addison-Wesley.
GodinhoFilho, M., Barco, C. F., & Neto, R. F. T. 2014. Using Genetic Algorithms To Solve Scheduling Problems On Flexible Manufacturing Systems (FMS): A Literature Survey, Classification And Analysis. Flexible Services and Manufacturing Journal. 26(3): 408-431.
Vieira, G. E., Herrmann, J. W., & Lin, E. 2003. Rescheduling Manufacturing Systems: A Framework Of Strategies, Policies, And Methods. Journal Of Scheduling. 6(1): 39-62.
Ruiz, R., Maroto, C., & Alcaraz, J. 2006. Two New Robust Genetic Algorithms For The Flow-Shop Scheduling Problem. Omega International Journal of Management Science. 34(5): 461-476.
Garey, M. R., & Johnson, D. S. 1979. Computer and intractability. A Guide to the Theory of NP-Completeness. Macmillan Higher Education.
Xia,W., & Wu, Z. 2005. An Effective Hybrid Optimization Approach For Multi-Objective Flexible Job-Shop Scheduling Problems. Computers & Industrial Engineering. 48(2): 409-425.
Rashid, Y., Kanji U. and Itsuo H. 2001. From Static To Sensor Scheduling: Distributed Environment. International Journal Of Manufacturing Technology And Management. 3(6): 586-599.
Montazeri, Mrn, and L. N. Van Wassenhove. 1990. Analysis Of Scheduling Rules For An FMS. The International Journal of Production Research. 28(4): 785-802.
Chiang, Tsung-Che, and Li-Chen Fu. 2007. Using Dispatching Rules For Job Shop Scheduling With Due Date-Based Objectives. International Journal of Production Research. 45(14): 3245-3262.
Dominic, P., Sathya K., and Saravana K. 2004. Efficient Dispatching Rules For Dynamic Job Shop Scheduling. The International Journal of Advanced Manufacturing Technology. 24(1): 70-75.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
