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A semantics-based dispatching rule selection approach for job shop scheduling

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Abstract

Dispatching rules are commonly used for job shop scheduling in industries because they are easy to implement, and they yield reasonable solutions within a very short computational time. Many dispatching rules have been developed but they can only perform well in specific scenarios. This is because a dispatching rule or a combination of dispatching rules always pursues a single or multiple fixed production objectives. A lot of approaches (e.g. simulation based or machine learning based approaches) have been published in the literatures attempted to solve the problem of selecting the proper dispatching rules for a given production objective. To select a combination of dispatching rules per randomly selected combination of objectives, this paper investigates a novel semantics-based dispatching rule selection system. Each of the dispatching rules and production objectives relates to a set of scheduling parameters like processing time, remaining work, total work, due date, release date, tardiness, etc. These parameters are semantically interrelated so that a dispatching rule and a production objective can also be semantically related through their semantic expressions. A semantic similarity value can be calculated by comparing their semantic expressions. Based on this idea, a semantics-based dispatching rule selection system for job shop scheduling is developed to generate a combination of dispatching rules given randomly selected combination of production objectives. A proof-of-concept verification process is provided at the end of the paper.

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References

  • Ahmed, F., & Han, S. (2015). Interoperability of product and manufacturing information using ontology. Concurrent Engineering, 23(3), 265–278.

    Article  Google Scholar 

  • Azadeh, A., Hosseini, N., Abdolhossein Zadeh, S., & Jalalvand, F. (2015). A hybrid computer simulation-adaptive neuro-fuzzy inference system algorithm for optimization of dispatching rule selection in job shop scheduling problems under uncertainty. The International Journal of Advanced Manufacturing Technology, 79(1), 135–145.

    Article  Google Scholar 

  • Azadeh, A., Negahban, A., & Moghaddam, M. (2012). A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems. International Journal of Production Research, 50(2), 551–566.

    Article  Google Scholar 

  • Baykasoglu, A., Göçken, M., & Özbakir, L. (2010). Genetic programming based data mining approach to dispatching rule selection in a simulated job shop. Simulation, 86(12), 715–728.

    Article  Google Scholar 

  • Blackstone, J. H., Phillips, D. T., & Hogg, G. L. (1982). A state-of-the-art survey of dispatching rules for manufacturing job shop operations. International Journal of Production Research, 20(1), 27–45.

    Article  Google Scholar 

  • Borgo, S., & Leitão, P. (2004). The role of foundational ontologies in manufacturing domain applications. In R. Meersman & Z. Tari (Eds.), On the move to meaningful internet systems 2004: CoopIS, DOA, and ODBASE. OTM 2004. Lecture notes in computer science (Vol. 3290). Berlin: Springer.

    Google Scholar 

  • Borsato, M. (2014). Bridging the gap between product lifecycle management and sustainability in manufacturing through ontology building. Computers in Industry, 65(2), 258–269.

    Article  Google Scholar 

  • Canbolat, Y. B., & Gundogar, E. (2004). Fuzzy priority rule for job shop scheduling. Journal of Intelligent Manufacturing, 15, 527–533.

    Article  Google Scholar 

  • Chang, X., Rai, R., & Terpenny, J. (2010). Development and utilization of ontologies in design for manufacturing. Journal of Mechanical Design, 132(2), 021009–021009.

    Article  Google Scholar 

  • Chen, X., Wen, L. H., & Murata, T. (2012). Composite dispatching rule design for dynamic scheduling with customer-oriented production priority control. IEEJ Transactions on Electrical and Electronic Engineering, 7(1), 53–61.

    Article  Google Scholar 

  • Chhim, P., Chinnam, R. B., & Sadawi, N. (2017). Product design and manufacturing process based ontology for manufacturing knowledge reuse. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-016-1290-2.

    Article  Google Scholar 

  • Chungoora, N., Canciglieri, O., & Young, R. I. M. (2010). Towards expressive ontology-based approaches to manufacturing knowledge representation and sharing. International Journal of Computer Integrated Manufacturing, 23(12), 1059–1070.

    Article  Google Scholar 

  • El Bouri, A., & Amin, G. R. (2015). A combined OWA-DEA method for dispatching rule selection. Computers & Industrial Engineering, 88, 470–478.

    Article  Google Scholar 

  • El-Bouri, A., & Shah, P. (2006). A neural network for dispatching rule selection in a job shop. The International Journal of Advanced Manufacturing Technology, 31(3), 342–349.

    Article  Google Scholar 

  • Fortineau, V., Paviot, T., & Lamouri, S. (2013). Improving the interoperability of industrial information systems with description logic-based models: The state of the art. Computers in Industry, 64(4), 363–375.

    Article  Google Scholar 

  • Gan, M., Dou, X., & Jiang, R. (2013). From ontology to semantic similarity: Calculation of ontology-based semantic similarity. The Scientific World Journal, 2013, Article ID 793091.

  • Geiger, C. D., Uzsoy, R., & Aytuğ, H. (2006). Rapid modeling and discovery of priority dispatching rules: An autonomous learning approach. Journal of Scheduling, 9(1), 7–34.

    Article  Google Scholar 

  • Guerra-Zubiaga, D. A., & Young, R. I. M. (2008a). Information and knowledge interrelationships within a manufacturing knowledge model. The International Journal of Advanced Manufacturing Technology, 39(1), 182–198.

    Article  Google Scholar 

  • Guerra-Zubiaga, D. A., & Young, R. I. M. (2008b). Design of a manufacturing knowledge model. International Journal of Computer Integrated Manufacturing, 21(5), 526–539.

    Article  Google Scholar 

  • Harispe, S., Ranwez, S., Janaqi, S., & Montmain, J. (2013). Semantic measures for the comparison of units of language, concepts or instances from text and knowledge representation analysis. arXiv:1310.1285.

  • Harispe, S., Ranwez, S., Janaqi, S., & Montmain, J. (2017). Semantic similarity from natural language and ontology analysis. arXiv:1704.05295.

  • Haupt, R. (1989). A survey of priority rule-based scheduling. Operations-Research-Spektrum, 11(1), 3–16.

    Article  Google Scholar 

  • Heger, J., Hildebrandt, T., & Scholz-Reiter, B. (2015). Dispatching rule selection with Gaussian processes. Central European Journal of Operations Research, 23(1), 235–249.

    Article  Google Scholar 

  • Joseph, O. A., & Sridharan, R. (2011). Effects of routing flexibility, sequencing flexibility and scheduling decision rules on the performance of a flexible manufacturing system. The International Journal of Advanced Manufacturing Technology, 56(1), 291–306.

    Article  Google Scholar 

  • Kashfi, M. A., & Javadi, M. (2015). A model for selecting suitable dispatching rule in FMS based on fuzzy multi attribute group decision making. Production Engineering, 9(2), 237–246.

    Article  Google Scholar 

  • Kızıl, M., Özbayrak, M., & Papadopoulou, T. C. (2006). Evaluation of dispatching rules for cellular manufacturing. The International Journal of Advanced Manufacturing Technology, 28(9), 985–992.

    Article  Google Scholar 

  • Lemaignan, S., Siadat, A., Dantan, J. Y., & Semenenko, A. (2006). MASON: A proposal for an ontology of manufacturing domain. Paper presented at IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS’06), Prague, Czech Republic. https://doi.org/10.1109/DIS.2006.48.

  • Lin, H. K., & Harding, J. A. (2007). A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration. Computers in Industry, 58(5), 428–437.

    Article  Google Scholar 

  • Lin, L. F., Zhang, W. Y., Lou, Y. C., Chu, C. Y., & Cai, M. (2011). Developing manufacturing ontologies for knowledge reuse in distributed manufacturing environment. International Journal of Production Research, 49(2), 343–359.

    Article  Google Scholar 

  • Lin, Y., Chiu, C., & Tsai, C. (2008). The study of applying ANP model to assess dispatching rules for wafer fabrication. Expert Systems with Applications, 34(3), 2148–2163.

    Article  Google Scholar 

  • Liu, H., & Dong, J. (1996). Dispatching rule selection using artificial neural networks for dynamic planning and scheduling. Journal of Intelligent Manufacturing, 7, 243–250.

    Article  Google Scholar 

  • Metaxiotis, K. S., Psarras, J. E., & Askounis, D. (2001). Building ontologies for production scheduling systems: Towards a unified methodology. Information Management & Computer Security, 9(1), 44–51.

    Article  Google Scholar 

  • Mouelhi-Chibani, W., & Pierreval, H. (2010). Training a neural network to select dispatching rules in real time. Computers & Industrial Engineering, 58(2), 249–256.

    Article  Google Scholar 

  • Panetto, H., Dassisti, M., & Tursi, A. (2012). ONTO-PDM: Product-driven ONTOlogy for product data management interoperability within manufacturing process environment. Advanced Engineering Informatics, 26(2), 334–348.

    Article  Google Scholar 

  • Panwalkar, S. S., & Iskander, W. (1977). A survey of scheduling rules. Operations Research, 25(1), 45–61.

    Article  Google Scholar 

  • Rahmani, K., & Thomson, V. (2012). Ontology based interface design and control methodology for collaborative product development. Computer-Aided Design, 44(5), 432–444.

    Article  Google Scholar 

  • Scholz-Reiter, B., Heger, J., & Hildebrandt, T. (2010). Gaussian processes for dispatching rule selection in production scheduling: Comparison of learning techniques. Paper presented at 2010 IEEE International Conference on Data Mining Workshops, Sydney, NSW, Australia. https://doi.org/10.1109/ICDMW.2010.19.

  • Shafiq, S. I., Faheem, M., & Ali, M. (2010). Effect of scheduling and manufacturing flexibility on the performance of FMS. Global Journal of Flexible Systems Management, 11(3), 21–38.

    Article  Google Scholar 

  • Shiue, Y. (2009). Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach. International Journal of Production Research, 47(13), 3669–3690.

    Article  Google Scholar 

  • Stanford Center for Biomedical Informatics Research (BMIR). (2018). Protégé: A free, open-source ontology editor and framework for building intelligent systems. http://protege.stanford.edu/. Accessed on 29 Jan 2018.

  • Talhi, A., Fortineau, V., Huet, J., & Lamouri, S. (2017). Ontology for cloud manufacturing based product lifecycle management. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-017-1376-5.

    Article  Google Scholar 

  • The Apache Software Foundation. (2018). Apache jena: A free and open source java framework for building semantic web and linked data application. https://jena.apache.org/. Accessed on 29 Jan 2018.

  • Usman, Z., Young, R. I. M., Chungoora, N., Palmer, C., Case, K., & Harding, J. A. (2013). Towards a formal manufacturing reference ontology. International Journal of Production Research, 51(22), 6553–6572.

    Article  Google Scholar 

  • W3C Working Group. (2012). OWL 2 web ontology language Manchester syntax (2\(^{\rm nd}\) ed.). W3C Recommendation.

  • Wilbrecht, J. K., & Prescott, W. B. (1969). The influence of setup time on job shop performance. Management Science, 16(4), 274–280.

    Article  Google Scholar 

  • Wu, Z., & Palmer, M. (1994). Verb semantics and lexical selection. Paper presented at Proceeding ACL ’94 Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, Las Cruces, New Mexico, USA.

  • Zhang, H., Zhu, B., Li, Y., Yaman, O., & Roy, U. (2015). Development and utilization of a process-oriented information model for sustainable manufacturing. Journal of Manufacturing Systems, 37(2), 459–466.

    Article  Google Scholar 

  • Zhong, R. Y., Huang, G. Q., Dai, Q. Y., & Zhang, T. (2014). Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data. Journal of Intelligent Manufacturing, 25, 825–843.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse, NY, 13244, USA

    Heng Zhang & Utpal Roy

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  1. Heng Zhang
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  2. Utpal Roy
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Correspondence to Heng Zhang.

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Zhang, H., Roy, U. A semantics-based dispatching rule selection approach for job shop scheduling. J Intell Manuf 30, 2759–2779 (2019). https://doi.org/10.1007/s10845-018-1421-z

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  • DOI: https://doi.org/10.1007/s10845-018-1421-z

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