Agent-based distributed manufacturing scheduling: an ontological approach

References

• Adhau, S., Mittal, M. L., & Mittal, A. (2012). A multi-agent system for distributed multi-project scheduling: An auction-based negotiation approach. Engineering Applications of Artificial Intelligence, 25(8), 1738–23. doi:10.1016/j.engappai.2011.12.003
   Web of Science ®Google Scholar
• Alvarez, E. (2007). Multi-plant production scheduling in SMEs. Robotics and Computer-Integrated Manufacturing, 23(6), 608–613. doi:10.1016/j.rcim.2007.02.006
   Web of Science ®Google Scholar
• Anand, N., van Duin, R., & Tavasszy, L. (2014). Ontology-based multi-agent system for urban freight transportation. International Journal of Urban Sciences, 18(2), 133–153. doi:10.1080/12265934.2014.920696
   Google Scholar
• Artto, K., Kulvik, I., Poskela, J., & Turkulainen, V. (2011). The integrative role of the project management office in the front end of innovation. International Journal of Project Management, 29(4), 408–421. doi:10.1016/j.ijproman.2011.01.008
   Web of Science ®Google Scholar
• Aydin, M. E., & Fogarty, T. C. (2004a). A simulated annealing algorithm for multi-agent systems: A job-shop scheduling application. Journal of Intelligent Manufacturing, 15(6), 805–814. doi:10.1023/B:JIMS.0000042665.10086.cf
   Web of Science ®Google Scholar
• Aydin, M. E., & Fogarty, T. C. (2004b). Teams of autonomous agents for job-shop scheduling problems: An Experimental Study. Journal of Intelligent Manufacturing, 15(4), 455–462. doi:10.1023/B:JIMS.0000034108.66105.59
   Web of Science ®Google Scholar
• Bai, D., & Tang, L. (2013). Open shop scheduling problem to minimize makespan with release dates. Applied Mathematical Modelling, 37(4), 2008–2015. doi:10.1016/j.apm.2012.04.037
   Web of Science ®Google Scholar
• Barbosa, J., Leitão, P., Adam, E., & Trentesaux, D. (2015). Dynamic self-organization in holonic multi-agent manufacturing systems: The ADACOR evolution. Computers in Industry, 66, 99–111. doi:10.1016/j.compind.2014.10.011
   Web of Science ®Google Scholar
• Bellifemine, F. L., Caire, G., & Greenwood, D. (2007). Developing multi-agent systems with JADE (Vol. 7).Chichester, UK: John Wiley & Sons.
   Google Scholar
• Bonabeau, E., Corne, D., Knowles, J., & Poli, R. (2010). Swarm intelligence theory: A snapshot of the state of the art. Theoretical Computer Science, 411(21), 2081–2083. doi:10.1016/j.tcs.2010.03.001
   Web of Science ®Google Scholar
• Cowling, P. I., Ouelhadj, D., & Petrovic, S. (2003). A multi-agent architecture for dynamic scheduling of steel hot rolling. Journal of Intelligent Manufacturing, 14(5), 457–470. doi:10.1023/A:1025701325275
   Web of Science ®Google Scholar
• Deepa, O., & Senthilkumar, A. (2016). Swarm intelligence from natural to artificial systems: Ant colony optimization. Networks (GRAPH-HOC), 8(1), 9–17.
   Google Scholar
• Frey, D., Nimis, J., Wörn, H., & Lockemann, P. (2003). Benchmarking and robust multi-agent-based production planning and control. Engineering Applications of Artificial Intelligence, 16(4), 307–320. doi:10.1016/S0952-1976(03)00075-7
   Web of Science ®Google Scholar
• Gabel, T. (2009). Multi-agent reinforcement learning approaches for distributed job-shop scheduling problems (Doctoral dissertation). Retrieved from https://repositorium.ub.uni-osnabrueck.de
   Google Scholar
• Gaševic, D., Djuric, D., & Devedžic, V. (2009). Model driven engineering and ontology development.  Berlin, Heidelberg: Springer Science & Business Media.
   Google Scholar
• Gen, M., Zhang, W., & Hao, X. (2017). Advances in hybrid metaheuristics for stochastic manufacturing scheduling: Part II case studies. In Proceedings of the Tenth International Conference on Management Science and Engineering Management (pp. 1079–1094). Singapore: Springer.
   Google Scholar
• Gennari, J. H., Musen, M. A., Fergerson, R. W., Grosso, W. E., Crubézy, M., Eriksson, H., … Tu, S. W. (2003). The evolution of Protégé: An environment for knowledge-based systems development. International Journal of Human-Computer Studies, 58(1), 89–123. doi:10.1016/S1071-5819(02)00127-1
   Web of Science ®Google Scholar
• Giordani, S., Lujak, M., & Martinelli, F. (2013). A distributed multi-agent production planning and scheduling framework for mobile robots. Computers & Industrial Engineering, 64(1), 19–30. doi:10.1016/j.cie.2012.09.004
   Web of Science ®Google Scholar
• Goldingay, H., & Van Mourik, J. (2013). The effect of load on agent-based algorithms for distributed task allocation. Information Sciences, 222, 66–80. doi:10.1016/j.ins.2011.06.011
   Web of Science ®Google Scholar
• Gruber, T. (1993). What is an Ontology. Retrieved from http://www-ksl. stanford. edu/kst/whatis-an-ontology
   Google Scholar
• Guarino, N., Oberle, D., & Staab, S. (2009). What is an ontology? In Handbook on ontologies (pp. 1–17). Berlin, Heidelberg: Springer.
   Google Scholar
• Guo, Q., & Zhang, M. (2009). A novel approach for multi-agent-based intelligent manufacturing system. Information Sciences, 179(18), 3079–3090. doi:10.1016/j.ins.2009.05.009
   Web of Science ®Google Scholar
• Harjunkoski, I., Maravelias, C. T., Bongers, P., Castro, P. M., Engell, S., Grossmann, I. E., … Wassick, J. (2014). Scope for industrial applications of production scheduling models and solution methods. Computers & Chemical Engineering, 62, 161–193. doi:10.1016/j.compchemeng.2013.12.001
   Web of Science ®Google Scholar
• Hasan, S. K., Sarker, R., Essam, D., & Cornforth, D. (2009). A genetic algorithm with priority rules for solving job-shop scheduling problems. In Natural intelligence for scheduling, planning and packing problems (pp. 55–88). Berlin, Heidelberg: Springer.
   Google Scholar
• Jules, G., & Saadat, M. (2017). Agent cooperation mechanism for decentralized manufacturing scheduling. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(12), 3351–3362. doi:10.1109/TSMC.2016.2578879
   Web of Science ®Google Scholar
• Jules, G. D., Saadat, M., & Li, N. (2013). On designing a unified ontology for holonic manufacturing networks. In Integration of practice-oriented knowledge technology: Trends and prospectives (pp. 207–220). Berlin, Heidelberg: Springer.
   Google Scholar
• Jules, G. D., Saadat, M., & Saeidlou, S. (2015). Holonic ontology and interaction protocol for manufacturing network organization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(5), 819–830. doi:10.1109/TSMC.2014.2387099
   Web of Science ®Google Scholar
• Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks. (pp. IV, 1942–1948). Piscataway.
   Google Scholar
• Komma, V. R., Jain, P. K., & Mehta, N. K. (2011). An approach for agent modeling in manufacturing on JADE™ reactive architecture. The International Journal of Advanced Manufacturing Technology, 52(9–12), 1079–1090. doi:10.1007/s00170-010-2784-2
   Web of Science ®Google Scholar
• Kotulski, L., Sȩdziwy, A., & Strug, B. (2014). Translation of graph-based knowledge representation in multi-agent system. Procedia Computer Science, 29, 1048–1056. doi:10.1016/j.procs.2014.05.094
   Google Scholar
• Kulon, J., Broomhead, P., & Mynors, D. J. (2006). Applying knowledge-based engineering to traditional manufacturing design. The International Journal of Advanced Manufacturing Technology, 30(9–10), 945–951. doi:10.1007/s00170-005-0067-0
   Web of Science ®Google Scholar
• Kurdi, M. (2017). An improved island model memetic algorithm with a new cooperation phase for multi-objective job shop scheduling problem. Computers & Industrial Engineering, 111, 183–201. doi:10.1016/j.cie.2017.07.021
   Web of Science ®Google Scholar
• Lawrence, S. (1984). Resouce constrained project scheduling: An experimental investigation of heuristic scheduling techniques (Supplement). Graduate School of Industrial Administration Pittsburgh, PA: Carnegie-Mellon University.
   Google Scholar
• Leitão, P., Barbosa, J., & Trentesaux, D. (2012). Bio-inspired multi-agent systems for reconfigurable manufacturing systems. Engineering Applications of Artificial Intelligence, 25(5), 934–944. doi:10.1016/j.engappai.2011.09.025
   Web of Science ®Google Scholar
• Li, W., Veliyath, R., & Tan, J. (2013). Network characteristics and firm performance: An examination of the relationships in the context of a cluster. Journal of Small Business Management, 51(1), 1–22. doi:10.1111/jsbm.2013.51.issue-1
   Web of Science ®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. doi:10.1080/00207540903349021
   Web of Science ®Google Scholar
• Lin, T. L., Horng, S. J., Kao, T. W., Chen, Y. H., Run, R. S., Chen, R. J., … Kuo, I. H. (2010). An efficient job-shop scheduling algorithm based on particle swarm optimization. Expert Systems with Applications, 37(3), 2629–2636. doi:10.1016/j.eswa.2009.08.015
   Web of Science ®Google Scholar
• Luck, M., McBurney, P., & Preist, C. (2003). Agent technology: Enabling next generation computing (a roadmap for agent based computing). University of Southampton: AgentLink.
   Google Scholar
• Martin, S., Ouelhadj, D., Beullens, P., Ozcan, E., Juan, A. A., & Burke, E. K. (2016). A multi-agent based cooperative approach to scheduling and routing. European Journal of Operational Research, 254(1), 169–178. doi:10.1016/j.ejor.2016.02.045
   Web of Science ®Google Scholar
• Meloni, C., Pacciarelli, D., & Pranzo, M. (2004). A rollout metaheuristic for job shop scheduling problems. Annals of Operations Research, 131(1–4), 215–235. doi:10.1023/B:ANOR.0000039520.24932.4b
   Web of Science ®Google Scholar
• Mohebbi, S., & Shafaei, R. (2012). e-Supply network coordination: The design of intelligent agents for buyer-supplier dynamic negotiations. Journal of Intelligent Manufacturing, 23(3), 375–391. doi:10.1007/s10845-009-0377-4
   Web of Science ®Google Scholar
• Monostori, L., Váncza, J., & Kumara, S. R. (2006). Agent-based systems for manufacturing. CIRP Annals-Manufacturing Technology, 55(2), 697–720. doi:10.1016/j.cirp.2006.10.004
   Web of Science ®Google Scholar
• Müller-Seitz, G., & Sydow, J. (2012). Maneuvering between networks to lead–A longitudinal case study in the semiconductor industry. Long Range Planning, 45(2–3), 105–135. doi:10.1016/j.lrp.2012.02.001
   Web of Science ®Google Scholar
• Nguyen, S., Mei, Y., & Zhang, M. (2017). Genetic programming for production scheduling: A survey with a unified framework. Complex & Intelligent Systems, 3(1), 41–66. doi:10.1007/s40747-017-0036-x
   Web of Science ®Google Scholar
• Noy, N. F., Sintek, M., Decker, S., Crubézy, M., Fergerson, R. W., & Musen, M. A. (2001). Creating semantic web contents with protege-2000. IEEE Intelligent Systems, 16(2), 60–71. doi:10.1109/5254.920601
   Web of Science ®Google Scholar
• Owiti, O. H., Omulo, E. T. O., Okelo-Odongo, W., & Manderick, B. (2014). Game theoretic multi-agent algorithms for the job shop scheduling problem. International Journal of Computer and Information Technology, 3, 06.
   Google Scholar
• Owliya, M., Saadat, M., Jules, G. G., Goharian, M., & Anane, R. (2013). Agent-based interaction protocols and topologies for manufacturing task allocation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(1), 38–52. doi:10.1109/TSMCA.2012.2192263
   Web of Science ®Google Scholar
• Rini, D. P., Shamsuddin, S. M., & Yuhaniz, S. S. (2011). Particle swarm optimization: Technique, system and challenges. International Journal of Computer Applications, 14(1), 19–26. doi:10.5120/1810-2331
   Google Scholar
• Saeidlou, S., Saadat, M., Amini Sharifi, E., & Jules, G. D. (2017). An ontology-based intelligent data query system in manufacturing networks. Production & Manufacturing Research, 5(1), 250–267. doi:10.1080/21693277.2017.1374887
   Web of Science ®Google Scholar
• Schalkoff, R. J. (2011). Intelligent systems: Principles, paradigms and pragmatics. Boston, MA: Jones & Bartlett Publishers.
   Google Scholar
• Shen, L., Dauzère-Pérès, S., & Neufeld, J. S. (2018). Solving the flexible job shop scheduling problem with sequence-dependent setup times. European Journal of Operational Research, 265(2), 503–516. doi:10.1016/j.ejor.2017.08.021
   Web of Science ®Google Scholar
• Shen, W. (2002). Distributed manufacturing scheduling using intelligent agents. IEEE Intelligent Systems, 17(1), 88–94. doi:10.1109/5254.988492
   Web of Science ®Google Scholar
• Uschold, M., & Gruninger, M. (1996). Ontologies: Principles, methods and applications. The Knowledge Engineering Review, 11(2), 93–136. doi:10.1017/S0269888900007797
   Web of Science ®Google Scholar
• Vallikavungal Devassia, J., Salazar-Aguilar, M. A., & Boyer, V. (2018). Flexible job-shop scheduling problem with resource recovery constraints. International Journal of Production Research, 56, 1–18.
   Web of Science ®Google Scholar
• Vrba, P., & Marik, V. (2010). Capabilities of dynamic reconfiguration of multiagent-based industrial control systems. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40(2), 213–223. doi:10.1109/TSMCA.2009.2034863
   Web of Science ®Google Scholar
• Wang, L., Tang, D. B., Gu, W. B., Zheng, K., Yuan, W. D., & Tang, D. S. (2012). Pheromone-based coordination for manufacturing system control. Journal of Intelligent Manufacturing, 23(3), 747–757. doi:10.1007/s10845-010-0426-z
   Web of Science ®Google Scholar
• Wang, K., & Choi, S. H. (2014). A holonic approach to flexible flow shop scheduling under stochastic processing times. Computers & operations research, 43, 157–168. doi:10.1016/j.cor.2013.09.013
   Web of Science ®Google Scholar
• Wang, Z., & Liu, Y. (2006, October). A multi-agent agile scheduling system for job-shop problem. In Intelligent Systems Design and Applications, 2006. ISDA’06. Sixth International Conference on (Vol.2, pp. 679–683). Jinan, China: IEEE.
   Google Scholar
• Wong, T. N., Leung, C. W., Mak, K. L., & Fung, R. Y. (2006). Dynamic shopfloor scheduling in multi-agent manufacturing systems. Expert Systems with Applications, 31(3), 486–494. doi:10.1016/j.eswa.2005.09.073
   Web of Science ®Google Scholar
• Wu, Z. (2005). Multi-agent workload control and flexible job shop scheduling (Doctoral dissertation). Retrieved from https://scholarcommons.usf.edu
   Google Scholar
• Yoo, M. J., & Müller, J. P. (2002, April). Using multi-agent system for dynamic job shop scheduling. 4th International Conference on Enterprise Information Systems : Universidad de Castilla-La Mancha, Ciudad Real, Spain. UCLM. s.l. : s.n., 8 .
   Google Scholar
• Zhao, B., Gao, J., Chen, K., & Guo, K. (2018). Two-generation Pareto ant colony algorithm for multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines. Journal of Intelligent Manufacturing, 29(1), 93–108. doi:10.1007/s10845-015-1091-z
   Web of Science ®Google Scholar