Job Shop problem solution with an intelligent agent
Main Article Content
Keywords
makespan time, Idle time, intelligent agents, production sequencing, Job Shop, evolutionary algorithms, multiobjective optimization
Abstract
In this paper, is defines a new and effective methodology based on intelligent agents for production sequencing Job Shop Environments; especially for small and medium enterprises in the metal mechanics sector, where these techniques have not been employed , due to the high resistance to change. This work is developed in two phases. In the first one, the different techniques used are defined. In the second step, statistical tests are executed in order to determine the approximation percentage solutions’ to the optimal or sub optimal solution. This work’s results show that the intelligent agents techniques don’t produce an optimal result every single time; but in few seconds, these techniques, can find sub optimal solutions with an approximation of 97.81% and 90.43%, to the optimal or sub optimal solution, in the variables total time process and total idle time, respectively. This contrasts with the little effectiveness of traditional techniques.
MSC: 68Txx, 68Uxx
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References
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[35] C. Romero, P. González, S. Ventura, M. J. del Jesús and F. Herrera. Evolu- tionary algorithms for subgroup discovery in e–learning: a practical application using moodle data. Expert systems with applications, ISSN 0957–4174, 36(2), 1632–1644 (2009).
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[46] Yuhong Yan, Torsten Kuphal and J¨urgen Bode. Application of multiagent systems in project management . International journal of production economics, ISSN 0925–5273, 68(2), 185–197 (2000).
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[49] Omar D. Castrillón, William A. Sarache y Jaime A. Giraldo. Análisis de un pro- blema job–shop por medio de un sistema experto y un agente inteligente. Congreso de Ingeniería de la Organización, Barcelona, 2009.
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[2] M. R. Garey, D. S. Johnson and R. Sethi. The complexity of flowshop and job–shop scheduling. Mathematics of operations research, ISSN 0364–765X, 1(2), 117–129 (1976).
[3] Jin hui Yang, Liang Sun, Heow Pueh Lee, Yun Qiand and Yan-chun Liang. Clonal selection based memetic algorithm for job–shop scheduling problems. Journal of bionic engineering, ISSN 1672–6529, 5(2), 111–119 (2008).
[4] M. Elena Pérez y Francisco Herrera. Algoritmos genéticos multimodales: un estudio sobre la parametrización del método clearing aplicado al problema “Job Shop”. http://sci2s.ugr.es/publications/ficheros/0679.pdf, julio 2009.
[5] Ángel A. Castillo. Agentes inteligentes. http://www.sia.eui.upm.es/grupos/ IntroAI.pdf, julio 2009.
[6] Pablo L. Navarra y José A. Martínez. Agentes inteligentes en la búsqueda y recuperación de información, ISBN 84–9707–571–4. Editorial planeta, Barcelona, 2004.
[7] V. Julián y V. Botti. Agentes inteligentes: el siguiente paso en la inteligencia artificial . Novatica, ISSN 0211–2124, 1(145), 95–99 (2000).
[8] Li–Ning Xing, Ying–Wu Chen and Ke–Wei Yang. Multi–objective flexible Job Shop schedule: design and evaluation by simulation modeling. Applied soft computing, ISSN 1568–4946, 9(1), 362–376 (2009).
[9] Shi–jin Wang, Li–feng Xi and Bing–hai Zhou. FBS–enhanced agent–based dynamic scheduling in FMS. Engineering applications of artificial intelligence, ISSN 0952–1976, 21(4), 644–657 (2008).
[10] Alain Cardon, Thierry Galinho and Jean–Philippe Vacher. Genetic algorithms using multi objectives in a multi–agent . Robotics and autonomous systems, ISSN 0921–8890, 33, 179-190 (2000).
[11] Omar López–Ortega and Israel Villar–Medina. A multi–agent system to cons- truct production orders by employing an expert system and a neural network. Expert Systems with Applications: An International Journal, ISSN 0957–4174, 36(2), 2937–2946 (2009).
[12] María Dolores R.–Moreno, Daniel Borrago, Amedeo Cesta and Angelo Oddi. Integrating planning and scheduling in workflow domains. Expert Systems with Applications: An International Journal, ISSN 0957–4174, 33(2), 389–406 (2007).
[13] Ruey–Shun Chen and Mengru Tu. Development of an agent–based system for manufacturing control and coordination with ontology and RFID technology. Expert Systems with Applications: An International Journal, ISSN 0957–4174, 36(4), 7581–7593 (2009).
[14] Nhu Binh Ho, Joc Cing Tay and Lai Edmund M.–K. An effective architecture for learning and evolving flexible job–shop schedules. European journal of operational research, ISSN 0377–2217, 179(2), 316–333 (2007).
[15] Arvind Sathi, Thomas Morton and Steven Roth. Callisto: an intelligent project management system. AI magazine, ISSN 0738–4602, 7(5), 34–52 (1986).
[16] Amy Trappey, Tung–Hung Lu and Li–DienFu. Development of an intelligent agent system for collaborative mold production with RFID technology. Robotics and computer–integrated manufacturing, ISSN 0736–5845, 25(1), 42–56 (2009).
[17] Kuo–Ling Huang and Ching–Jong Liao. Ant colony optimization combined with taboo search for the Job Shop scheduling problem. Computers & operations research, ISSN 0305–0548, 35(4), 1030–1046 (2008).
[18] Andrea Rossi and Elena Boschi. A hybrid heuristic to solve the parallel machines job–shop scheduling problem. Advances in Engineering Software, ISSN 0965–9978, 40(2), 118–127 (2009).
[19] óscar Buitrago–Suescún, Rodrigo Britto–Agudelo y Gonzalo Mejía– Delgadillo. Análisis comparativo de colonia de hormigas vs. un enfoque combinado cuello de botella móvil/búsqueda tabú en la minimización de la tardanza ponderada total en sistemas de manufactura tipo taller. http://caribdis.unab.edu.co/pls/portal/docs/PAGE/REVISTACOLOMBIANA COMPUTO/RCC ESPANOL/NUMEROSANTERIORES/JUNIO2007/R81 AR T2 C.PDF, julio 2009.
[20] W. Xiang and H. P. Lee. Ant colony intelligence in multi–agent dynamic manufacturing scheduling. Engineering applications of artificial intelligence, ISSN 0952–1976, 21(1), 73–85 (2008).
[21] Hong Zhou, Cheung Waiman and Leung Lawrence. Minimizing weighted tardiness of job–shop scheduling using a hybrid genetic algorithm. European journal of operational research, ISSN 0377–2217, 194(3), 637–649 (2009).
[22] N. Liu, M. A. Abdelrahman and S. Ramaswamy. Robust and adaptable Job Shop scheduling using multiple agents, ISBN 0–7803–8808–9. Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, 2005. SSST ’05, 45– 49 (2005).
[23] H. K. T¨onshoff, O. Herzog and I. J. Timm. Integrated process planning and production control based on the application of intelligent agents. http://en.scientificcommons.org/42323286, january 2009. Referenciado en 79
[24] Weiming Shen. Distributed manufacturing scheduling using intelligent agents. IEEE Intelligent Systems, ISSN 1541–1672, 17(1), 88–94 (2002).
[25] M. K. Lim and D. Z. Zhang. An integrated agent–based approach for responsive control of manufacturing resources. Computers & Industrial Engineering, ISSN 0360–8352, 46(2), 221–232 (2004).
[26] Weiming Shen and Douglas H. Norrie. An Agent– Based approach for dynamic manufacturing scheduling. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.2734, January 2009.
[27] Ludovica Adacher, Alessandro Agnetis and Carlo Meloni. Autonomous agents architectures and algorithms in flexible manufacturing systems. IIE Transactions, ISSN 0740–817X, 32(10), 941–951 (2000).
[28] Weiming Shen, Lihui Wang and QiHao. Agent–Based distributed manufacturing process planning and scheduling: a state–of–the-art survey. IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews, ISSN 1094–6977, 36(4), 563–577 (2006).
[29] Anthony Karageorgos, Nikolay Mehandjiev, Alexander Hammerle and Georg Weichhart. Agent–based optimization of logistics and production planning. http://users.teilar.gr/˜karageorgos/publications/IMS 2003 v 6.0 Karageorgos et al camera ready.pdf, julio 2009.
[30] M. Emin Aydin and Terence C. Fogarty. A simulated annealing algorithm for multi–agent systems: a job–shop scheduling application: applied intelligent heuristics for responsive manufacturing. Journal of intelligent manufacturing, ISSN 0956–5515, 15(6), 805–814 (2004).
[31] Emmy M. Ayden and Terence C. Fagorty. Teams of autonomous agents for job–shop scheduling problems: an experimental study: intelligent manufacturing systems: vision for the future. Journal of intelligent manufacturing, ISSN 0956– 5515, 15(4), 455–462 (2004).
[32] Zhanjie Wang and Yanbo Liu. A Multi–Agent agile scheduling system for job– shop problem, ISBN 0–7695–2528–8. Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications, Jinan, 679–683 (2006).
[33] Gonzalo Villarreal. Agentes inteligentes en educación. Edutec–E, Revista electrónica de tecnología educativa, ISSN 1135–9250, 16, 1–5 (2003).
[34] JohnWang, Ruiliang Yan, Kimberly Hollister and Dan Zhu. A historic review of management science research in China. Omega, ISSN 0305–0483, 36(6), 919–932 (2008).
[35] C. Romero, P. González, S. Ventura, M. J. del Jesús and F. Herrera. Evolu- tionary algorithms for subgroup discovery in e–learning: a practical application using moodle data. Expert systems with applications, ISSN 0957–4174, 36(2), 1632–1644 (2009).
[36] Maria J. Lamarca. Robots y agentes. http://www.hipertexto.info/documentos/ robot agent.htm, julio 2009.
[37] J. Sauer and H. J. Appelrath. Scheduling the supply chain by teams of agent. http://www2.computer.org/portal/web/csdl/doi/10.1109/HICSS.2003.1174200, ISBN 0–7695–1874–5, julio 2009.
[38] Isabel C. Zattar, Joao C. E. Ferreira, Joao G. Rodríguez and Carlos Humberto B De Sousa. Integration between process planning and scheduling using feature–based time–extended negotiation protocols in a multiagent system. International journal of services operations and informatics, ISSN 1741–5403, 3(1), 71–89 (2008).
[39] Jeremy Blum and Azim Eskandarian. Enhancing intelligent agent collaboration for flow optimization of railroad traffic. Transportation research part a: policy and practice, ISSN 0965–8564, 36(10), 919–930 (2002).
[40] Yu Lean, Wang Shouyang and Lai Kin Keung. An intelligent–agent–based fuzzy group decision making model for financial multicriteria decision support: the case of credit scoring. European journal of operational research, ISSN 0377–2217, 195(3), 942–959 (2009).
[41] Lessmann Stefan, Sung Ming-Chien and Johnson Johnnie E. V. Identifying win- ners of competitive events: a SVM–based classification model for horserace prediction. European journal of operational research, ISSN 0377–2217, 196(2), 569–577 (2009).
[42] Hyung Rim Choi, Hyun Soo Kim, Byung Joo Park, et al. An agent for selecting óptimal order set in EC marketplace. Decision Support Systems, ISSN 0167–9236, 36(4), 371–383 (2004).
[43] J. Sun, Y. Zhang, Nee A. Y. C. Agent-based product design and planning for distributed concurrent engineering. Proceedings. ICRA ’00. IEEE International Conference on Robotics and Automation, 2000., ISBN 0–7803–5886–4, 4, 3101– 3106 (2000).
[44] Jens Henoch and Heinz Ulrich. Agent–based management systems in logistics. http://www.ifor.math.ethz.ch/publications/2000 agentbasedmanagementsystems.pdf, julio 2009.
[45] Byung-in Kim, Robert J. Graves, Sunderesh S. Heragu and Art St. Onge. Intelligent agent modeling of an industrial warehousing Problem. IIE transactions, ISSN 1573–9724, 34(7), 601–612 (2002).
[46] Yuhong Yan, Torsten Kuphal and J¨urgen Bode. Application of multiagent systems in project management . International journal of production economics, ISSN 0925–5273, 68(2), 185–197 (2000).
[47] Sev V. Nagalingam and Grier C.I. Lin. CIM–still the solution for manufacturing industry. Robotics and computer–integrated manufacturing, ISSN 0736– 5845, 24(3), 332–344 (2008).
[48] Omar D. Castrillón, Jaime A. Giraldo y William A. Sarache. Secuenciación en ambientes job–shop por medio de sistemas expertos y agentes inteligentes, ISBN 978–1–934272–64–0. Octava conferencia iberoamericana en sistemas, cibernética e informática, Orlando, 39–42 (2009).
[49] Omar D. Castrillón, William A. Sarache y Jaime A. Giraldo. Análisis de un pro- blema job–shop por medio de un sistema experto y un agente inteligente. Congreso de Ingeniería de la Organización, Barcelona, 2009.
[50] J. R. Latta, E. G. Sarabia, D. Fernández, J. Arce y J. P. Oria. Aplicación de inteligencia artificial en sistemas automatizados de producción. Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN 1988–3064, 4(10), 100–110 (2000).
[51] Koonce D. A. and Tsai S. C. Using data mining to find patterns in genetic algorithm solutions to a job–shop schedule. Computer & Industrial Engineering, ISSN 0360–8352, 38(3), 361–374 (2000).