Digital Twins: Review and Challenges

[EN] With the arises of Industry 4.0, numerous concepts have emerged; one of the main concepts is the digital twin (DT). DT is being widely used nowadays, however, as there are several uses in the existing literature; the understanding of the concept and its functioning can be diffuse. The main goal of this paper is to provide a review of the existing literature to clarify the concept, operation, and main characteristics of DT, to introduce the most current operating, communication, and usage trends related to this technology, and to present the performance of the synergy between DT and multi-agent system (MAS) technologies through a computer science approach.This work was partly supported by the Spanish Government (RTI2018-095390-B-C31)Juárez-Juárez, MG.; Botti, V.; Giret Boggino, AS. (2021). Digital Twins: Review and Challenges. Journal of Computing and Information Science in Engineering. 21(3):1-23. https://doi.org/10.1115/1.405024412321

Dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism

[EN] With the development of the market globalisation trend and increasing customer orientation, many uncertainties have entered into the manufacturing context. To create an agile response to the emergence of and change in conditions, this article presents a dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism. The dynamic re-scheduling function is the result of cooperation among several autonomous bio-inspired manufacturing cells with computing power and optimisation capabilities. The dynamic re-scheduling model is designed based on hormone regulation principles to agilely respond to the frequent occurrence of unexpected disturbances at the shop floor level. The cooperation mechanisms of the dynamic re-scheduling model are described in detail, and a test bed is set up to simulate and verify the dynamic re-scheduling approach. The results verify that the proposed method is able to improve the performances and enhance the stability of a manufacturing systemThis research was sponsored by the National Natural Science Foundation of China (NSFC) under Grant No. 51175262 and No. 61105114 and the Jiangsu Province Science Foundation for Excellent Youths under Grant BK20121011. This research was also sponsored by the CASES project supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under grant agreement No. 294931Zheng, K.; Tang, D.; Giret Boggino, AS.; Gu, W.; Wu, X. (2015). Dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 229(S1):121-134. https://doi.org/10.1177/0954405414558699S121134229S1Maravelias, C. T., & Sung, C. (2009). Integration of production planning and scheduling: Overview, challenges and opportunities. Computers & Chemical Engineering, 33(12), 1919-1930. doi:10.1016/j.compchemeng.2009.06.007Yandra, & Tamura, H. (2007). A new multiobjective genetic algorithm with heterogeneous population for solving flowshop scheduling problems. International Journal of Computer Integrated Manufacturing, 20(5), 465-477. doi:10.1080/09511920601160288Fattahi, P., & Fallahi, A. (2010). Dynamic scheduling in flexible job shop systems by considering simultaneously efficiency and stability. CIRP Journal of Manufacturing Science and Technology, 2(2), 114-123. doi:10.1016/j.cirpj.2009.10.001Renna, P. (2011). Multi-agent based scheduling in manufacturing cells in a dynamic environment. International Journal of Production Research, 49(5), 1285-1301. doi:10.1080/00207543.2010.518736Qin, L., & Kan, S. (2013). Production Dynamic Scheduling Method Based on Improved Contract Net of Multi-agent. Advances in Intelligent Systems and Computing, 929-936. doi:10.1007/978-3-642-31656-2_128Iwamura, K., Mayumi, N., Tanimizu, Y., & Sugimura, N. (2010). A Study on Real-time Scheduling for Holonic Manufacturing Systems - Application of Reinforcement Learning -. Service Robotics and Mechatronics, 201-204. doi:10.1007/978-1-84882-694-6_35Jana, T. K., Bairagi, B., Paul, S., Sarkar, B., & Saha, J. (2013). Dynamic schedule execution in an agent based holonic manufacturing system. Journal of Manufacturing Systems, 32(4), 801-816. doi:10.1016/j.jmsy.2013.07.004Dan, Z., Cai, L., & Zheng, L. (2009). Improved multi-agent system for the vehicle routing problem with time windows. Tsinghua Science and Technology, 14(3), 407-412. doi:10.1016/s1007-0214(09)70058-6Hsieh, F.-S. (2009). Developing cooperation mechanism for multi-agent systems with Petri nets. Engineering Applications of Artificial Intelligence, 22(4-5), 616-627. doi:10.1016/j.engappai.2009.02.006Tang, D., Gu, W., Wang, L., & Zheng, K. (2011). A neuroendocrine-inspired approach for adaptive manufacturing system control. International Journal of Production Research, 49(5), 1255-1268. doi:10.1080/00207543.2010.518734Keenan, D. M., Licinio, J., & Veldhuis, J. D. (2001). A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitary-adrenal axis. Proceedings of the National Academy of Sciences, 98(7), 4028-4033. doi:10.1073/pnas.051624198Farhy, L. S. (2004). Modeling of Oscillations in Endocrine Networks with Feedback. Numerical Computer Methods, Part E, 54-81. doi:10.1016/s0076-6879(04)84005-9Cavalieri, S., Macchi, M., & Valckenaers, P. (2003). Journal of Intelligent Manufacturing, 14(1), 43-58. doi:10.1023/a:1022287212706Leitão, P., & Restivo, F. (2008). A holonic approach to dynamic manufacturing scheduling. Robotics and Computer-Integrated Manufacturing, 24(5), 625-634. doi:10.1016/j.rcim.2007.09.005Bal, M., & Hashemipour, M. (2009). Virtual factory approach for implementation of holonic control in industrial applications: A case study in die-casting industry. Robotics and Computer-Integrated Manufacturing, 25(3), 570-581. doi:10.1016/j.rcim.2008.03.020Leitao P. An agile and adaptive holonic architecture for manufacturing control. PhD Thesis, University of Porto, Porto, 2004

An engineering framework for Service-Oriented Intelligent Manufacturing Systems

Nowadays fully integrated enterprises are being replaced by business networks in which each participant provides others with specialized services. As a result, the Service Oriented Manufacturing Systems emerges. These systems are complex and hard to engineer. The main source of complexity is the number of different technologies, standards, functions, protocols, and execution environments that must be integrated in order to realize them. This paper proposes a framework and associated engineering approach for assisting the system developers of Service Oriented Manufacturing Systems. The approach combines multi-agent system with Service Oriented Architectures for the development of intelligentautomation control and execution of manufacturing systems.Giret Boggino, AS.; Garcia Marques, ME.; Botti Navarro, VJ. (2016). An engineering framework for Service-Oriented Intelligent Manufacturing Systems. Computers in Industry. 81:116-127. doi:10.1016/j.compind.2016.02.002S1161278

A hormone regulation based approach for distributed and on-line scheduling of machines and automated guided vehicles

[EN] With the continuous innovation of technology, automated guided vehicles are playing an increasingly important role on manufacturing systems. Both the scheduling of operations on machines as well as the scheduling of automated guided vehicles are essential factors contributing to the efficiency of the overall manufacturing systems. In this article, a hormone regulation¿based approach for on-line scheduling of machines and automated guided vehicles within a distributed system is proposed. In a real-time environment, the proposed approach assigns emergent tasks and generates feasible schedules implementing a task allocation approach based on hormonal regulation mechanism. This approach is tested on two scheduling problems in literatures. The results from the evaluation show that the proposed approach improves the scheduling quality compared with state-of-the-art on-line and off-line approaches.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was sponsored by the National Natural Science Foundation of China (NSFC) under grant nos 51175262 and 51575264 and the Jiangsu Province Science Foundation for Excellent Youths under grant no. BK2012032. This research was also sponsored by the CASES project which was supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under grant agreement no. 294931.Zheng, K.; Tang, D.; Giret Boggino, AS.; Salido, MA.; Sang, Z. (2016). A hormone regulation based approach for distributed and on-line scheduling of machines and automated guided vehicles. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 232(1):99-113. https://doi.org/10.1177/0954405416662078S99113232

mWater prototype review

This document reviews our current water policy-making decision-support framework, build on top of a regulated open Multi-Agent System (MAS),mWater [BGG+10, GGG+11], that models a flexible water-rights market. Our simulator focuses on the effect of regulations on demand and thus provides means to explore the interplay of norms and conventions that regulate trading (like trader eligibility conditions, tradeable features of rights, trading periods and price-fixing conventions), the assumptions about agent behaviour (individual preferences and risk attitude, or population profile mixtures) and market scenarios (water availability and use restrictions). A policy-maker would then assess the effects of those interactions by observing the evolution of the performance indicators (efficiency of use, price dynamics, welfare functions) (s)he designs. 1.2 OurBotti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS.; Noriega, P. (2013). mWater prototype review. http://hdl.handle.net/10251/3212

An Intelligent Platform for supporting optimized collaborative urban logistics

[EN] Optimized urban logistics is an important issue for rapidly growing cities worldwide. Many criteria can be optimized in order to improve the performance of urban logistics. Economic and time dependent criteria are central but not the only ones; lately, sustainable criteria are becoming key and urgent due to new regulations and environmental concern of governments and the society. In this work we review the state of the art of intelligent developments and techniques that might aid to build smart and optimized urban logistic applications. Moreover, we propose a prototype platform conceived as a supporting and facilitating layer for the growing business of last mile delivery (LMD) companies that operate in cities in an isolated way. Our vision is to provide a cooperative intelligent platform that provides coordination and collaboration services for the LMD companies of urban areas.This research is supported by research project TIN2015-65515-C4-1-R from the Spanish government.Giret Boggino, AS.; Julian Inglada, VJ.; Botti, V. (2019). An Intelligent Platform for supporting optimized collaborative urban logistics. Springer. 3-14. https://doi.org/10.1007/978-3-030-27477-1_1S314Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., Rus, D.: On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc. Natl. Acad. Sci. 114(3), 462–467 (2017)Bianchi, L., Dorigo, M., Gambardella, L.M., Gutjahr, W.J.: A survey on metaheuristics for stochastic combinatorial optimization. Nat. Comput. 8(2), 239–287 (2009)Buning, M., Schonewolf, W.: Freight transport system for urban shipment and delivery. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, FISTS 2011, pp. 136–140 (2011)Gentile, G., Noekel, K.: Modeling Public Transport Passenger Flows in the Era of Intelligent Transport Systems. Springer, Heidelberg (2016)Gonzalez-Feliu, J., Semet, F., Routhier, J.L.: Sustainable Urban Logistics: Concepts, Methods and Information Systems. Springer, Heidelberg (2014)Griffis, S.E., Goldsby, T.J., Cooper, M., Closs, D.J.: Aligning logistics performance measures to the information needs of the firm. J. Bus. Logist. 48, 35–56 (2007)Gunasekaran, A., Kobu, B.: Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. Int. J. Prod. Res. 45, 2819–2840 (2007)Macharis, C., Melo, S.: City Distribution and Urban Freight Transport: Multiple Perspectives. Edward Elgar Publishing, Cheltenham (2011)Morana, J., Gonzalez-Feliu, J.: A sustainable urban logistics dashboard from the perspective of a group of operational managers. Manag. Res. Rev. 38(10), 1068–1085 (2015)Neirotti, P., De Marco, A., Cagliano, A.C., Mangano, G., Scorrano, F.: Current trends in smart city initiatives: some stylised facts. Cities 38, 25–36 (2014)Pagell, M., Wu, Z.: Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars. J. Supply Chain Manag. 45, 37–56 (2009)Chatterjee, R.: Optimizing last mile delivery using public transport with multiagent based control. Master Thesis, pp. 1–59 (2016)Market Reports. Global last mile delivery market size, status and forecast 2019–2025. The Market Reports. Report Code 1362721, pp. 1–114 (2019)Sabater, J., Sierra, C.: Review on computational trust and reputation models. Artif. Intell. Rev. 24(1), 33–60 (2005)Skiver, R.L., Godfrey, M.: Crowdserving: A last mile delivery method for brick-and-mortar retailers. Global J. Bus. Res. 11(2), 67–77 (2017)Xiao, Z., Wang, J.J., Lenzer, J., Sun, Y.: Understanding the diversity of final delivery solutions for online retailing: a case of shenzhen, china. In: Transportation Research Procedia, World Conference on Transport Research - WCTR 2016 Shanghai, 10-15 July 2016, vol. 25, pp. 985–998 (2017

A MAS-based infrastructure for negotiation and its application to a water-right market

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-013-9443-8This paper presents a MAS-based infrastructure for the specification of a negotiation framework that handles multiple negotiation protocols in a coherent and flexible way. Although it may be used to implement one single type of agreement mechanism, it has been designed in such a way that multiple mechanisms may be available at any given time, to be activated and tailored on demand (on-line) by participating agents. The framework is also generic enough so that new protocols may be easily added. This infrastructure has been successfully used in a case study to implement a simulation tool as a component of a larger framework based on an electronic market of water rights.This paper was partially funded by the Consolider AT project CSD2007-0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation; the MICINN projects TIN2011-27652-C03-01 and TIN2009-13839-C03-01; and the Valencian Prometeo project 2008/051.Alfonso Espinosa, B.; Botti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS. (2014). A MAS-based infrastructure for negotiation and its application to a water-right market. Information Systems Frontiers. 16(2):183-199. https://doi.org/10.1007/s10796-013-9443-8S183199162Alberola, J.M., Such, J.M., Espinosa, A., Botti, V., García-Fornes, A. (2008). Magentix: a multiagent platform integrated in linux. In EUMAS (pp. 1–10).Alfonso, B., Vivancos, E., Botti, V., García-Fornes, A. (2011). Integrating jason in a multi-agent platform with support for interaction protocols. In Proceedings of the compilation of the co-located workshops on AGERE!’11, SPLASH ’11 workshop (pp. 221–226). New York: ACM.Andreu, J., Capilla, J., Sanchis, E. (1996). AQUATOOL, a generalized decision-support system for water-resources planning and operational management. Journal of Hydrology, 177(3–4), 269–291.Bellifemine, F., Caire, G., Greenwood, D. (2007). Developing multi-agent systems with JADE. Wiley.Bordini, R.H., Hübner, J.F., Wooldridge, M. (2007). Programming multi-agent systems in agent speak usign Jason. Wiley.Botti, V., Garrido, A., Gimeno, J.A., Giret, A., Noriega, P. (2011). The role of MAS as a decision support tool in a water-rights market. In AAMAS 2011 workshops, LNAI 7068 (pp. 35–49). Springer.Braubach, L., Pokahr, A., Lamersdorf, W. (2005). Software agent-based applications, platforms and development kits In C.M.K.R. Unland (Ed.), Jadex: a BDI agent system combining middleware and reasoning (Vol. 9, pp. 143–168): Birkhäuser-Verlag.DeSanctis, G.B., & Gallupe, B. (1987). A foundation for the study of group decision support systems. Knowledge based systems, 33(5), 589–609.Eckersley, P. (2003). Virtual markets for virtual goods. Available at http://www.ipria.com/publications/wp/2003/IPRIAWP02.2003.pdf (Accessed April 2012).Fjermestad, J., & Hiltz, S. (2001). Group support systems: a descriptive evaluation of case and field studies. Journal of Management Information Systems, 17(3), 115–161.Fogués, R.L., Alberola, J.M., Such, J.M., Espinosa, A., García-Fornes, A. (2010). Towards dynamic agent interaction support in open multiagent systems. In Proceedings of the 13th international conference of the catalan association for artificial intelligence (Vol. 220, pp. 89–98). IOS Press.Foundation for Intelligent Physical Agents. (2001). FIPA interaction protocol library specification XC00025E. FIPA Consortium.Garrido, A., Arangu, M., Onaindia, E. (2009). A constraint programming formulation for planning: from plan scheduling to plan generatio. Journal of Scheduling, 12(3), 227–256.Giret, A., Garrido, A., Gimeno, J.A., Botti, V., Noriega, P. (2011). A MAS decision support tool for water-right markets. In Proceedings of the tenth international conference on autonomous agents and multiagent systems (Demonstrations@AAMAS) (pp. 1305–1306).Gomez-Limon, J., & Martinez, Y. (2006). Multi-criteria modelling of irrigation water market at basin level: a Spanish case study. European Journal of Operational Research, 173, 313–336.Janjua, N.K., Hussain, F.K., Hussain, O.K. (2013). Semantic information and knowledge integration through argumentative reasoning to support intelligent decision making. Information Systems Frontiers, 15(2), 167–192.jen Hsu, J.Y., Lin, K.-J., Chang, T.-H., ju Ho, C., Huang, H.-S., rong Jih, W. (2006). Parameter learning of personalized trust models in broker-based distributed trust management. Information Systems Frontiers, 8(4), 321–333.Kersten, G., & Lai, H. (2007). 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Engineering Intelligent Systems for Electrical Engineering and Communications, 13(2), 147–154.Sierra, C., Botti, V., Ossowski, S. (2011). Agreement computing. KI - Künstliche Intelligenz, 25(1), 57–61.Thobani, M. (1997). Formal water markets: why, when and how to introduce tradable water rights. The World Bank Research Observer, 12(2), 161–179

mWater prototype #3 analysis and design

In themWater case study prototype #3 it has been used Magentix2 [1, 24, 3, 22, 4, 17] (for more details on Magentix2 see WP7 Deliverables) as the MAS platform for supporting the execution of the MAS system. The platform follows the FIPA standards [14] offering a set of useful mechanisms for the agents to communicate and also tools to allow programming agents in a high level language based on the BDI model. Magentix2 is an open system which facilitates the interaction between heterogeneous agents through FIPA-ACL messages. Also complex interactions can be carried out in a flexible an open way as conversations. The platform offers special structures to allow to use such conversations by considering a set of issues: In each conversation there are always two roles involved: Initiator and Participant. The first is the one who initiates the conversation, and the rest of agents play the Participant role. The conversation can be seen as a direct graph where nodes represent the actions to perform in each step of the conversation and arcs represent the transition between such states. Those steps allow to perform some actions and they can be of different kinds, for example: Begin, Final, Wait, Send, Receive, Action, etc. Conversations have a unique identifier that allows to manage them individually. 1Botti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS.; Noriega, P.; Bexi, A. (2013). mWater prototype #3 analysis and design. http://hdl.handle.net/10251/3212

A holonic multi-agent methodology to design sustainable intelligent manufacturing control systems

[EN] The urgent need for sustainable development is imposing radical changes in the way manufacturing systems are designed and implemented. The overall sustainability in industrial activities of manufacturing companies must be achieved at the same time that they face unprecedented levels of global competition. Therefore, there is a well-known need for tools and methods that can support the design and implementation of these systems in an effective way. This paper proposes an engineering method that helps researchers to design sustainable intelligent manufacturing systems. The approach is focused on the identification of the manufacturing components and the design and integration of sustainability-oriented mechanisms in the system specification, providing specific development guidelines and tools with built-in support for sustainable features. Besides, a set of case studies is presented in order to assess the proposed method.This research was supported by research projects TIN2015-65515-C4-1-R and TIN2016-80856-R from the Spanish government. The authors would like to acknowledge T. Bonte for her contribution to the NetLogo simulator of the AIP PRIMECA cell.Giret Boggino, AS.; Trentesaux, D.; Salido Gregorio, MÁ.; Garcia, E.; Adam, E. (2017). A holonic multi-agent methodology to design sustainable intelligent manufacturing control systems. Journal of Cleaner Production. 167(1):1370-1386. https://doi.org/10.1016/j.jclepro.2017.03.079S13701386167

mWater Prototype 3

This report concerns the application of a regulated open Multi-Agent System (MAS), mWater, that uses intelligent agents to simulate a flexible water-right market. Our simulator focuses on demands and, in particular, on the type of regulatory (in terms of norms selection and agents behaviour), and market mechanisms that foster an efficient use of water while also trying to prevent conflicts among parties. In this scenario, a MAS plays a vital role as it allows us to define different norms, agents behaviour and roles, and assess their impact in the market, thus enhancing the quality and applicability of its results as a decision support tool.Botti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS.; Noriega, P.; Gimeno, J. (2013). mWater Prototype 3. http://hdl.handle.net/10251/3212