A Multi-Agent Approach Towards Collaborative Supply Chain Management

Supply chain collaboration has become a critical success factor for supply chain management and effectively improves the performance of organizations in various industries. Supply chain collaboration builds on information sharing, collaborative planning and execution. Information technology is an important enabler of collaborative supply chain management. Many information systems have been developed for supply chain management from legacy systems and enterprise resource planning (ERP) into the newly developed advanced planning and scheduling system (APS) and e-commerce solutions. However, these systems do not provide sufficient support to achieve collaborative supply chain. Recently, intelligent agent technology and multi-agent system (MAS) have received a great potential in supporting transparency in information flows of business networks and modeling of the dynamic supply chain for collaborative supply chain planning and execution. This paper explores the similarities between multi-agent system and supply chain system to justify the use of multi-agent technology as an appropriate approach to support supply chain collaboration. In addition, the framework of the multi-agent-based collaborative supply chain management system will be presented

Method of supply chain optimization in E-commerce

Rapid development of technologies and their penetration into all sectors generates a wide range of streamlining of production and trade processes. Electronic commerce is the area in which information and communication technology (ICT) is an essential and indispensable element. It is based on the use of e-commerce systems. An e-commerce system combines several parts consisting of customers, suppliers (sellers, dealers, producers, businessmen, etc.), the web server (web interface), the information system (ERP, CRM, the database system), the payment system, the dispatch system and the legislature itself. All these subsystems must be managed both at the operational level and in terms of the whole e-commerce system. E-commerce systems are tools meant to support the supply chain (SC), the quality of which as well as other parts of the e-commerce system largely depend on management processes representing supply chain management (SCM). The optimal way to ensure the success of SCM is to use the methods of modelling and simulation based on appropriate models and mathematical representation of a real SC. Such models are constructed with the use of process and value-chain oriented approaches or based on the concept of multi-agent systems. Different types of models in conjunction with a suitable mathematical representation allow us to perform the simulation process which outputs can help managers make suitable decisions. The paper aims at presenting contemporary approaches to the supply chain modelling within e-commerce systems. Moreover, the case study emphasized hereby is oriented to present the sample simulation approach in order to find the optimal allocation of resources which are meant to minimize shipping costs.e-commerce system, supply chain, supply chain management, warehouse, allocation of resources

Multi-Agent Systems

[EN] With the current advance of technology, agent-based applications are becoming a standard in a great variety of domains such as e-commerce, logistics, supply chain management, telecommunications, healthcare, and manufacturing. Another reason for the widespread interest in multi-agent systems is that these systems are seen as a technology and a tool that helps in the analysis and development of new models and theories in large-scale distributed systems or in human-centered systems. This last aspect is currently of great interest due to the need for democratization in the use of technology that allows people without technical preparation to interact with the devices in a simple and coherent way. In this Special Issue, different interesting approaches that advance this research discipline have been selected and presented.Julian Inglada, VJ.; Botti V. (2019). Multi-Agent Systems. Applied Sciences. 9(7):1-7. https://doi.org/10.3390/app9071402S1797Kravari, K., & Bassiliades, N. (2015). A Survey of Agent Platforms. Journal of Artificial Societies and Social Simulation, 18(1). doi:10.18564/jasss.2661Baldoni, M., Baroglio, C., May, K., Micalizio, R., & Tedeschi, S. (2018). Computational Accountability in MAS Organizations with ADOPT. Applied Sciences, 8(4), 489. doi:10.3390/app8040489Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., & Santi, A. (2013). Multi-agent oriented programming with JaCaMo. Science of Computer Programming, 78(6), 747-761. doi:10.1016/j.scico.2011.10.004Challenger, M., Tezel, B., Alaca, O., Tekinerdogan, B., & Kardas, G. (2018). Development of Semantic Web-Enabled BDI Multi-Agent Systems Using SEA_ML: An Electronic Bartering Case Study. Applied Sciences, 8(5), 688. doi:10.3390/app8050688Challenger, M., Demirkol, S., Getir, S., Mernik, M., Kardas, G., & Kosar, T. (2014). On the use of a domain-specific modeling language in the development of multiagent systems. Engineering Applications of Artificial Intelligence, 28, 111-141. doi:10.1016/j.engappai.2013.11.012Boztepe, İ., & Erdur, R. (2018). Linked Data Aware Agent Development Framework for Mobile Devices. Applied Sciences, 8(10), 1831. doi:10.3390/app8101831Shoham, Y., Powers, R., & Grenager, T. (2007). If multi-agent learning is the answer, what is the question? Artificial Intelligence, 171(7), 365-377. doi:10.1016/j.artint.2006.02.006Duan, K., Fong, S., Zhuang, Y., & Song, W. (2018). Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms. Applied Sciences, 8(6), 862. doi:10.3390/app8060862Zhang, Q., Yao, J., Yin, Q., & Zha, Y. (2018). Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution. Applied Sciences, 8(7), 1077. doi:10.3390/app8071077Cook, D. J., Augusto, J. C., & Jakkula, V. R. (2009). Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing, 5(4), 277-298. doi:10.1016/j.pmcj.2009.04.001Kranz, M., Holleis, P., & Schmidt, A. (2010). Embedded Interaction: Interacting with the Internet of Things. IEEE Internet Computing, 14(2), 46-53. doi:10.1109/mic.2009.141Gershenfeld, N., Krikorian, R., & Cohen, D. (2004). The Internet of Things. Scientific American, 291(4), 76-81. doi:10.1038/scientificamerican1004-76Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010Costa, A., Novais, P., Corchado, J. M., & Neves, J. (2011). Increased performance and better patient attendance in an hospital with the use of smart agendas. Logic Journal of IGPL, 20(4), 689-698. doi:10.1093/jigpal/jzr021Tapia, D. I., & Corchado, J. M. (2009). An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care. International Journal of Ambient Computing and Intelligence, 1(1), 15-26. doi:10.4018/jaci.2009010102Barriuso, A., De la Prieta, F., Villarrubia González, G., De La Iglesia, D., & Lozano, Á. (2018). MOVICLOUD: Agent-Based 3D Platform for the Labor Integration of Disabled People. Applied Sciences, 8(3), 337. doi:10.3390/app8030337Rosales, R., Castañón-Puga, M., Lara-Rosano, F., Flores-Parra, J., Evans, R., Osuna-Millan, N., & Gaxiola-Pacheco, C. (2018). Modelling the Interaction Levels in HCI Using an Intelligent Hybrid System with Interactive Agents: A Case Study of an Interactive Museum Exhibition Module in Mexico. Applied Sciences, 8(3), 446. doi:10.3390/app8030446Ramos, J., Oliveira, T., Satoh, K., Neves, J., & Novais, P. (2018). Cognitive Assistants—An Analysis and Future Trends Based on Speculative Default Reasoning. Applied Sciences, 8(5), 742. doi:10.3390/app8050742SATOH, K. (2005). Speculative Computation and Abduction for an Autonomous Agent. IEICE Transactions on Information and Systems, E88-D(9), 2031-2038. doi:10.1093/ietisy/e88-d.9.2031Miyashita, K. (2017). Incremental Design of Perishable Goods Markets through Multi-Agent Simulations. Applied Sciences, 7(12), 1300. doi:10.3390/app7121300Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21. doi:10.1080/10630732.2014.942092Roscia, M., Longo, M., & Lazaroiu, G. C. (2013). Smart City by multi-agent systems. 2013 International Conference on Renewable Energy Research and Applications (ICRERA). doi:10.1109/icrera.2013.6749783Lozano, Á., De Paz, J., Villarrubia González, G., Iglesia, D., & Bajo, J. (2018). Multi-Agent System for Demand Prediction and Trip Visualization in Bike Sharing Systems. Applied Sciences, 8(1), 67. doi:10.3390/app8010067Jordán, J., Palanca, J., del Val, E., Julian, V., & Botti, V. (2018). A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations. Applied Sciences, 8(2), 313. doi:10.3390/app8020313Billhardt, H., Fernández, A., Lujak, M., & Ossowski, S. (2018). Agreement Technologies for Coordination in Smart Cities. Applied Sciences, 8(5), 816. doi:10.3390/app805081

Method of supply chain optimization in E-commerce

Rapid development of technologies and their penetration into all sectors generates a wide range of streamlining of production and trade processes. Electronic commerce is the area in which information and communication technology (ICT) is an essential and indispensable element. It is based on the use of e-commerce systems. An e-commerce system combines several parts consisting of customers, suppliers (sellers, dealers, producers, businessmen, etc.), the web server (web interface), the information system (ERP, CRM, the database system), the payment system, the dispatch system and the legislature itself. All these subsystems must be managed both at the operational level and in terms of the whole e-commerce system. E-commerce systems are tools meant to support the supply chain (SC), the quality of which as well as other parts of the e-commerce system largely depend on management processes representing supply chain management (SCM). The optimal way to ensure the success of SCM is to use the methods of modelling and simulation based on appropriate models and mathematical representation of a real SC. Such models are constructed with the use of process and value-chain oriented approaches or based on the concept of multi-agent systems. Different types of models in conjunction with a suitable mathematical representation allow us to perform the simulation process which outputs can help managers make suitable decisions. The paper aims at presenting contemporary approaches to the supply chain modelling within e-commerce systems. Moreover, the case study emphasized hereby is oriented to present the sample simulation approach in order to find the optimal allocation of resources which are meant to minimize shipping costs

Evaluating how agent methodologies support the specification of the normative environment through the development process

[EN] Due to the increase in collaborative work and the decentralization of processes in many domains, there is an expanding demand for large-scale, flexible and adaptive software systems to support the interactions of people and institutions distributed in heterogeneous environments. Commonly, these software applications should follow specific regulations meaning the actors using them are bound by rights, duties and restrictions. Since this normative environment determines the final design of the software system, it should be considered as an important issue during the design of the system. Some agent-oriented software engineering methodologies deal with the development of normative systems (systems that have a normative environment) by integrating the analysis of the normative environment of a system in the development process. This paper analyses to what extent these methodologies support the analysis and formalisation of the normative environment and highlights some open issues of the topic.This work is partially supported by the PROMETEOII/2013/019, TIN2012-36586-C03-01, FP7-29493, TIN2011-27652-C03-00, CSD2007-00022 projects, and the CASES project within the 7th European Community Framework Program under the grant agreement No 294931.Garcia Marques, ME.; Miles, S.; Luck, M.; Giret Boggino, AS. (2014). Evaluating how agent methodologies support the specification of the normative environment through the development process. Autonomous Agents and Multi-Agent Systems. 1-20. https://doi.org/10.1007/s10458-014-9275-zS120Cossentino, M., Hilaire, V., Molesini, A., & Seidita, V. (Eds.). (2014). Handbook on agent-oriented design processes (Vol. VIII, 569 p. 508 illus.). Berlin: Springer.Akbari, O. (2010). A survey of agent-oriented software engineering paradigm: Towards its industrial acceptance. 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Verhagen (Eds.), Normative multi-agent systems, number 09121 in Dagstuhl seminar proceedings.Boella, G., Torre, L., & Verhagen, H. (2006). Introduction to normative multiagent systems. Computational and Mathematical Organization Theory, 12(2–3), 71–79.Bogdanovych, A., Esteva, M., Simoff, S., Sierra, C., & Berger, H. (2008). A methodology for developing multiagent systems as 3d electronic institutions. In M. Luck & L. Padgham (Eds.), Agent-Oriented Software Engineering VIII (Vol. 4951, pp. 103–117). Lecture Notes in Computer Science. Berlin: Springer.Boissier, O., Padget, J., Dignum, V., Lindemann, G., Matson, E., Ossowski, S., Sichman, J., & Vazquez-Salceda, J. (2006). Coordination, organizations, institutions and norms in multi-agent systems. LNCS (LNAI) (Vol. 3913).Bordini, R. H., Fisher, M., Visser, W., & Wooldridge, M. (2006). Verifying multi-agent programs by model checking. In Autonomous agents and multi-agent systems (Vol. 12, pp. 239–256). 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A multi-agent architecture for electronic payment

The Internet has brought about innumerable changes to the way enterprises do business. An essential problem to be solved before the widespread commercial use of the Internet is to provide a trustworthy solution for electronic payment. We propose a multi-agent mediated electronic payment architecture in this paper. It is aimed at providing an agent-based approach to accommodate multiple e-payment schemes. Through a layered design of the payment structure and a well-defined uniform payment interface, the architecture shows good scalability. When a new e-payment scheme or implementation is available, it can be plugged into the framework easily. In addition, we construct a framework allowing multiple agents to work cooperatively to realize automation of electronic payment. A prototype has been built to illustrate the functionality of this design. Finally we discuss the security issues

A modularized electronic payment system for agent-based e-commerce

With the explosive growth of the Internet, electronic-commerce (e-commerce) is an increasingly important segment of commercial activities on the web. The Secure Agent Fabrication, Evolution & Roaming (SAFER) architecture was proposed to further facilitate e-commerce using agent technology. In this paper, the electronic payment aspect of SAFER will be explored. The Secure Electronic Transaction (SET) protocol and E-Cash were selected as the bases for the electronic payment system implementation. The various modules of the payment system and how they interface with each other are shown. An extensible implementation done using JavaTM will also be elaborated. This application incorporates agent roaming functionality and the ability to conduct e-commerce transactions and carry out intelligent e-payment procedures

SOLACE: A framework for electronic negotiations

Copyright @ 2011 Walter de Gruyter GmbHMost existing frameworks for electronic negotiations today are tied to specific negotiation systems for which they were developed, preventing them from being applied to other negotiation scenarios. Thus, the evaluation of electronic negotiation systems is difficult as each one is based on a different framework. Additionally, each developer has to design a new framework for any system to be developed, leading to a ‘reinvention of the wheel’. This paper presents SOLACE—a generic framework for multi-issue negotiations, which can be applied to a variety of negotiation scenarios. In contrast with other frameworks for electronic negotiations, SOLACE supports hybrid systems in which the negotiation participants can be humans, agents or a combination of the two. By recognizing the importance of strategies in negotiations and incorporating a time attribute in negotiation proposals, SOLACE enhances existing approaches and provides a foundation for the flexible electronic negotiation systems of the future