Self-Organization through a multi-agent system for orders distribution in large companies
This article presents the development of a multi-agent system in charge of self-managing a delivery system. The article focuses on the delivery management system and not on the movement systems of the different used vehicles.This system consists of different types of vehicles, each with different characteristics, and there may be several instances of each type of vehicle. There will be three operating agents (Drone Operator, Car Operator and Amphibious Operator), an agent that will be responsible for creating random tasks (used only in simulations) and another one that is responsible for distributing these tasks to the operators taking into account the algorithm. This algorithm follows the bases of backtracking and its main function is to assign a task to a vehicle taking into account the distance, the consumption, the limitations of weight and distances, etc. The whole system has been developed in JADE on java. The described software performs a complete simulation with a console in which it is indicated relevant information such as the tasks that are created, the type of vehicle and the instance of that type of vehicle that resolve the delivery, among others. The purpose of this system is to minimize costs and times.
Bechlioulis, C., Rovithakis, G. A., 2016. Decentralized Robust Synchronization of Unknown High Order Nonlinear Multi-Agent Systems with Prescribed Transient and Steady State Performance. Pages 123-134. IEEE - https://doi.org/10.1109/TAC.2016.2535102
Briones, C. D., Machaen, Z. A., Martín, L. M., Torres, G. A., 2018. Self-organizing mobile robots based on multi-agent coordination techniques implemented with aerial vision and communication gateway between Wi-Fi and RF.
Castelfranchis, C., 1997. Modeling social interaction for Al agents. Pages 1567-1576.
Castelfranchis, C., Dignum, F., Jonker, C., Treur, J., 1999. Deliberative normative agents: Principles and architecture. Pages 364-378. Springer, Berlin, Heidelberg. - https://doi.org/10.1007/10719619_27
Chamoso, P., De la Prieta, F., Bajo, J., Corchado, J. M., 2019. Conflict resolution with agents in smart cities. Pages 695-713. IGI Global. - https://doi.org/10.4018/978-1-5225-7030-1.ch031
Corchado, J. M., Bajo, J., De Paz, Y., Tapia, D., 2008. Intelligent environment for monitoring Alzheimer patients, agent technology for health care. Pages 382-396. North-Holland. - https://doi.org/10.1016/j.dss.2007.04.008
Corchado, J. M., Lasa, R., 2003. Constructing deliberative agents with case-based reasoning technolgy. Pages 1227-1241. Wiley Subscription Services, Inc., A Wiley Company. - https://doi.org/10.1002/int.10138
Corchado, J.M., Lees, B., 2001. A hybrid case-based model for forecasting. Pages 106-127- Applies Artificial Intelligence. - https://doi.org/10.1080/088395101750065723
Dimopoulos, Y., Moraitis, P., 2006. Multi-agent coordination and cooperation through classical planning. Pages 398-402. IEEE/WIC/ACM. - https://doi.org/10.1109/IAT.2006.90
Fyfe, C., Corchado, E., González, M., Corchado, J.M., 2002. Analytical Model for Constructing Deliberative Agents. CRL Publishing.
Guo, G., Ding, L., Han, Q., 2014. A distributed event-triggered transmission strategy for sampled-data consensus of multi-agent systems. Pages 1489-1496. Pergamon. - https://doi.org/10.1016/j.automatica.2014.03.017
Jennings, N.R., Wooldridge, M., 1998. A roadmap of agent research and development. Pages 7-38. Springer Netherlands.
Jennings, N.R., 2000. On agent-based software engineering. Pages 117. Elseiver. - https://doi.org/10.1016/S0004-3702(99)00107-1
Jennings, N.R., Wooldridge, M., Kinny, D, 2000. The Gaia methodology for agent-oriented analysis and design. Pages 284-312. Springer Netherlands. - https://doi.org/10.1145/301136.301165
Jennings, N.R., Wooldridge, M., 1994. Agent theories, architectures and languages: a survey. Pages 1-39. Springer, Berlin, Heidelberg. - https://doi.org/10.1007/3-540-58855-8_1
Muñoz, R., Aguirre, R., García-Silvente, M., Gomez, M., 2005. A multi-agent system architecture for mobile robot navigation based on fuzzy and visual behavior. Pages 689-699. - https://doi.org/10.1017/S0263574704001390
Pelrine, R., Hsu, A., Cowan, C., Wong-Foy, A., 2017. Multi-agent systems using diamagnetic micro manipulation - From floating swarms to mobile sensors. IEEE. - https://doi.org/10.1109/MARSS.2017.8001930
Peng, Z., Wen, G., Rahmani, A., 2013. Leader-follower formation control of multiple nonholonomic robots based on backstepping. Pages 211-216. ACM. - https://doi.org/10.1145/2480362.2480408
Román, J. A., Rodríguez, S., 2016. Improvement in the distribution of services in multi-agent systems with SCODA. Pages 31-46, v. 4, n. 3. ADCAIJ - https://doi.org/10.14201/ADCAIJ2015433146
Shouwenaats, T., De Moor, B., Feron, E., How, J., 2001. Mized integer programming for multi-vehicle path planning. Pages 2603-2608. IEEE. - https://doi.org/10.23919/ECC.2001.7076321
Turner, J. R., 2013. Multiagent systems as a team member. Pages 73-90. International Journal of Technology, Knowledge & Society, vol. 9, no. 1. - https://doi.org/10.18848/1832-3669/CGP/v09i01/56355
Wooldridge, M., 1997. Agent-based software engineering. Pages 26-37. IET. - https://doi.org/10.1049/ip-sen:19971026
Wooldridge. M., 2009. An introduction to multiagent systems.
This work is licensed under a Creative Commons Attribution 3.0 License.