Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling

  • Laura Tosselli Dpto. Ingeniería en Sistemas de Información – UTN FRVM, Villa María, X5900 HLR, Córdoba, Argentina
  • Verónica Bogado CIT Villa María (CONICET-UNVM),Carlos Pellegrini 211, Villa María, Córdoba, Argentina
  • Ernesto Martínez INGAR (CONICET – UTN) – UTN FRSF, Santa Fe, S3002GJC, Santa Fe, Argentina
Keywords: Agent-based simulation, Multi-agent System, Multi-project (Re)scheduling, Project oriented Fractal Organization, Resource Leveling


In a multi-project context within enterprise networks, reaching feasible solutions to the (re)scheduling problem represents a major challenge, mainly when scarce resources are shared among projects. The multi-project (re)scheduling must achieve the most efficient possible resource usage without increasing the prescribed project constraints, considering the Resource Leveling Problem (RLP), whose objective is to level the consumption of resources shared in order to minimize their idle times and to avoid overallocation conflicts. In this work, a multi-agent solution that allows solving the Resource Constrained Multi-project Scheduling Problem (RCMPSP) and the Resource Investment Problem is extended to incorporate indicators on agents’ payoff functions to address the Resource Leveling Problem in a decentralized and autonomous way, through decoupled rules based on Trial-and-Error approach. The proposed agent-based simulation model is tested through a set of project instances that vary in their structure, parameters, number of resources shared, etc. Results obtained are assessed through different scheduling goals, such as project total duration, project total cost and leveling resource usage. Our results are far better compared to the ones obtained with alternative approaches. This proposal shows that the interacting agents that implement decoupled learning rules find a solution which can be understood as a Nash equilibrium.


Download data is not yet available.


[1] M. Garey, D. Johnson, Computers and Intractability: A Guide to the Theory of NP Completeness, New York, USA: W. H. Freeman, 1979.
[2] Project Management Institute, Guía de los Fundamentos para la Dirección de Proyectos (Guía del PMBOK), 5ª ed., Project Management Institute, Pensilvania, USA, 2013.
[3] J.L. Ponz-Tienda, V. Yepes, E. Pellicer and J. Moreno-Flores, “The Resource Leveling Problem with multiple resources using an adaptive genetic algorithm”, Automation in Construction, vol. 29, pp. 161–172, 2013.
[4] M. Vanhoucke, Integrated Project Management Sourcebook – A Technical Guide to Project Scheduling, Risk and Control, Springer, 2016.
[5] M.Canavesio and E. Martinez, “Enterprise modeling of a project-oriented fractal company for SMEs networking”, Computers in Industry, vol. 54, pp. 794–813, 2007.
[6] L. Tosselli, V. Bogado and E. Martínez, “An agent-based simulation model using decoupled learning rules to (re)schedule multiple projects”, in XXIII Congreso Argentino de Ciencias de la Computación, pp. 33-42, 2017.
[7] J. Homberger, “A (μ, λ)-coordination mechanism for agent-based multi-project scheduling”, OR Spectrum, vol. 34, pp. 107-132, 2009.
[8] A. Shahsavar, A. Najafi and S.T.A. Niaki, “Three self-adaptive multi-objective evolutionary algorithms for a triple-objective project scheduling problem”, Computers & Industrial Engineering, Elsevier, vol. 87, pp. 4-15, 2015.
[9] X. Shen, L. Minku, R. Bahsoon and X. Yao.“Dynamic Software Project Scheduling through a Proactive-Rescheduling Method”, IEEE Transactions on Software Engineering, vol. 42,no. 7, pp. 658–686, 2016.
[10]S. Adhau, M. Mittal and A. Mittal, “A multiagent system for distributed multi-project scheduling: An auction-based negotiation approach”, Eng. Applications of Artificial Intelligence, vol. 25, pp. 1738–1751, 2012.
[11]S. Adhau, M. Mittal and A. Mittal, “A multiagent system for decentralized multi-project scheduling with resource transfers”, Int. J. Prod. Economics, vol. 146, pp. 646–661, 2013.
[12] T. Wauters, K. Verbeeck, G. Vanden Berghe and P. De Causmaecker “A Multi-Agent Learning for the Multi-Mode Resource- Constrained Project Scheduling Problem”, Proc. of 8th Int. Conf. on Autonomous Agents andMultiagent Systems, pp. 1-8, 2009.
[13] J. Araúzo, J. Pajares and A. López-Paredes, “Simulating the dynamic scheduling of Project portfolios”, Simulation Modelling Practice and Theory, vol. 18, pp. 1428-1441, 2010.
[14]Chaos Report. Standish Group. Available at on 2017-07-07.
[15]Y. Shoham and K. Leyton-Brown, Multiagent Systems-Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press, 2009.
[16] H.P. Young, “Learning by trial and error”, Games and Economic Behavior, vol. 65, pp. 626-643, 2009.
[17]S. Railsback and V. Grimm, Agent-Based and Individual-Based Modeling: A practical Introduction, Princeton University Press, 2012.
[18]L. Tosselli, V. Bogado and E. Martínez, “Un Enfoque de Sistemas Multiagente para la Gestión Ágil de Riesgos en la Compañía Fractal Mediante la (Re) Planificación de Proyectos”, in Conaiisi 2015, Bs. As., Argentina, 2015.
How to Cite
Tosselli, L., Bogado, V., & Martínez, E. (2018). Multi-agent Learning by Trial and Error for Resource Leveling during Multi-Project (Re)scheduling. Journal of Computer Science and Technology, 18(02), e14.
Original Articles