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Oil industry value chain simulation with learning agents

2018; Elsevier BV; Volume: 111; Linguagem: Inglês

10.1016/j.compchemeng.2018.01.008

1873-4375

Daniel Barry Fuller, Virgílio José Martins Ferreira Filho, Edilson F. Arruda,

Scheduling and Optimization Algorithms

Simulation is an important tool to evaluate many systems, but it often requires detailed knowledge of each specific system and a long time to generate useful results and insights. A large portion of the required time stems from the need to define operational rules and build valid models that represent them properly. To shorten this model construction time, a learning-agent-based model is proposed. This technique is recommended for cases where optimal policies are not known or hard and costly to unequivocally determine, as it enables the simulation agents to learn good policies "by themselves". A model is built with this technique and a representative case study of oil industry value chain simulation is presented as a proof of concept.