Toward data-driven modeling of material flow simulation: automatic parameter calibration of multiple agents from sparse production log

Abstract

Modeling accurate material flow simulation is a time-consuming task and requires high expertise about both simulation techniques and production system. Recently, data-driven modeling approaches that build simulation models from production log are gathering attentions to automate the modeling process. However, in most practical cases, production log does not have enough resolution to specify the input and output of each agent in material flow simulation such as processing time agent and dispatching agent. For the issue, we proposed a novel approach and method that models multiple agents simultaneously from sparse production log. In our method, agents are described as machine learning models, then parameters in the models are calibrated to minimize simulation error. We confirmed the usefulness of the proposed method through experiments with virtual production system.