Optimal control in dynamic food supply chains using big data

We consider a dynamic food supply chain with multiple process steps where the decisions at each step include supplier selection and settings for their process parameters. We assume that the resulting quality level of the product at every step is stochastic. We also consider that this quality level at each supply chain step has a minimum accepted level and any product that does not meet the minimum accepted level is rejected. We propose an integrated approach that uses Big Data mining techniques to study the effect of these decisions on the quality of the final product and determine our state transition matrix. From there, stochastic optimization methods are implemented to find an optimal policy describing the preferred suppliers and settings for their process parameters. The goal of this policy is to maximize the supply chain's expected profit and reduce rejected product. We show how this technique outperforms traditional techniques in running time as the problem's complexity (state and action space size) grows. We also conduct experiments to explore how the optimal policy changes as the quality threshold for acceptance becomes more restrictive and exhibits a convex behavior. (C) 2020 Elsevier Ltd. All rights reserved.