Supply chain planning: a reinforcement learning approach to production planning in the fabrication/fulfillment manufacturing process

We have used Reinforcement Learning together with Monte Carlo simulation to solve a multi-period production planning problem in a two-stage hybrid manufacturing process (a combination of build-to-plan with build-to-order) with a capacity constraint. Our model minimizes inventory and penalty costs while considering real-world complexities such as different component types sharing the same manufacturing capacity, multi-end-products sharing common components, multi-echelon bill-of-material (BOM), random lead times, etc. To efficiently search in the huge solution space, we designed a two-phase learning scheme where "good" capacity usage ratios are first found for different decision epochs, based on which a detailed production schedule is further improved through learning to minimize costs. We will illustrate our approach through an example and conclude the paper with a discussion of future research directions.