Nested Partitions Method for Global Optimization
Operations Research
Simulation optimization: simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Manufacturing 2: shop scheduling using Tabu search and simulation
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Maufacturing supply chain applications 1: supply chain multi-objective simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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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.