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Operations Optimization in Supply Chain Systems using Simulation and Reinforcement Learning

Mahmoudi, Farzaneh | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56315 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Hassan Nayebi, Erfan
  7. Abstract:
  8. The inventory costs constitute a significant portion of the supply chain costs. Therefore, choosing an optimal inventory policy for orders is of great importance. The aim of this research is to find the optimal inventory policy for a distribution center in a three-tier supply chain consisting of a manufacturer, a distribution center, and a retailer. This research simulates a supply chain in agent-based framework and optimizes it using reinforcement learning. The optimization KPI in this research is the mean daily cost of the supply chain. Finally, the result obtained from reinforcement learning is compared with the optimized result of AnyLogic and the mean daily cost in the model optimized with reinforcement learning has been 1.4477% lower. Additionally, three scenarios are designed to further examine the model. In the first and second scenarios, the reorder point and order up to level of the manufacturer and retailer are also optimized. Taking into account the second scenario, the mean daily cost has decreased by 1.0923% compared to the initial problem. In the third scenario, uncertainty in the supply chain has increased. With increased uncertainty, the mean daily cost is 3.1986% lower with reinforcement learning compared to AnyLogic optimization. Considering that this improvement is greater than the initial improvement (1.4477%), it can be concluded that reinforcement learning has high capability in uncertain conditions
  9. Keywords:
  10. Goods Supply Chain Management ; Inventory Management ; Reinforcement Learning ; Agent Based Simulation ; Proximal Policy Optimization Algorithm

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