Summary of Solving Dual Sourcing Problems with Supply Mode Dependent Failure Rates, by Fabian Akkerman et al.
Solving Dual Sourcing Problems with Supply Mode Dependent Failure Rates
by Fabian Akkerman, Nils Knofius, Matthieu van der Heijden, Martijn Mes
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates dual sourcing problems with supply mode dependent failure rates, which is relevant for managing spare parts for downtime-critical assets. To enhance resilience, businesses adopt dual sourcing strategies using both conventional and additive manufacturing techniques. The paper explores how these strategies can optimize sourcing by addressing variations in part properties and failure rates. A significant challenge is the distinct failure characteristics of parts produced by different methods, which influence future demand. The authors propose a new iterative heuristic and several reinforcement learning techniques combined with an endogenous parameterized learning (EPL) approach to tackle this challenge. The EPL approach allows a single policy to handle various input parameters for multiple items. In a stylized setting, the best policy achieves an average optimality gap of 0.4%. In a case study within the energy sector, the policies outperform the baseline in 91.1% of instances, yielding average cost savings up to 22.6%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how businesses can optimize their spare parts management by using dual sourcing strategies that combine conventional and additive manufacturing techniques. The authors propose a new approach that uses reinforcement learning and machine learning to improve the efficiency of spare part supply chains. The approach helps to address the challenge of managing spare parts with different failure rates, which is important for downtime-critical assets. In a case study, the authors show that their approach can reduce costs by up to 22.6%. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
