<ul><li>This paper discusses dual sourcing problems with supply mode dependent failure rates, specifically in relation to spare parts for downtime-critical assets.</li><li>The paper explores how dual sourcing strategies using conventional and additive manufacturing techniques can optimize sourcing by addressing variations in part properties and failure rates.</li><li>The study proposes an iterative heuristic and reinforcement learning techniques, along with an endogenous parameterized learning (EPL) approach, to manage the distinct failure characteristics of parts produced by different methods.</li><li>In a stylized setting, the best policy achieves an average optimality gap of 0.4% while in an energy sector case study, the policies outperform the baseline in 91.1% of instances with average cost savings of up to 22.6%.</li></ul>

Solving Dual Sourcing Problems with Supply Mode Dependent Failure Rates

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