Optimalnightvision.com, MILPs for optimizing night vision systems

Hacker News

Because night vision intensifier tubes are produced with highly variable qualities, they are binned and sold in discrete grade batches. This wastes money and degrades overall system performance. With two mixed-integer linear programs (one for grouping tubes into systems, and another for assigning systems to customers), OptimalNightVision is able to greatly improve end-product quality and reduce costs. OptimalNightVision is the only (publicly available) inventory optimization platform for night vision intensifier tubes. Tech: Django, Pyomo for formulation, GLPK for solving. Feedback and questions are very welcome :)

Visit product View on Hacker NewsJan 13, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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