Cost-Ecient Sequential Predictions with a Hybrid Method of Topological Sorting and Boosting: A Case Study on LDPE-Property Prediction

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Abstract
Determining low-density polyethylene (LDPE) properties typically requires extensive laboratory testing, which is time-consuming and costly. For instance, the conditioning phase alone for measuring Vicat softening temperature requires a minimum of 40 hours [1]. Predictive modeling can reduce these costs. Ensuring accuracy that meets manufacturing standards, however, is challenging. This paper introduces TopSABoost, a hybrid method that combines topological sorting and boosting techniques to perform sequential predictions of LDPE properties and minimize the overall laboratory-testing cost. This approach reduces laboratory testing costs by predicting one property first and using it to predict another. The complexity analysis demonstrates that the proposed algorithm is ideal for non-real-time determination of sequential predictions, as it computes the model offline once for repeated use without requiring recalculations, aligning with manufacturing needs. The experimental results demonstrate that TopSABoost achieves a maximum error of just 0.11%, satisfying strict manufacturing constraints. TopSABoost identifies that prioritizing the prediction of L-value, followed by Density, offers the most cost-efficient sequence and significantly reduces reliance on direct laboratory testing while maintaining adherence to error thresholds.

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