Two-stage Wasserstein metric-based distributionally robust scheduling for ethylene plants under price uncertainty

Abstract

An ethylene cracking system comprises multiple parallel furnaces operating in inherent cracking–decoking cycles, making the scheduling of cyclic furnace operations a key determinant of plant profitability. Meanwhile, exogenous fluctuations in product prices driven by market supply–demand conditions and production costs significantly affect the economic performance of scheduling decisions. Therefore, this paper develops a new two-stage Wasserstein distributionally robust scheduling framework. The Wasserstein metric is employed to construct the data-driven ambiguity set that describes all possible probability distributions of uncertainty. Afterwards, the kernel density estimation algorithm is utilized to design the data-driven support set, further mitigating the adverse effects of outliers. By introducing the ambiguity set and support set, a tractable mixed-integer nonlinear programming model is re-derived based on the duality theory. A practical case study shows that the proposed method can effectively hedge against price uncertainty, and the obtained robust scheduling yields the daily profit of $8.639\times 10^6$ RMB/day.