Robust Hot Rolling Production Scheduling Under Carbon Tax Regulation

Hot rolling production scheduling (HRPS) is an essential process in modern steel manufacturing. Its effectiveness is influenced by three primary challenges: selecting suitable slabs to maximize efficiency and maintain product quality, adhering to increasingly stringent carbon tax regulations, and managing uncertainties in processing times stemming from fluctuations in rolling speed. This article presents a novel robust HRPS problem under carbon tax regulation (RHRPSP-CTR) that considers slab selection, carbon tax regulation, and uncertain processing times simultaneously for the first time. Based on a budgeted uncertainty set, we develop a robust counterpart model that utilizes a classical dualization scheme and dynamic programming recursive equations to address the challenges associated with evaluating the worst case cost of carbon emissions (CEs) and determining the worst case completion time for each slab caused by processing time uncertainty, respectively. Recognizing the characteristics of slab selection and the high computational complexity in the large-scale RHRPSP-CTR, we propose an adaptive large neighborhood search algorithm incorporating two enhancement strategies: a slab selection rule and a max-min weight update mechanism. Extensive computational experiments demonstrate that the proposed method yields optimal solutions for small-scale problem instances and high-quality, robust solutions for large-scale instances within a relatively short computation time. Moreover, the results indicate that compared with deterministic HRPS schemes, robust HRPS schemes lead to only a slight increase in cost. Notably, a higher carbon tax does not necessarily lead to lower CEs, and a larger uncertainty budget coefficient or uncertainty range does not always result in higher CEs.