Robust Hot Rolling Production Scheduling Under Carbon Tax Regulation.

Area of Science:

• Operations Research
• Manufacturing Engineering
• Environmental Management

Background:

• Hot rolling production scheduling (HRPS) is critical in steel manufacturing.
• Key challenges include slab selection, carbon tax compliance, and processing time uncertainty.
• Existing methods struggle to integrate these factors effectively.

Purpose of the Study:

• To develop a novel robust HRPS model (RHRPSP-CTR) integrating slab selection, carbon tax, and uncertain processing times.
• To address the complexities of worst-case carbon emissions and completion times under uncertainty.
• To propose an efficient algorithm for large-scale RHRPSP-CTR instances.

Main Methods:

• Developed a robust counterpart model using a budgeted uncertainty set and dualization.
• Employed dynamic programming for worst-case carbon emissions and completion time evaluation.
• Designed an adaptive large neighborhood search algorithm with slab selection and weight update strategies.

Main Results:

• The proposed method achieves optimal solutions for small instances and high-quality, robust solutions for large instances quickly.
• Robust HRPS schemes result in only a marginal cost increase compared to deterministic methods.
• Higher carbon taxes do not guarantee lower carbon emissions; uncertainty parameters do not always increase emissions.

Conclusions:

• The RHRPSP-CTR model effectively balances production efficiency, carbon tax regulations, and processing time uncertainties.
• The adaptive large neighborhood search algorithm offers an efficient solution for complex, large-scale scheduling problems.
• The findings provide valuable insights for optimizing steel manufacturing under environmental and operational constraints.