Research on a production plan-driven multi-objective inventory-to-sales ratio optimization model for cigarettes

The cigarette industry faces persistent conflicts between rigid production quotas and volatile market demand, often resulting in suboptimal inventory structures and frequent stockouts or overstocking. To address this, we propose a production plan-driven multi-objective optimization model that explicitly integrates the inventory-to-sales ratio (ISR) as a feasibility constraint rather than an objective. The model simultaneously optimizes inventory turnover, production smoothness, total cost, and service level, subject to capacity, safety-stock, and annual quota constraints. An enhanced NSGA-II is developed, featuring hierarchical constraint handling, equipment-plan coupled encoding, and demand-responsive mutation, to efficiently explore the constrained search space. Simulation experiments on five cigarette brands demonstrate that the improved algorithm outperforms the conventional NSGA-II, reducing IGD by 38.6%, increasing Hypervolume by 56.1%, and lowering Spacing by 80.2%, while ensuring all Pareto solutions are strictly feasible. Three preference schemes—cost-priority, market-priority, and balanced—yield distinct production plans with clear trade-offs between cost and turnover. The results confirm that the proposed framework provides actionable decision support for inventory-production coordination under plan-driven manufacturing environments.