Zhengyang Cheng

Runhao Liu, You Li, Zhengyang Cheng et al.

Long-horizon agricultural planning requires optimizing crop allocation under complex spatial heterogeneity, temporal agronomic dependencies, and multi-source environmental uncertainty. Existing approaches often either address crop interactions, such as legume-cereal complementarity, only implicitly or rely on static deterministic formulations that fail to ensure resilience against market and climate volatility.To address these challenges, we propose a Multi-Layer Robust Crop Planning Framework (MLRCPF) that integrates spatial reasoning, temporal dynamics, and robust optimization. Specifically, we formalize crop-to-crop relationships through a structured interaction matrix embedded within the state-transition logic, and employ a distributionally robust optimization layer to mitigate worst-case risks defined by a data-driven ambiguity set. Evaluations on a real-world high-mix farming dataset from North China demonstrate the effectiveness of the proposed approach. The framework autonomously generates sustainable checkerboard rotation patterns that restore soil fertility, significantly increasing the legume planting ratio compared to deterministic baselines. Economically, it successfully resolves the trade-off between optimality and stability. These results highlight the importance of explicitly encoding domain-specific structural priors into optimization models for resilient decision-making in complex agricultural systems.

Runhao Liu, Zhengyang Cheng, Fei Ding et al.

High-mix manufacturing systems require production plans that are both profitable and refinable into executable machine-level schedules under heterogeneous resources, mold-dependent compatibility, setup losses,delivery windows, and accessory synchronization. We study this problem as a production-envelope refinement task. A rolling-horizon mixed-integer linear programming (MILP) planner generates a valid production envelope that fixes daily production, fulfillment, mold states, inventory flows, outsourcing, and unmet-demand variables. A structure-aware constructive scheduler then refines this envelope into concrete order-machine allocations while preserving capacity feasibility, product-mold-machine compatibility, and delivery-window compliance. The scheduler enforces a one-mold-per-machine-per-day stability rule to avoid intra-day mold fragmentation. We establish residual invariants and prove a soundness theorem: whenever refinement terminates with zero residual fulfillment, the returned allocation is executable with respect to the valid envelope. The framework is implemented as an Advanced Planning and Scheduling (APS) prototype and evaluated on a real industrial case from a Jiangsu smartphone-case manufacturer in China with 37 product types, 150 orders, and over 8.3 million requested units. The proposed stable refinement achieves 100% on-time delivery, eliminates outsourcing, and bounds changeover-driven capacity loss to 1.9-4.6%. Across nine demand and changeover perturbation scenarios, it maintains robust delivery performance, showing that sound envelope refinement is a practical mechanism for reliable manufacturing scheduling.