SMOP: Self-supervised Multi-period Optimization Proxy for Resource Allocation with Temporal Constraints

Multi-period resource allocation problems are typically solved using optimization tools, but solving optimization from scratch can be computationally expensive for large-scale online decision-making. Recent neural-network-based optimization proxies offer substantial speedups, yet they often struggle to preserve feasibility over an entire horizon when global resource-balance constraints interact with inter-temporal constraints: a correction made at the current period may destroy future feasibility. To address this challenge, we exploit the underlying resource–temporal structure and develop a Self-supervised Multi-period Optimization Proxy (SMOP) with three main components: backward–forward feasible-interval propagation, neural-network-based proportional prediction with projection-based feasibility repair, and future-aware forward recovery. We establish full-horizon feasibility guarantees while reducing the nominal computational complexity from cubic to linear in problem size compared with the dense KKT-based differentiable optimization. Experiments on fixed commitment economic dispatch demonstrate that the proposed method achieves millisecond-level inference and produces solutions with zero constraint violations. Across most test scenarios, the resulting feasible solutions also exhibit less than 0.5% optimality gaps without using labels, demonstrating that SMOP can achieve both high computational efficiency and strong solution quality.