Dynamic Heterogeneous-State Guided Exact Search for Multi-Paper Reviewer Allocation

Reviewer allocation requires assigning reviewer groups to papers while satisfying expertise and operational constraints. When multiple papers are assigned simultaneously, reviewer-load constraints couple their assignments, and optimizing aggregate reviewer–paper affinity may leave some papers with substantially lower expertise coverage. We therefore formulate the Multi-Paper Reviewer Allocation (MPRA) problem, which assigns a fixed-size reviewer group to each paper under keyword-relevance, reviewer-load, and group-level social-distance constraints, with the objective of maximizing the minimum keyword coverage over all papers. We prove that MPRA is NP-hard and propose Dynamic Heterogeneous-State Guided Exact Search (HGES), which combines rareness-aware greedy initialization with exact branch-and-bound search. HGES derives paper demand, reviewer availability, keyword rareness, and social feasibility from each partial allocation to guide paper expansion and reviewer-group enumeration, while admissible upper-bound pruning preserves optimality. We further develop an Index-Enhanced Search Acceleration (IESA) module that uses keyword inverted lists, paper-level candidate precomputation, and $k$-hop neighborhood indexing to reduce repeated search operations. On academic-network and attributed social-network instances, exact methods obtain matching average objectives on jointly solved instances. HGES+IESA reduces reported runtime relative to unguided branch-and-bound, while dynamic-ordering ablations show that fewer expanded nodes need not yield shorter runtime. General-purpose mixed-integer optimization remains faster in several settings.