Multi-Dimensional Expert Matching in Enterprise Technical Routing Systems: A Weighted Graph-Based Approach to Intelligent Resource Allocation

Abstract

Introduction: The allocation of specialized technical expertise to enterprise service requests constitutes a combinatorial optimization problem of considerable practical significance, yet existing systems overwhelmingly rely on rudimentary heuristics keyword matching, round-robin distribution, or manual triage that disregard the multidimensional nature of expert competency, geographic constraints, linguistic requirements, and stochastic workload fluctuations.

Objectives: This article formalizes the enterprise expert-matching problem as a weighted bipartite graph optimization over heterogeneous attribute spaces, introducing a unified scoring framework that jointly optimizes across six orthogonal matching dimensions.

Methods: This article introduces the Composite Affinity Score(CAS), a parametric objective function that integrates skill-domain alignment, Technical Field Community (TFC) membership hierarchies, sales territory congruence, language capability vectors, resolver availability indices, and customer-value priority weights. This article presents MDEM (Multi-Dimensional Expert Matching), a greedy approximation algorithm with provable (1 − 1/e)-competitive ratio guarantees under submodular objective conditions, achieving sub-second assignment latency for annual request volumes exceeding 160,000.

Results: Empirical evaluation on seven years of anonymized operational data from a global cloud infrastructure provider (N = 1,043,217 requests, K = 12,847 unique resolvers) demonstrates that MDEM reduces mean time-to-resolution (MTTR) by 34.7% relative to manual routing (ρ < 0.001) and 21.9% relative to single-dimension automated baselines while maintaining resolver workload Gini coefficients below 0.12.

Conclusions: The framework's generalizability is validated through cross-domain transfer experiments in healthcare specialist referral and legal expertise allocation, yielding MTTR reductions of 28.3% and 19.6%, respectively, confirming that the six-dimensional MDEM structure transfers effectively beyond its originating cloud services context.