Role-Based Access Control (RBAC) is the standard model for enterprise authorization. However, automated role assignment typically prioritizes administrative efficiency over security. Traditional approaches minimize the number of roles, often producing coarse-grained policies that violate the Principle of Least Privilege by bundling high-risk permissions with common permissions. In this paper, we propose a risk-aware role allocation framework that explicitly accounts for permission sensitivity. We formulate the problem as a submodular cost minimization task and introduce a non-linear penalty for high-risk permissions. To address the resulting NP-hard problem, we develop a scalable greedy role allocation algorithm that exploits submodularity. We also consider budget-constrained scenarios, ensuring effective risk reduction under limited administrative capacity. Experimental results show that our approach scales almost linearly, achieves near-optimal solutions, and effectively isolates high-risk permissions where traditional methods fail.
Healthcare systems face increasing pressure to provide timely access to surgical services while operating under limited physician capacity and uncertain demand. This study proposes a policy-driven expert decision-support framework for coordinated physician assignment in surgical specialty divisions. The framework integrates a two-stage stochastic optimization model with Sample Average Approximation (SAA) to manage demand uncertainty and generate adaptive schedules across interdependent clinic and surgical stages. The model explicitly balances access to clinic, access to surgery (itinerary length), and total access through a policy parameter (β) that allows healthcare administrators to calibrate institutional priorities. By systematically varying β, the framework constructs an efficiency frontier that reveals structural trade-offs between treatment stages in sequential care systems. The methodology is validated using a real-world colorectal surgery case study including 5400 consultations. In the case study, extreme prioritization strategies create downstream bottlenecks, whereas balanced configurations substantially improve overall performance. The optimal policy (β=0.6) achieves a 93.7% reduction in average daily clinic waiting and a 69.0% reduction in average daily surgery waiting over a 28-day evaluation window, compared to the observed schedule. We provide an open access deployable expert decision-support system that enables policy evaluation and performance visualization in a reproducible computational environment. The proposed approach offers healthcare administrators a practical tool for managing access to services under uncertainty.
