Walk-In Multi-Stage Patient Flow Scheduling: An ASP Model with DES-Based Evaluation

2026-07-23

AI Summary

This study addresses real-time examination routing for dynamically arriving walk-in patients in multi-department hospitals, subject to medical precedence constraints and room capacity limits. The authors propose a reactive scheduling framework that, upon each patient’s arrival, optimizes only the new patient’s examination sequence and room assignment while keeping existing schedules fixed, minimizing a weighted cost of walking and waiting times. This work presents the first application of Answer Set Programming (ASP) to dynamic patient flow scheduling, leveraging the clingo solver for efficient optimization and integrating Discrete Event Simulation (DES) to evaluate robustness under stochastic service times. Experimental results demonstrate that the approach significantly outperforms greedy baselines across various load and capacity settings, notably reducing median patient sojourn time and increasing the proportion of zero-wait patients, especially under high-load conditions.

Abstract

An effective examination and test schedule for patients plays a crucial role in hospital resource management. In this work, we formulate a new reactive patient-flow scheduling problem in multi-department hospitals where walk-in patients arrive over time and each patient requires multiple examinations per visit. Upon each arrival, the scheduler computes a feasible examination pathway-both the sequence of examinations and the room assignment-for the incoming patient only, while previously scheduled assignments remain fixed. This process is subject to medical precedence constraints and room capacity limitations. We model the problem declaratively in Answer Set Programming (ASP) with clingo, and optimize a two-part cost: travel time between consecutive examination locations and queue-induced waiting time, weighted by the duration of the upcoming examination. To assess robustness under stochastic service times, we propose a Discrete-Event Simulation (DES) evaluation layer and a baseline greedy policy for comparison. On large-scale synthetic datasets across various capacity regimes and patient loads, the ASP approach reduces median stay time and increases the proportion of zero-wait patients compared to DES-based baselines. These improvements are most pronounced under heavy load, while the approach still outperforms baselines across all capacity settings, with smaller gains at higher capacities.

Problem

• patient flow scheduling
• walk-in patients
• multi-stage examinations
• resource constraints
• real-time scheduling

Innovation

• Answer Set Programming
• Discrete-Event Simulation
• Patient Flow Scheduling
• Reactive Scheduling
• Multi-stage Healthcare Workflow