发表机构
Indian Institute of Technology Kanpur（坎普尔印度理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对含初诊与转诊患者的医院队列，构建分析决策框架，推导性能指标，设计负载触发控制，验证结果并优化总成本，采用多种启发式求解器求解。
AI 中文摘要
本研究构建了一个综合医院服务的分析与决策框架，该服务包含两类患者：需先诊断后治疗的初诊患者，以及仅需按处方治疗的转诊患者。系统被建模为马尔可夫相位型队列，针对两种治疗模式推导了概率生成函数，针对任意数量n种治疗模式开发了矩阵分析方法，并推导和解释了关键性能指标。设计了一种负载触发控制策略，以确定达到规定利用率所需的最小容量增量。通过数值实验验证了分析结果。此外，总成本问题在倒数服务时间变量下存在严格凸的重构形式，具有唯一全局最小值，同时还使用粒子群优化（PSO）、模拟退火（SA）和正弦余弦算法（SCA）作为独立启发式求解器。
英文摘要
This study develops an analytical and decision framework for a pooled single-server diagnostic--treatment service receiving both first-time and referred patients. A first-time patient receives diagnosis followed immediately by one of $n$ treatment modes, whereas a referred patient enters directly into the prescribed treatment mode. For the two-treatment case, closed-form probability generating functions characterize the stationary queue-length distribution; for arbitrary $n$, a matrix-geometric representation provides a computational solution. The queueing analysis is then directed to capacity control decisions. The effective load is decomposed into mode-specific workload contributions, yielding a minimum service-rate increment for a prescribed utilization target to show that utilization-equivalent interventions need not be delay-equivalent. This motivates a distribution-aware control based on the stationary probability of severe congestion. Finally, the total cost function shown to be strictly convex, providing a global minimum for restricted one-mode interventions. Analytical results are cross-validated against the Pollaczek--Khintchine relations and discrete-event simulation.
