An intelligent real-time scheduler for out-patient clinics: A multi-agent system model

Jyoti R. Munavalli*, Shyam Vasudeva Rao, Aravind Srinivasan, G. G. van Merode

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Scheduling of resources and patients are crucial in outpatient clinics, particularly when the patient demand is high and patient arrivals are random. Generally, outpatient clinic systems are push systems where scheduling is based on average demand prediction and is considered for long term (monthly or bimonthly). Often, planning and actual scenario vary due to uncertainty and variability in demand and this mismatch results in prolonged waiting times and under-utilization of resources. In this article, we model an outpatient clinics as a multi-agent system and propose an intelligent real-time scheduler that schedules patients and resources based on the actual status of departments. Two algorithms are implemented: one for resource scheduling that is based on predictive demand and the other is patient scheduling which performs path optimization depending on the actual status of departments. In order to match resources with stochastic demand, a coordination mechanism is developed that reschedules the resources in the outpatient clinics in real time through auction-bidding procedures. First, a simulation study of intelligent real-time scheduler is carried out followed by implementation of the same in an outpatient clinic of Aravind Eye Hospital, Madurai, India. This hospital has huge patient demand and the patient arrivals are random. The results show that the intelligent real-time scheduler improved the performance measures like waiting time, cycle time, and utilization significantly compared to scheduling of resources and patients in isolation. By scheduling resources and patients, based on system status and demand, the outpatient clinic system becomes a pull system. This scheduler transforms outpatient clinics from open loop system to closed-loop system.

Original languageEnglish
Article number1460458220905380
Pages (from-to)2383-2406
Number of pages24
JournalHealth Informatics Journal
Issue number4
Early online date21 Feb 2020
Publication statusPublished - Dec 2020


  • multi-agent systems
  • push-pull systems
  • real-time coordination
  • real-time scheduling
  • walk-in patients
  • CARE

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