Feray TUNÇALP, Lerzan ÖRMECİ

2211

ÇOK TİPLİ HASTALAR İÇİN TERCİHLERİ BAZ ALAN BİR RANDEVU ÇİZELGELEME PROBLEMİ

Bu makale, bir sağlık tesisindeki bir doktor ya da tanı cihazının randevu planlama mekanizmasına odaklanmaktadır. Bu tesisi, getirileri birbirinden farklı olan birden çok hasta tipi kullanmaktadır. Tesis, her hasta tipinden gelen randevu isteklerini her günün başında gözlemlemektedir. Müsait randevu saatlerini bu randevu isteklerine nasıl tahsis edeceğine karar vermektedir. Hastalar belli bir olasılıkla rezervasyon dönemindeki bir günü tercih etmektedirler ve sadece bir tercihleri vardır. Hastalara ya tercih ettiği güne bir randevu verilmektedir ya da randevu istekleri reddedilmektedir. Tesis, getirilerini maksimize ederken reddedilme maliyetlerini belli bir seviyede tutmak istemektedir. Bu süreç, sonsuz zamanlı beklenen indirgenmiş karı maksimize etmek için ayrık zamanlı ve kısıtlı Markov Karar Süreci ile modellenmektedir. Kısıt, sonsuz zamanlı beklenen indirgenmiş reddedilme maliyetlerinin belli bir eşik değerinin altında olmasını garanti etmektedir. En iyi politikanın rassallaştırılmış bir rezervasyon limiti politikasının olduğunu gösterdik. Modeli çözmek için iyi bilinen bir “Yaklaşık Dinamik Programlama” metodu olan “Geçici Farklarla Öğrenme Algoritmasını” uyguladık. “Yaklaşık Dinamik Programlama” sonuçlarını diğer buluşsal yöntemlerle sayısal olarak karşılaştırdık.

A PREFERENCE-BASED APPOINTMENT SCHEDULING PROBLEM WITH MULTIPLE PATIENT TYPES

This paper focuses on the appointment scheduling mechanism of a physician or a diagnosticresource in a healthcare facility. Multiple patient types with different revenues use thefacility. The facility observes the number of appointment requests arriving from each patienttype at the beginning of each day. It decides on how to allocate available appointment slotsto these appointment requests. Patients prefer a day in the booking horizon with a specificprobability and they have only one preference. Patients are either given an appointment fortheir preferred days or their appointment requests are rejected. The facility wants to keepthe rejection costs at a certain level, while maximizing its revenues. This process is modeledwith a discrete time and constrained Markov Decision Process to maximize the infinitehorizonexpected discounted revenue. The constraint guarantees that the infinite-horizonexpected discounted rejection cost is below a specific threshold. We have proved that theoptimal policy is a randomized booking limit policy. To solve the model, we have implementedTemporal Difference (TD) Learning Algorithm, which is a well-known Approximate DynamicProgramming (ADP) method. We have compared the ADP results with other heuristicsnumerically.
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Endüstri Mühendisliği-Cover
  • ISSN: 1300-3410
  • Yayın Aralığı: Yılda 3 Sayı
  • Başlangıç: 1989
  • Yayıncı: TMMOB MAKİNA MÜHENDİSLERİ ODASI
Arşiv
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A PREFERENCE-BASED APPOINTMENT SCHEDULING PROBLEM WITH MULTIPLE PATIENT TYPES

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