Şimşek, Ahmet Bahadır; Merdane, Seher; Belindir, Aydanur; Akbaş, Ayşenur

In the Turkish Healthcare System, pharmacies are an important echelon of the drug supply chain. The pharmacy on duty practice ensures that the demand for drugs and similar medical supplies is met 24/7. The duty pharmacy scheduling problem aims to satisfy the customers and the pharmacists without violating the regulations while determining the duty sequence of the pharmacies in a certain planning period. This study handles the duty pharmacy scheduling problem of Gümüşhane City Center where 13 pharmacies operate. In the current system, the representative pharmacist prepares the duty schedule manually. The planning period is divided into subsets, and pharmacies are assigned to the duties in each subset respectively in such the number of duties of each pharmacy in the subsets is as equal as possible. The managerial problem of the case is that the duty frequency of each pharmacy is variable in the manual schedule and pharmacists perceive this as an unfairness. Because pharmacists expect to be on duty 1 in 13 days. We propose a mathematical model that adopts the fixed duty frequency and the inheritance of duty distribution. We test the model according to the 2020 calendar and compare model results with the real duty schedule belong to 2020 using the 2019 distribution of pharmacies. The comparison and test results show that the duties are distributed as fairly as possible and the turn of each pharmacy is repeated once every 13 days. The contribution of this study is that it introduces innovative components to the pharmacy duty scheduling problem, offers practical policy recommendations, and provides a case-specific example to limited PDS literature.

Ünlü, Ramazan

To find out which period the historical coins belong to requires a number of scientific procedures that archaeologists or expertscan do. These operations can often be time-consuming and demanding operations. From this point on, in this study, theautomatically classification of historical coins by using machine learning methods is discussed. Being able to use machine learningmethods to classify historical coins can help experts and can become an analysis tool without the need for scientific tests fornon-experts. For this purpose, some physical properties of different coins used in Anatolian geography were collected andclassified by various machine learning methods named SVM, Random Forest, Bagging, and Decision Trees. Also, two differentmissing values strategies are deployed in conjunction with each chosen method. Based on our findings, random forest methodtogether with imputing missing values with mean gives an acceptable results with the accuracy rate of %71, although there aresome limitations such as high rate of missing values and working with a small dataset