Development of heuristic optimization methods and experimental simulation design for the components and resources of healthcare

Sağlık sektörü, üretim dışında en büyük ve en hızlı büyüyen sektörlerden biridir. Çoğu ülkede, özellikle ABD’de küresel veya yerel bir sağlık sistemi bulunmamakla birlikte, sağlık hizmetleri için benzersiz ve karmaşık bir sistem bulunmaktadır. Bu karmaşıklık, kalitesiz hizmet, duplikasyonlar, yanlış̧ tedavi, yüksek bekleme süresi, ilaç hatası vb. gibi birçok soruna neden olmaktadır. Dünyada var olan sağlık sistemlerinde, sağlık kuruluşlarına ait iki tür sorun vardır. Birincisi, devlet sağlık kuruluşlarında hastaların tedavi olmak için bekleme süreleri çok yüksek olmaktadır. Bu nedenle hasta memnuniyetsizliği ve devlet sağlık harcamaları orantısız bir şekilde artmaktadır. İkinci problem ise özel sağlık kurumlarına aittir. Bu tür sağlık kuruluşlarında hastaların bekleme zamanı düşüktür ancak hastanın ödeyeceği tedavi maliyeti yüksektir. Bu, hastaların daha az beklemek için cebinden daha çok para ödemeleri gerektiği anlamına gelmektedir. Bu nedenle özel sağlık kurumlarında yıllık tedavi edilen hasta sayısı azalmaktadır ve yıllık gelirleri düşmektedir. Bu tür problemleri çözmek için iyi tanımlanmış matematiksel modelleme ile mantıklı bir çözüm elde etmek için optimizasyon yöntemi olarak bilinen sezgisel bir metot kullanımı kaçınılmaz olmaktadır. Dünyada sağlık sisteminde iki yapı vardır; bunlar devlet ve özel sağlık sistemleridir. Sağlık sistemlerini oluşturan birçok bileşenler bulunmaktadır. Özellikle hastalar, devletler (sağlık hizmetleri için ana kuralları ve yasaları yapan), sağlık hizmeti verenler ve özel sağlık sektörleri (özel sağlık sigorta şirketleri, özel hastaneler, özel sağlık sektörü çalışanları) sağlık sisteminin merkezinde yer almaktadır. Bu tez üç ana kısımdan oluşmaktadır. Tezin ilk kısmını oluşturan bölümde, sağlık sistemleri hem tekel düzeyde hem de etkileşimli olarak ele alınması ile optimizasyon modelleri geliştirilerek karşılaştırma yapılmıştır. Sağlık sistemleri için geliştirilen sezgisel matematiksel modellerde optimum sonuçların elde edilmesi amaçlanmıştır. Tezin ikinci bölümü ise sağlık hizmeti veren kuruluşlara ait sağlık kaynaklarının yönetimi ile alakalıdır. Stratejiler geliştirilerek sağlık sitemlerine ait kaynakların optimum sayısını elde etmek için deney tasarımı (DOE) tekniğinden yararlanılarak kesikli-olay-simülasyon modellemesi yapılmıştır. Sağlık sistemleri stokastik bir yapıya sahip oldukları için genellikle matematiksel modellemeler kapalı formüller ile ifade edilmektedir. Açıkça matematiksel olarak modellenemeyen sistemlerin simülasyon tekniklerini kullanmak yaygındır. Ancak, bu çalışma ile kaynaklara ait senaryolar oluşturularak her bir durum için sezgisel matematiksel modellemeler geliştirilmiştir. Böylelikle, hastaların bekleme süreleri en aza indirilerek tedavi edilen hastaların sayısını maksimuma çıkarılması amaçlanmıştır. Bu bölüme ek olarak, Türkiye’deki acil servislerinde uzman hemşire istihdamı ile acil servislerdeki yoğunluğun azaltılmasına yönelik bir simülasyon modeli geliştirilmiştir. Üçüncü bölümde, ülkelere ait sağlık sistemlerinin kalitesi sorgulanmıştır. Kişi başına düşen gayri safi milli hasılası 10.000 ABD dolarını aşan 53 ülkeyi kapsayan bir küresel sağlık rekabet endeksini (GHCI) oluşturmak için istatistiksel optimizasyon modeli geliştirilmiştir. Bu çalışmada, GHCI hastaların sağlık kuruluşlarında hizmet alma kolaylığını veya zorluğunu belirten bir indeks olarak tanımlanıştır. GHCI üzerinde etkili olduğu düşünülen faktörler belirlenmiş ve anlamlılık düzeyleri istatistiksel olarak analiz edilmiştir. Geliştirilen yöntemler aracılığıyla elde edilen verilere göre sağlık sisteminin içinde yer alan karar değişkenleri için senaryolar ve kriterler karşılaştırılarak en iyi çözümler elde edilmiştir. Tezin her üç kısmında, elde edilen sonuçlar ile sağlık sisteminin içinde yer alan bütün bileşenlerin fayda görmesi sağlanmıştır. Sağlık sistemlerin merkezinde yer alan ''hasta'' faktörünün daha çok fayda görmesi hedeflenmiştir--------------------Healthcare is one of the largest and fastest growing businesses besides manufacturing. Most of the countries, especially the U.S. do not have a global or local healthcare system but have a unique and complex system for healthcare service and delivery. This complexity causes many problems such as poor-quality service, duplicates, wrong treatment, high waiting time, medication error, etc. In health systems around the world, there are two types of problems that belong to health institutions. First, the waiting times for treating patients in public health facilities are very high. For this reason, patient dissatisfaction and state health spending are increasing disproportionately. The second problem belongs to private health institutions. In such health institutions, the waiting time of the patients is low, but the cost of the treatment is high. This means that patients have to pay a lot of money from their pocket to wait less. Because of this, the number of patients treated annually in private health institutions is decreasing and their annual income is decreasing. In order to solve such problems positively, the use of optimization method which is mathematical modelling is inevitable. This thesis consists of three chapters.There are two structures in healthcare system worldwide; these are public and private healthcare systems. There are many components that built up health systems. Especially, patients, governments as the main rules and laws maker for health care, healthcare providers and private healthcare sectors (private healthcare insurance companies, private hospitals, resources of private healthcare) are at the center of the healthcare system. In the section that constitutes the first part of the thesis, optimization models are developed by comparing healthcare systems both at the monopoly level and interactively. Intuitive mathematical models developed for health systems aim to achieve optimum results.The second part of the thesis is related to the management of healthcare resources. The aim of this research is to apply discrete event simulation approach based on optimization modelling and design of experiment (DOE) technique to derivate strategies and analyze situations to find optimum solutions in a healthcare department. Since healthcare systems have a stochastic structure, mathematical modeling is usually expressed by closed formulas. It is common to use simulation techniques of systems that cannot be mathematically modeled explicitly. However, with this study, the scenarios of the resources were created, and intuitive mathematical models were developed for each situation. Thus, the objectives of these applications are the minimization the waiting time of the patients, and the maximization the number of treated patients. In addition to this section, a simulation model to reduce the density of the emergency service with advanced nurses employed in the emergency services have been developed in Turkey.In the third chapter, the quality of health systems of countries is questioned. A statistical optimization model has been developed to create a global healthcare competitiveness index (GHCI) covering 53 countries with a gross domestic product per capita of over US $ 10,000. GHCI determines the ease or difficulty of receiving services for patients from healthcare systems of considered countries. Factors considered to be influential on the GHCI were identified and significance levels were analyzed statistically. Then, the desirability equation that is formed in the factors has been established to form the objective function in the optimization model. This section of the thesis has determined the optimum values of the factors for countries to be able to compete in healthcare service. Having those factors with optimum values is an indicator of higher quality of healthcare systems.According to the data obtained through the developed methods, the best solutions were obtained by comparing the scenarios and criteria for the decision variables within the healthcare system. In all three parts of the thesis, all the components included in the healthcare system are benefited with the results obtained by this research. It is aimed that the patient factor is addressed in the center of healthcare systems was benefit more

Development of heuristic optimization methods and experimental simulation design for the components and resources of healthcare

Sağlık sektörü, üretim dışında en büyük ve en hızlı büyüyen sektörlerden biridir. Çoğu ülkede, özellikle ABD’de küresel veya yerel bir sağlık sistemi bulunmamakla birlikte, sağlık hizmetleri için benzersiz ve karmaşık bir sistem bulunmaktadır. Bu karmaşıklık, kalitesiz hizmet, duplikasyonlar, yanlış̧ tedavi, yüksek bekleme süresi, ilaç hatası vb. gibi birçok soruna neden olmaktadır. Dünyada var olan sağlık sistemlerinde, sağlık kuruluşlarına ait iki tür sorun vardır. Birincisi, devlet sağlık kuruluşlarında hastaların tedavi olmak için bekleme süreleri çok yüksek olmaktadır. Bu nedenle hasta memnuniyetsizliği ve devlet sağlık harcamaları orantısız bir şekilde artmaktadır. İkinci problem ise özel sağlık kurumlarına aittir. Bu tür sağlık kuruluşlarında hastaların bekleme zamanı düşüktür ancak hastanın ödeyeceği tedavi maliyeti yüksektir. Bu, hastaların daha az beklemek için cebinden daha çok para ödemeleri gerektiği anlamına gelmektedir. Bu nedenle özel sağlık kurumlarında yıllık tedavi edilen hasta sayısı azalmaktadır ve yıllık gelirleri düşmektedir. Bu tür problemleri çözmek için iyi tanımlanmış matematiksel modelleme ile mantıklı bir çözüm elde etmek için optimizasyon yöntemi olarak bilinen sezgisel bir metot kullanımı kaçınılmaz olmaktadır. Dünyada sağlık sisteminde iki yapı vardır; bunlar devlet ve özel sağlık sistemleridir. Sağlık sistemlerini oluşturan birçok bileşenler bulunmaktadır. Özellikle hastalar, devletler (sağlık hizmetleri için ana kuralları ve yasaları yapan), sağlık hizmeti verenler ve özel sağlık sektörleri (özel sağlık sigorta şirketleri, özel hastaneler, özel sağlık sektörü çalışanları) sağlık sisteminin merkezinde yer almaktadır. Bu tez üç ana kısımdan oluşmaktadır. Tezin ilk kısmını oluşturan bölümde, sağlık sistemleri hem tekel düzeyde hem de etkileşimli olarak ele alınması ile optimizasyon modelleri geliştirilerek karşılaştırma yapılmıştır. Sağlık sistemleri için geliştirilen sezgisel matematiksel modellerde optimum sonuçların elde edilmesi amaçlanmıştır. Tezin ikinci bölümü ise sağlık hizmeti veren kuruluşlara ait sağlık kaynaklarının yönetimi ile alakalıdır. Stratejiler geliştirilerek sağlık sitemlerine ait kaynakların optimum sayısını elde etmek için deney tasarımı (DOE) tekniğinden yararlanılarak kesikli-olay-simülasyon modellemesi yapılmıştır. Sağlık sistemleri stokastik bir yapıya sahip oldukları için genellikle matematiksel modellemeler kapalı formüller ile ifade edilmektedir. Açıkça matematiksel olarak modellenemeyen sistemlerin simülasyon tekniklerini kullanmak yaygındır. Ancak, bu çalışma ile kaynaklara ait senaryolar oluşturularak her bir durum için sezgisel matematiksel modellemeler geliştirilmiştir. Böylelikle, hastaların bekleme süreleri en aza indirilerek tedavi edilen hastaların sayısını maksimuma çıkarılması amaçlanmıştır. Bu bölüme ek olarak, Türkiye’deki acil servislerinde uzman hemşire istihdamı ile acil servislerdeki yoğunluğun azaltılmasına yönelik bir simülasyon modeli geliştirilmiştir. Üçüncü bölümde, ülkelere ait sağlık sistemlerinin kalitesi sorgulanmıştır. Kişi başına düşen gayri safi milli hasılası 10.000 ABD dolarını aşan 53 ülkeyi kapsayan bir küresel sağlık rekabet endeksini (GHCI) oluşturmak için istatistiksel optimizasyon modeli geliştirilmiştir. Bu çalışmada, GHCI hastaların sağlık kuruluşlarında hizmet alma kolaylığını veya zorluğunu belirten bir indeks olarak tanımlanıştır. GHCI üzerinde etkili olduğu düşünülen faktörler belirlenmiş ve anlamlılık düzeyleri istatistiksel olarak analiz edilmiştir. Geliştirilen yöntemler aracılığıyla elde edilen verilere göre sağlık sisteminin içinde yer alan karar değişkenleri için senaryolar ve kriterler karşılaştırılarak en iyi çözümler elde edilmiştir. Tezin her üç kısmında, elde edilen sonuçlar ile sağlık sisteminin içinde yer alan bütün bileşenlerin fayda görmesi sağlanmıştır. Sağlık sistemlerin merkezinde yer alan ''hasta'' faktörünün daha çok fayda görmesi hedeflenmiştir -------------------- Healthcare is one of the largest and fastest growing businesses besides manufacturing. Most of the countries, especially the U.S. do not have a global or local healthcare system but have a unique and complex system for healthcare service and delivery. This complexity causes many problems such as poor-quality service, duplicates, wrong treatment, high waiting time, medication error, etc. In health systems around the world, there are two types of problems that belong to health institutions. First, the waiting times for treating patients in public health facilities are very high. For this reason, patient dissatisfaction and state health spending are increasing disproportionately. The second problem belongs to private health institutions. In such health institutions, the waiting time of the patients is low, but the cost of the treatment is high. This means that patients have to pay a lot of money from their pocket to wait less. Because of this, the number of patients treated annually in private health institutions is decreasing and their annual income is decreasing. In order to solve such problems positively, the use of optimization method which is mathematical modelling is inevitable. This thesis consists of three chapters. There are two structures in healthcare system worldwide; these are public and private healthcare systems. There are many components that built up health systems. Especially, patients, governments as the main rules and laws maker for health care, healthcare providers and private healthcare sectors (private healthcare insurance companies, private hospitals, resources of private healthcare) are at the center of the healthcare system. In the section that constitutes the first part of the thesis, optimization models are developed by comparing healthcare systems both at the monopoly level and interactively. Intuitive mathematical models developed for health systems aim to achieve optimum results. The second part of the thesis is related to the management of healthcare resources. The aim of this research is to apply discrete event simulation approach based on optimization modelling and design of experiment (DOE) technique to derivate strategies and analyze situations to find optimum solutions in a healthcare department. Since healthcare systems have a stochastic structure, mathematical modeling is usually expressed by closed formulas. It is common to use simulation techniques of systems that cannot be mathematically modeled explicitly. However, with this study, the scenarios of the resources were created, and intuitive mathematical models were developed for each situation. Thus, the objectives of these applications are the minimization the waiting time of the patients, and the maximization the number of treated patients. In addition to this section, a simulation model to reduce the density of the emergency service with advanced nurses employed in the emergency services have been developed in Turkey. In the third chapter, the quality of health systems of countries is questioned. A statistical optimization model has been developed to create a global healthcare competitiveness index (GHCI) covering 53 countries with a gross domestic product per capita of over US $ 10,000. GHCI determines the ease or difficulty of receiving services for patients from healthcare systems of considered countries. Factors considered to be influential on the GHCI were identified and significance levels were analyzed statistically. Then, the desirability equation that is formed in the factors has been established to form the objective function in the optimization model. This section of the thesis has determined the optimum values of the factors for countries to be able to compete in healthcare service. Having those factors with optimum values is an indicator of higher quality of healthcare systems. According to the data obtained through the developed methods, the best solutions were obtained by comparing the scenarios and criteria for the decision variables within the healthcare system. In all three parts of the thesis, all the components included in the healthcare system are benefited with the results obtained by this research. It is aimed that the patient factor is addressed in the center of healthcare systems was benefit more

Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy

The importance of solar power generation facilities, as one of the renewable energy types, is increasing daily. This study proposes a two-way validation approach to verify the validity of the forecast data by integrating solar energy production quantity with machine learning (ML) and I-MR statistical process control (SPC) charts. The estimation data for the amount of solar energy production were obtained by using random forest (RF), linear regression (LR), gradient boosting (GB), and adaptive boost or AdaBoost (AB) algorithms from ML models. Data belonging to eight independent variables consisting of environmental and geographical factors were used. This study consists of approximately two years of data on the amount of solar energy production for 636 days. The study consisted of three stages: First, descriptive statistics and analysis of variance tests of the dependent and independent variables were performed. In the second stage of the method, estimation data for the amount of solar energy production, representing the dependent variable, were obtained from AB, RF, GB, and LR algorithms and ML models. The AB algorithm performed best among the ML models, with the lowest RMSE, MSE, and MAE values and the highest R2 value for the forecast data. For the estimation phase of the AB algorithm, the RMSE, MSE, MAE, and R2 values were calculated as 0.328, 0.107, 0.134, and 0.909, respectively. The RF algorithm performed worst with performance scores for the prediction data. The RMSE, MSE, MAE, and R2 values of the RF algorithm were calculated as 0.685, 0.469, 0.503, and 0.623, respectively. In the last stage, the estimation data were tested with I-MR control charts, one of the statistical control tools. At the end of all phases, this study aimed to validate the results obtained by integrating the two techniques. Therefore, this study offers a critical perspective to demonstrate a two-way verification approach to whether a system’s forecast data are under control for the future

Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources

A healthcare resource allocation generally plays a vital role in the number of patients treated (pnt) and the patient waiting time (wt) in healthcare institutions. This study aimed to estimate pnt and wt as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient (δi) in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the δi of resources cost. The AB algorithm performed best in almost all scenarios based on the results of the analysis. According to the AB algorithm based on the δ0.0, δ0.1, δ0.2, and δ0.3, the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for pnt; 0.9514, 0.9517, 0.9514, and 0.9514 for wt, respectively in the training stage. The GB algorithm had the best performance value, except for the results of the δ0.2 (AB had a better accuracy at 0.8709 based on the value of δ0.2 for pnt) in the test stage. According to the AB algorithm based on the δ0.0, δ0.1, δ0.2, and δ0.3, the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for pnt; 0.8820, 0.8821, 0.8819, and 0.8818 for wt in the training phase, respectively. All scenarios created by the δi coefficient should be preferred for ED since the income provided by the pnt value to the hospital was more than the cost of healthcare resources. On the contrary, the wt estimation results of ML algorithms based on the δi coefficient differed. Although wt values in all ML algorithms with δ0.0 and δ0.1 coefficients reduced the cost of the hospital, wt values based on δ0.2 and δ0.3 increased the cost of the hospital

Employment of Emergency Advanced Nurses of Turkey: A Discrete-Event Simulation Application

In the present study, problems in emergency services (ESs) were dealt with by analyzing the working system of ESs in Turkey. The purpose of this study was to reduce the waiting times spent in hospitals by employing advanced nurses (ANs) to treat patients who are not urgent, or who may be treated as outpatients in ESs. By applying discrete-event simulation on a 1/24 (daily) and 7/24 (weekly) basis, and by employing ANs, it was determined that the number of patients that were treated increased by 26.71% on a 1/24 basis, and by 15.13% on a 7/24 basis. The waiting time that was spent from the admission to the ES until the treatment time decreased by 38.67% on a 1/24 basis and 53.66% on a 24/7 basis. Similarly, the length of stay was reduced from 82.46 min to 53.97 min in the ES. Among the findings, it was observed that the efficiency rate of the resources was balanced by the employment of ANs, although it was not possible to obtain sufficient efficiency from the resources used in the ESs prior to the present study

Development of Nonlinear Optimization Models for Wind Power Plants Using Box-Behnken Design of Experiment: A Case Study for Turkey

This study aims to develop an optimization model for obtaining the maximum benefit from wind power plants (WPPs) to help with reducing external dependence in terms of energy. In this sense, design of experiment and optimization methods are comprehensively combined in the wind energy field for the first time. Existing data from installed WPPs operating in Turkey for the years of 2017 and 2018 are analyzed. Both the individual and interactive effects of controllable factors, namely turbine power (MW), hub height (m) and rotor diameter (m), and uncontrollable factor as wind speed (m/s) on WPPs are investigated with the help of Box-Behnken design. Nonlinear optimization models are utilized to estimate optimum values for each decision variable in order to maximize the amount of energy to be produced for the future. Based on the developed nonlinear optimization models, the optimum results with high desirability value (0.9587) for the inputs of turbine power, hub height, rotor diameter and wind speed are calculated as 3.0670 MW, 108.8424 m, 106.7597 m, and 6.1684 m/s, respectively. The maximum energy output with these input values is computed as 9.952 million kWh per unit turbine, annually. The results of this study can be used as a guideline in the design of new WPPs to produce the maximum amount of energy contributing to supply escalating energy needs by more sustainable and clean ways for the future

E-Scooter Micro-Mobility Application for Postal Service: The Case of Turkey for Energy, Environment, and Economy Perspectives

In this research, the advantages of the e-scooter tool used in the mail or package delivery process were discussed by considering the Turkish Post Office (PTT) data in the districts of Istanbul (Kadıköy, Üsküdar, Kartal, and Maltepe) in Turkey. The optimization Poisson regression model was utilized to deliver the maximum number of packages or mails with minimum cost and the shortest time in terms of energy consumption, cost, and environmental contribution. Statistical and optimization results of dependent and independent variables were calculated using numerical and categorical features of 100 e-scooter drivers. The Poisson regression analysis determined that the e-scooter driver’s gender (p|0.05 p|0.05 2 emission. The fact that the distribution of packages or mail should now turn to micro-mobility is emerging with the advantages of e-scooter vehicles in the mail and package delivery. Finally, this analysis aims to provide a model for integrating e-scooters in package or mail delivery to local authorities, especially in densely populated areas

Estimation of Postal Service Delivery Time and Energy Cost with E-Scooter by Machine Learning Algorithms

This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R2) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R2 and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm’s MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R2 values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient