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ÖgeEmergency medical system design for disaster response(Fen Bilimleri Enstitüsü, 2020)Disasters are large-scale events that affect human life, both materially and spiritually. There are many precautions to be taken to mitigate the devastating effect of disasters. One of them is effectively planning of post-disaster emergency medical response system. Since the most important factor is saving human life, proper planning of medical centers, and transportation of casualties to these centers is crucial during the disaster response phase. Therefore, it is necessary to design the Emergency Medical System (EMS) before disasters. EMS consists of many components such as disaster areas, hospitals, Temporary Medical Centers (TMC), casualties, medical staff, ambulances, etc. The proper planning and design of EMS are crucial to respond casualties and serve them effectively. Therefore, location planning of TMCs or field hospitals, classification of injuries (triage), assignment and transportation of casualties, determining the needs of medical staff in the medical centers play a significant role in mitigating the devastating effect of mass casualty events like disasters or incidents. Humanitarian Logistics or Disaster Management activities consist of preparation, supply, transportation, location, allocation, network design, tracking, and storage. Humanitarian Logistics is a challenging process, and this process contains many uncertainties. The main uncertainties in are the time, location, severity of a disaster, and size of demand. The uncertainties and variability in the complex nature of disaster management require formulating problems as a stochastic programming model in general. Humanitarian Logistics (HL) can be divided into three main topics, which are facility location, inventory management, and network flows/design problems. In the HL literature, facility location studies are commonly divided into three categories. These are emergency medical center, relief supplies warehouse, and shelter site or collection point location problems. In large-scale emergency events such as earthquake, hurricane, flood, and tsunami, the capacity of hospitals is not enough for the treatment of the casualties. Therefore, TMCs are located at the suitable sites by considering existing hospitals to serve casualties for medical response. In the first part of this study, it is aimed to determine the location and number of TMCs in case of an earthquake by considering different factors. In the objective function, we considered the setup cost of TMCs and the transportation cost of casualties. In addition, locations and bed capacities of the existing hospitals, possibilities of damage to the hospitals and roads are taken into account. At the same time, a widely used triage system is applied to classify casualties according to their injured level. The distances between disaster areas and EMCs also considered to minimize response time. For this problem, a two-stage stochastic programming model was developed. The proposed model finds an optimal TMC location solution while minimizing the total setup cost of the TMCs and the total expected transportation cost of casualties. Besides, the model was reformulated by considering a single-type of casualty to show the effect of triage on the solution of the problem. Based on the different earthquake scenarios in JICA Report (2002), a real case study was conducted for the Kartal district of Istanbul. The results were presented, and a sensitivity analysis was performed for critical parameters. The medical staff planning of medical centers is vital as wells as the location planning of medical centers to provide services to all casualties assigned to these centers. Therefore, the medical staff capacity should be considered in addition to the patient's capacity when assigning casualties to the medical centers. Besides, assuming that all of the expected casualties occur immediately after the disaster causes ineffective and unrealistic usage of resources. There is also another fact that a casualty might not stay in the same health condition as time passes. For these reasons, a multi-objective dynamic stochastic model was proposed for the medical staff assignment, casualty allocation, and TMC location planning simultaneously. In the proposed model, it is aimed to minimize the expected values of the total number of unserved casualties, the distance between disaster areas and emergency medical centers, and the number of medical staff needed. The first 72 hours after the disaster was considered and divided into four periods to reflect the dynamic behaviour of such events. Thus, with the dynamic model, it is aimed to use the capacities of emergency medical centers more efficiently and realistically. The stochastic nature of casualties' health condition was also included the model as a Discrete-time Markov Chain. For the case study, Kartal district data used in the model-1 has been updated according to the recently published report of the Istanbul Metropolitan Municipality (IBB-KRDAE, 2020). AUGMECON2 method was applied to solve the multi-objective model, and the results were analysed. According to the results for the case study in the first model, the total patient capacity of existing hospitals and all recommended TMCs are not enough for the most probable earhtquake scenarios defined in JICA Report (2002). However, for the most optimistic scenario, setting up 53 out of 74 candidate TMCs after the disaster is suffcient to assign all casualties to the medical centers. Besides, the percentage of unassigned casualties is 14.9% for the most probable scenario and the average percentage of unassigned casualties over all scenarios is about 10%. In the second case study, where the injured estimates are taken from the most recent study (IBB-KRDAE, 2020), there is enough capacity to assign all casualties to the EMCs over all scenarios. The number of TMC that must be set up is 50 for the worst-case scenario and 21 for the base-scenario. The results and analysis for both models offers some managerial insights associated with the number of temporary medical centers needed, their locations, additional capacity requirements, required number of medical staff, and allocation of casualties. We hope that this study will give a new perspective about the pre- and post-disaster emergency medical system design and contribute to the Humanitarian Logistics literature.
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ÖgeOrganizasyonel öğrenme, adımları ve uygulanması(Fen Bilimleri Enstitüsü, 2020)Organizasyonel öğrenme tartışmalarının uzun sayılabilecek tarihine karşın, bu başlık yirminci yüzyılın son yirmi yılında yeniden doğdu. Kurt Lewin ve James March'tan Peter Senge'ye pek çok araştırmacı ve bilim insanı, bu tartışmalara katkı olarak günümüze dek pek çok değerli çalışmalar sundu. Organizasyon etkinliğinin niteliği ve öğrenmenin niteliği, son yıllarda dikkat çeken başlıklar oldu. Günümüzde organizasyonel öğrenme, sertleşen rekabetin ve beklenmedik belirsizliklerin olağanlaştığı piyasalarda, şirketlerin başarı yolculuklarında önemli bir yetenek olarak ilgi ve dikkat çekmektedir. Böyle olunca da organizasyonel öğrenme başlıklı çalışmalar konuya dikkat çekmektedir. Organizasyonel öğrenme, değişim yönetiminin de çok önemli başlıklarından biri hâline gelmiştir. Firmalar, kurumsallaştıkça organizasyonel öğrenmeyi daha da çok gereksinim olarak görmüşler ve organizasyonel öğrenmenin sağladığı önemli rekabet avantajlarını fark etmişlerdir. Konuyla ilgili çalışmalar, organizasyonel öğrenme sürecinin farklı adım ya da adımlarına kendi bağlamları itibarıyla işaret etmektedir. Bu tez çalışması, kapsamlı bir kaynak çalışması üzerinden, organizasyonel öğrenme sürecinin adımlarını bir arada görünür duruma getirmeyi amaçlamaktadır ve bu adımların nasıl uygulanacağına ilişkin bir öneri sunmaktadır. Organizasyonel öğrenme sürecinde uygulanması gerekenler, farklı farklı noktalardan bakılarak dile getirilmiş olan çok sayıda çalışmada önerilenler, bütünsel bir bağlamda ve organizasyonel öğrenmeye bir süreç olarak bakarak değerlendirilmiştir. Bu çerçevede beş adımdan oluşan bir süreç önerisi ortaya çıkmıştır. Ardından, bu öneri, bu adımların nasıl uygulanabileceğine ilişkin bir literatür çalışması ile desteklenmiştir. Kuşkusuz organizasyonel öğrenmeden söz edildiğinde; öğrenen takımlar, öğrenen takımların kapasitesi, öğrenen takımların nasıl tasarlanacağı ve öğrenen takımlarla öğrenme ilişkisinden de söz edilmelidir. Bu çerçevede daha önceki yıllarda ortaya konmuş bu çalışmalar, bu tez çalışmasına da hem önemli bir esin kaynağı ve hem de önemli basılı kaynaklar olmuştur. Çalışma sonunda, organizasyonel öğrenmenin öğrenen takımlar üzerinden yürütülmesi gerektiği ve başlıca sessiz bilginin ortaya çıkarılıp süreçlere taşınmasıyla ilerleyecek oluşu ortaya çıkmıştır. Gerçekleştirilen literatür çalışması sonucunda, başlıca, bilginin ortaya çıkarılması, bilginin paylaşılması, bilgide ortaklaşma, bilginin uygulanması ve kayıt altına alınması adımlarından oluşmasının önerildiği bu süreç, bir KOBİ uygulamasıyla sınanmış ve ağır kriz altındaki bu işletmede oldukça olumlu ve ölçülebilir sonuçlar vermiştir. Satış sürecine yönelik olarak uygulanan organizasyonel öğrenme sürecinin, şirketin diğer yönetim alanlanlarına da uygulandığında başka önemli, etkili ve şirket performansını yükselten sonuçlar ortaya çıkabileceği değerlendirmesi yapılabilir. "Organizasyonel öğrenme, adımları ve uygulanması" başlığı altında bugüne dek organizasyonel öğrenme konusunda yapılmış çalışmalara bir katkı olabileceği umulan bu tez çalışmasının, gelecekte, bu ya da benzeri başlık ya da başlıklar altında yapılacak başka çalışmalarla daha da zenginleşeceği umulmaktadır.
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ÖgeDeriving weights of decision makers in group decision making and applications in medical decision making and sensor fusion( 2020)The motivation behind the rational decision-making method is to determine the most proper alternative(s) from a set of alternatives regarding the predefined criteria. A structured and reasonable decision making process is essential to settling on rational and appropriate decisions. Especially, the use of rational approaches instead of subjective techniques stimulates organizations to take the correct decisions and cope with any difficulties, efficiently. Consequently, decision making methods have been applied efficiently in a variety of complex areas, such as the military, economics, government organization, and are increasingly attracting the attention of academics for years. Quality of the solution of the decision making depends fundamentally on the nature of the problem, but mostly on the characteristics of decision makers. As the complication of the socio-economic environment increases, it gets more problematic for single decision maker to handle all the relevant features of the problem. Most decision making problems in real world occur in a group environment and this adds too much complexity to the analysis. Therefore, academics are searching for appropriate group decision making (GDM) approaches in recent years to overcome this problem. GDM is a method in which a group of experts (i.e. decision makers, group members, voters, stakeholders) are gathered to find out the solution of the decision making problem. In this process, motivation and understanding of a common problem differ from one decision maker to other depending on the knowledge, background, and expertise of these decision makers. At this point, different weights can be assigned to these people reflecting their importance or perceived reliability for the given problem. In GDM problems, experts describe their preferences by taking each criterion into account, and final decision is reached by merging all decision matrices into an aggregated solution applying a proper operator. At this point, it is important to develop a better technique for aggregating different decision makers' preferences to obtain an acceptable decision making result. In the literature, GDM methods commonly assume that the decision makers have same level of importance weights and disregards the relative weights. This situation may cause inappropriate and inaccurate outcomes that cannot be compensated in the final result. Consequently, reliability and the significance of decision makers on the final decision should be taken into consideration. At that point, how to derive the appropriate weights of decision makers stands as a new challenge. Same challenge is also valid for the multi-source fusion problems that effort to find an appropriate technique to combine the data from multiple sources; for example, sensors, where each sensor may have different features. The key difference here is that the sensors, which may differ in specifications, are replaced with the decision makers whose expertise, background, or knowledge may also vary. Therefore, methods, which are developed to overcome this challenge, have several applications in wireless sensor networks, image fusion, etc. In literature, researches on deriving the weight of decision makers are relatively limited. Moreover, a comprehensive literature review on determining the weight of decision makers is missing among a limited number of studies. Consequently, in the second chapter of the thesis, the literature on deriving the objective weights of decision makers is studied and a new scheme for classification is proposed. According to the stated classification scheme; objective methods are divided into five groups: Similarity-based approaches, index-based approaches, cluster-based approaches, integrated approaches, and other approaches. Literature review and analysis of the studies in literature were conducted with respect to these categories. In the third chapter of the thesis; in order to demonstrate the application of integrated approaches, a new method, that derives decision makers' combined weights using the geometric weights consensus index (objective method) and the subjective weights provided by a supervisor, is developed. The application of the method is verified on a case study in a medical decision making problem, specifically, selection of a suitable anesthesia method to apply in the surgery which involves three alternatives such as the general anesthesia, local anesthesia and sedation. In the fourth chapter, a large scale GDM approach is proposed for the sensor fusion. Since the proposed method is a cluster-based method, it provides acceptable results in sensor networks consisting of multiple sensors. The method can operate under uncertainty as a result of converting raw data from sensors into basic probability assignments. In addition, by assigning three objective weights, the reliability of the sensor clusters was also taken into account. In addition to these objective weights, the proposed method allows subjective weights to be allocated to integrate the experience and knowledge of supervisors into the problem area. The applicability and validity of the proposed method have been checked with two real classification data sets. Experiments show that when the proposed method is applied to two data sets, the classification rate increases significantly. In the last part of the study, the effect of the expansion parameter, objective weights, reliability threshold, number of clusters and clustering method on the classification rate and probability of detection are examined. In the last chapter of the thesis, the results obtained from these studies, problem areas, limitations and potential research directions are discussed.
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ÖgeMedikal turizm hizmeti tedarik zinciri tasarımında işbirlikçi bir bütünleşik yöntem önerisi( 2020)Medikal turizm (MT), hastaların ikamet ettikleri ülke dışında tedavi görmek için tıbbi kurumlara erişebilmesidir. Yapılan literatür taraması ve sektör raporu incelemeleri sonucu bu çalışma kapsamında MT artan önemi dolayısıyla ele alınmıştır. Özellikle saç ekimi, diş tedavisi ve göz ameliyatı gibi isteğe bağlı olan, aciliyet göstermeyen tedavi tipleri üzerinde durulmuştur. Mevcut MT çalışmaları, özellikle iş birliklerinin önemine vurgu yapmakta, bu alanda araştırmaların yapılması gerekliliğine dikkat çekmektedir. Bu çalışmanın amacı MT hizmetlerinde tedarik zinciri yapısını ortaya koymak ve medikal turizm hizmet tedarik zinciri (MTHTZ) kapsamındaki "hizmet tasarımı" ve "tedarikçi iş birlikleri" süreçlerini bütünleşik olarak yönetmeye destek olacak bir yöntembilim önermektir. Bu amacı gerçekleştirebilmek için ilk olarak MT hizmeti tedarik zincirinde yer alan taraflar belirlenmiş, iş süreçleri tanımlanmış ve kavramsal bir model önerilmiştir. Yöntem olarak ise üçgenleme (triangulation) uygulanmış, bu kapsamda literatür taraması, derinlemesine görüşmeler ve uzman değerlendirmeleri gerçekleştirilmiştir. MTHTZ iş süreçleri hizmet tasarımı, müşteri ilişkileri yönetimi, talep yönetimi, kapasite ve kaynak yönetimi, tedarikçi ilişkileri yönetimi, hizmet sunumu yönetimi, hizmet telafisi yönetimi olarak tanımlanmıştır. Bu süreçlerin ilki olan "Hizmet Tasarımı", takip eden diğer süreçleri tetiklemesi sebebiyle seçilmiş, tezde bu iş sürecine odaklanılmıştır. Hizmet tasarımı, müşteri beklentilerini anlamayı, bu beklentilere istinaden belli kriterlere göre tedarikçi / kaynak bulmayı, ve son olarak da bu kaynakların kapasitelerini planlamayı gerektirmektedir. Tezin devamında hizmet tasarımı iş süreci kapsamındaki kararlara destek olan bir yöntembilim ortaya konmuştur. Medikal turist ihtiyaç ve beklentilerini anlamak amacıyla Uyarlamalı Seçime - Dayalı Konjoint (USDK) Analizi kullanılmış,. medikal turistlerden toplanan geri bildirimler sayma verilerinin analizi ve Hiyerarşik Bayes Regresyon analizi ile incelenmiştir. analiz sonuçlarına göre bir medikal turizm hizmet paketinde muhakkak olması gereken veya isteğe bağlı bırakılması gereken hizmetler net olarak tanımlanmıştır. Böylece bir asistan firmanın, medikal turistlere sunabileceği hizmet paketinin içeriği ve arka planda hangi hizmet sağlayıcılar ile iş birliği yapması gerektiği de netlik kazanmıştır. MTHTZ'de yapılacak iş birliklerinin tedarikçi/hizmet sağlayıcı ön seçim kararına destek olmak amacıyla bulanık kural tabanlı çıkarım sistemi kullanımı ile iş birliği / tedarikçi değerlendirme kriterleri belirlenmiş, literatür taraması ve sektör yetkilileri görüşleri ile bu kriterlerin bulanık veri setlerini elde edilmiş, veriler MATLAB'te analiz edildikten sonra kabul edilebilir iş birliği seviyesi netleştirilmiş ve farklı senaryoların gösterimi sağlanmıştır. Son aşamada ise gelen taleplerin ön seçimle belirlenen tedarikçilerden hangilerine atanacağına karar vermeye destek olması için bir matematiksel atama modeli önerilmiştir. Önerilen yöntemler adım adım İstanbul'daki bir asistan firma için uygulanmış ve önerilen yöntembilimin kullanılabilirliği/uygulanabilirliği tartışılmıştır. Çalışma sonucunda bir asistan firmanın aracılık hizmetlerini yönettiği MTHTZ yapısı ortaya konmuş, zincir üyeleri ve iş süreçleri tanımlanmıştır. Hizmet tasarımı iş süreci kapsamında bütünleşik bir tedarikçi iş birliği yöntembilimi önerilmiştir. Bu kapsamda yapılan uygulamada ilk olarak müşterinin (bu durumda hastanın) bir MT hizmetinde hangi hizmet sağlayıcılardan faydalanmak istediği ortaya konmuştur. Devamında asistan firmanın tedarikçileri konumundaki hizmet sağlayıcılar için kriterleri belirlenmiş, ilgili tedarikçiler skorlarla değerlendirilmiştir. Son olarak, hem müşterinin tercihlerini dikkate alan, hem de belirlenen kriterlere istinaden tedarikçileri önceliklendiren bir matematiksel atama modeli ile, kapasiteleri de göz önünde bulundurarak asistan firmaya gelen taleplerin tedarikçilere atanması sağlanmıştır.
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ÖgeEmergency medical system design for disaster response(Graduate School, 2020-08-28)Disasters are large-scale events that affect human life, both materially and spiritually. There are many precautions to be taken to mitigate the devastating effect of disasters. One of them is effectively planning of post-disaster emergency medical response system. Since the most important factor is saving human life, proper planning of medical centers, and transportation of casualties to these centers is crucial during the disaster response phase. Therefore, it is necessary to design the Emergency Medical System (EMS) before disasters. EMS consists of many components such as disaster areas, hospitals, Temporary Medical Centers (TMC), casualties, medical staff, ambulances, etc. The proper planning and design of EMS are crucial to respond casualties and serve them effectively. Therefore, location planning of TMCs or field hospitals, classification of injuries (triage), assignment and transportation of casualties, determining the needs of medical staff in the medical centers play a significant role in mitigating the devastating effect of mass casualty events like disasters or incidents. Humanitarian Logistics or Disaster Management activities consist of preparation, supply, transportation, location, allocation, network design, tracking, and storage. Humanitarian Logistics is a challenging process, and this process contains many uncertainties. The main uncertainties in are the time, location, severity of a disaster, and size of demand. The uncertainties and variability in the complex nature of disaster management require formulating problems as a stochastic programming model in general. Humanitarian Logistics (HL) can be divided into three main topics, which are facility location, inventory management, and network flows/design problems. In the HL literature, facility location studies are commonly divided into three categories. These are emergency medical center, relief supplies warehouse, and shelter site or collection point location problems. In large-scale emergency events such as earthquake, hurricane, flood, and tsunami, the capacity of hospitals is not enough for the treatment of the casualties. Therefore, TMCs are located at the suitable sites by considering existing hospitals to serve casualties for medical response. In the first part of this study, it is aimed to determine the location and number of TMCs in case of an earthquake by considering different factors. In the objective function, we considered the setup cost of TMCs and the transportation cost of casualties. In addition, locations and bed capacities of the existing hospitals, possibilities of damage to the hospitals and roads are taken into account. At the same time, a widely used triage system is applied to classify casualties according to their injured level. The distances between disaster areas and EMCs also considered to minimize response time. For this problem, a two-stage stochastic programming model was developed. The proposed model finds an optimal TMC location solution while minimizing the total setup cost of the TMCs and the total expected transportation cost of casualties. Besides, the model was reformulated by considering a single-type of casualty to show the effect of triage on the solution of the problem. Based on the different earthquake scenarios in JICA Report (2002), a real case study was conducted for the Kartal district of Istanbul. The results were presented, and a sensitivity analysis was performed for critical parameters. The medical staff planning of medical centers is vital as wells as the location planning of medical centers to provide services to all casualties assigned to these centers. Therefore, the medical staff capacity should be considered in addition to the patient's capacity when assigning casualties to the medical centers. Besides, assuming that all of the expected casualties occur immediately after the disaster causes ineffective and unrealistic usage of resources. There is also another fact that a casualty might not stay in the same health condition as time passes. For these reasons, a multi-objective dynamic stochastic model was proposed for the medical staff assignment, casualty allocation, and TMC location planning simultaneously. In the proposed model, it is aimed to minimize the expected values of the total number of unserved casualties, the distance between disaster areas and emergency medical centers, and the number of medical staff needed. The first 72 hours after the disaster was considered and divided into four periods to reflect the dynamic behaviour of such events. Thus, with the dynamic model, it is aimed to use the capacities of emergency medical centers more efficiently and realistically. The stochastic nature of casualties' health condition was also included the model as a Discrete-time Markov Chain. For the case study, Kartal district data used in the model-1 has been updated according to the recently published report of the Istanbul Metropolitan Municipality (IBB-KRDAE, 2020). AUGMECON2 method was applied to solve the multi-objective model, and the results were analysed. According to the results for the case study in the first model, the total patient capacity of existing hospitals and all recommended TMCs are not enough for the most probable earhtquake scenarios defined in JICA Report (2002). However, for the most optimistic scenario, setting up 53 out of 74 candidate TMCs after the disaster is suffcient to assign all casualties to the medical centers. Besides, the percentage of unassigned casualties is 14.9% for the most probable scenario and the average percentage of unassigned casualties over all scenarios is about 10%. In the second case study, where the injured estimates are taken from the most recent study (IBB-KRDAE, 2020), there is enough capacity to assign all casualties to the EMCs over all scenarios. The number of TMC that must be set up is 50 for the worst-case scenario and 21 for the base-scenario. The results and analysis for both models offers some managerial insights associated with the number of temporary medical centers needed, their locations, additional capacity requirements, required number of medical staff, and allocation of casualties. We hope that this study will give a new perspective about the pre- and post-disaster emergency medical system design and contribute to the Humanitarian Logistics literature.