Person: ÇALIŞ USLU, BANU
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ÇALIŞ USLU
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Publication Open Access Analysis of factors affecting IoT-based smart hospital design(SPRINGER, 2020-12) ÇALIŞ USLU, BANU; Uslu, Banu Calis; Okay, Ertug; Dursun, ErkanCurrently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital's dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.Publication Open Access The role of MAS interoperability for IoT applications: A survey on recent advances in manufacturing systems(2022-10-01) ÇALIŞ USLU, BANU; Çalış Uslu B.Çok Etmenli Sistemler (MAS), Nesnelerin İnterneti (IoT) uygulamaları içerisinde, akıllı nesnelerin etmenler (agent) olarak tasarlanması aracılığıyla birden çok akıllı cihazdan bilgilerin algılamasını, toplamasını, paylaşmasını, pekiştirilmesini ve revize edilmesini mümkün kılmaktadır. Çok etmenli sistemler ve ilgili araçlar, sistemlerin optimizasyonunda kullanılan paradigmalar için önemli değişikliklere katkıda bulunmakla birlikte, MAS hakkındaki mevcut literatür, IoT cihazlarının gelişmiş iş birliğini açıklayan modern dağıtılmış hesaplama yöntemlerini açıklamak için yeterli düzeyde değildir. Bu makale, IoT içerisinde yer alan, sensörler, akıllı telefonlar ve bilgi işlem merkezleri gibi akıllı nesnelerin, MAS teknolojisiyle entegre edilmeleri durumunda, sistemin performansına nasıl etki edeceğini açıklayan kapsamlı bir araştırma sunmaktadır. Bu kapsamda öncelikle, MAS ve IoT teknolojileri hakkında genel bir bilgilendirilme, MAS’ler içerisinde birlikte çalışabilirliğin önemi ve üretim sistemleri özelinde çoklu etmen teknolojisinin, sensör verilerini yakalamadan karar vermeye kadar çeşitli amaçlarla nasıl kullanılabileceği açıklanmıştır. Sonrasında, IoT sistemlerinde, akıllı nesnelerin birbirleriyle iletişim ve etkileşimini sağlayacak, teknik, anlamsal, sözdizimsel ve platform iş birlikteliği yaklaşımları ve MAS’lerin bu iş birlikteliklerinin kurulmasına nasıl katkı sağlayabileceği ifade edilmiştir. Son olarak, üretim sektörü özelinde, etmen tabanlı iş birliği modellerinin, merkeziyetçi ve merkeziyetçi olmayan yaklaşımla sistemin performansını nasıl etkilediği sunulmuştur. Araştırma, çok etmenli sistemlerin IoT’nin her bir katmanına entegre edilebileceğini ve bu yolla, bilgi güvenliğinden, enerjinin etkin kullanımına, rotalamadan sistem verimliliğin arttırılmasına kadar birçok alanda, IoT’nin etkinliğini arttırabileceğini ortaya koymuştur.Publication Open Access A HEURISTIC APPROACH TO MINIMISING MAXIMUM LATENESS ON A SINGLE MACHINE(SOUTHERN AFRICAN INST INDUSTRIAL ENGINEERING, 2015-11-30) ÇALIŞ USLU, BANU; Calis, B.; Bulkan, S.; Tuncer, F.This paper focuses on the problem of scheduling on a single machine to minimise the maximum lateness when each job has a different ready time, processing time, and due date. A simple procedure is developed to find a better solution than the early due date (EDD) algorithm. The new algorithm suggested in this paper is called Least Slack Time Look Ahead (LST-LA), which minimises the maximum lateness problem. Computational results show that when the number of jobs increases, LST-LA outperforms EDD.Publication Open Access Discrete event simulation model performed with data analytics for a call center optimization(2022-02-01) ÇALIŞ USLU, BANU; Serper N. G., Şen E., Çalış Uslu B.Optimization models enable organizations to find the best solution and respond to the demand from an uncertain environment and stochastic process promptly and with less engineering effort. This study aims to optimize the number of seasonal agents and customer prioritization needed for a call center system using big data analytics and discrete event simulations to improve customer satisfaction. The study was carried out based on data from a leading heating and ventilation company’s call center. The K-means clustering technique was used to determine customer segmentation on 6-million-customer data. For prioritization, the making of a Recency-Frequency-Monetary (RFM) analysis was applied. The system was modeled using ARENA simulation software, and performance parameters were measured depending on the segments obtained. The results show that the simulation model performed with data analytics gives better results for a beneficial financial impact with numerical values in customer prioritization, reducing the average waiting time of the most prioritized customers by more than 90%, and for the least prioritized customers, it increased the average waiting time by approximately just 40%. However, with the company segments, the increase in the average waiting time of the least prioritized customers was approximately 300%.Publication Metadata only Energy saving potential and energy audit of a faculty building at Marmara University in Turkiye(2022-10-01) VARBAK NEŞE, SEÇİL; ÇALIŞ USLU, BANU; AKPOLAT, ALPER NABİ; DURSUN, ERKAN; Sönmez T., Varbak Neşe S., Çaliş Uslu B., Akpolat A. N. , Dursun E.According to the Council of Higher Education\"s records in 2022, one hundred thirty-one public universities operate in Turkiye. If the energy efficiency in faculty buildings is improved by 20%, 1.458 million U.S. dollars savings per year would be possible. This paper presents the energy profile and energy audit of the Faculty of Technology at Marmara University, in Istanbul Türkiye. The energy efficiency opportunities for faculty building are investigated depending on the energy audit results. The fact that the faculty building is uninsulated therefore this situation causes severe energy losses. The annual heating energy requirement of this university building, which has an average construction area of 15,000 m², is 297,728 kWh. Only an average of 56,987 kWh in fuel consumption and up to 20% energy savings can be achieved with thermal insulation. However, when the existing lighting fixtures are replaced with Light Emitting Diode (LED) fixtures, the annual savings amount is $6,198. The results of this study enable search of sustainable solutions for reducing energy consumption and improving energy audits for faculty buildings.Publication Metadata only Municipal solid waste management: A case study utilizing DES and GIS(2023-10-02) ÇALIŞ USLU, BANU; DOĞAN, BUKET; ÜLKÜ, EYÜP EMRE; Çaliş Uslu B., Kerçek V. A., Şahin E., Perera T., Doğan B., Ülkü E. E.This research aims to compare two well-known solution methodologies, namely Geographical Information Systems (GIS) and Discrete Event Simulation (DES), which are used to design, analyze, and optimize the solid waste management system based on the locations of the garbage bins. A significant finding of the study was that the application of the simulation methodology for a geographical area of a size of 278km2was challenging in that the addition of the geographical conditions to the developed model proved to be time-consuming. On the other hand, the simulation model that was developed without adding geographical conditions revealed that the number of bins could be reduced by 60.3% depending on the population size and garbage density. However, this model could not be implemented since the required walking distance was higher than 75 m, which is greater than the distance the residents could be reasonably expected to travel to reach a bin. Thus, using a cutoff value of 75 m, the total number of bins can be reduced by 30% on average with regard to the result obtained from the GIS-based solution. This can lead to an annual cost reduction of 93.706 € on average in the collection process and carbon dioxide release reduction of 18% on average.Publication Metadata only A research survey: review of AI solution strategies of job shop scheduling problem(SPRINGER, 2015) ÇALIŞ USLU, BANU; Calis, Banu; Bulkan, SerolThis paper focus on artificial intelligence approaches to NP-hard job shop scheduling (JSS) problem. In the literature successful approaches of artificial intelligence techniques such as neural network, genetic algorithm, multi agent systems, simulating annealing, bee colony optimization, ant colony optimization, particle swarm algorithm, etc. are presented as solution approaches to job shop scheduling problem. These studies are surveyed and their successes are listed in this article.Publication Open Access Simulation analysis of a neonatal unit with complex patient flow patterns – an enhanced model for capacity planning (inpress)(2022-10-01) ÇALIŞ USLU, BANU; Perera T., Çalış Uslu B.Steadily increasing demand requires neonatal units and networks to improve their overall capacities. Given the operational complexities involved, simulation is a popular choice for modelling patient flow and analysing its impact on resource capacities. Clinical pathways, designed to reduce variation of care and improve the quality of care for a specific group of patients, broadly define patient flow patterns. The literature points to many simulation studies where the interactions between clinical pathways and resource planning have been addressed. For efficient model building, these simulation studies have, however. assumed a unidirectional flow of patients, i.e., progressively moving to lower levels of care. Patient flows are, however, much more complex. In some instances, patients may require a higher level of care than the current level of care. In such cases, bi-directional flows are created. This paper explores the impact of bidirectional flows on capacity planning. Using a real-world neonatal unit as an example, two scenarios of patient flow, i.e., unidirectional and bidirectional, are modelled and extensively analysed. This study revealed that the bidirectional flow model, which is the more realistic model, produces significantly different capacity planning estimates. For example, the number of admission requests rejected by the unit increased by 5-7 times, i.e., the uni-directional model significantly underestimates the overall capacity. The bidirectional model also revealed that there is a need to double the number of beds required for high-level care, and bed utilisation, in general, is higher than the estimates produced by the unidirectional model. Given that there is a need to generate accurate capacity estimates to ensure better services for patients and minimise regular changes due to poor capacity estimates, this paper argues that bidirectional modelling should be used to produce more accurate capacity estimates.