Analyzing the location of city logistics centers in Istanbul by integrating Geographic Information Systems with Binary Particle Swarm Optimization algorithm

Çakmak, Emre; Önden, İsmail; Acar, A. Zafer; Eldemir, Fahrettin

doi:10.1016/j.cstp.2020.07.004

Cited by 28 publications

(15 citation statements)

References 31 publications

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“…In addition, the European part of the city is divided in two parts by a natural port called the Golden Horn. These divided parts are connected by bridges that cause significant congestion and delays in urban traffic ( Çakmak et al, 2021 ). There are a total of 4,388,118 motor vehicles in Istanbul traffic and this number constitutes approximately 18% of the total number of vehicles in Turkey.…”

Section: Methodsmentioning

confidence: 99%

Transport mode preferences of university students in post-COVID-19 pandemic

Bağdatlı

Ipek

2022

Transport Policy

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The COVID-19 outbreak very quickly disrupted the order of human beings. While many sectors have been trying to cope with the ongoing COVID-19 process, they have also been trying to plan the new process for after the pandemic. Transport is one of the sectors most affected by the pandemic and it is necessary to produce the right political formulations for the post-pandemic period. For this reason, it is necessary to carefully examine the changing user demands in various segments of society due to COVID-19 and reveal effective post-pandemic transport policies. This study contributes to this requirement. Accordingly, this study investigated the transport mode preferences of university students in post-pandemic period in Istanbul, one of the important metropolises of the world, via the use of a survey. The reason for university students were focused on was that the mobility of university students is very high and in addition, they are more flexible than other age groups in using different transport modes. The main findings obtained from the study show that there will be a significant change in demand in transport modes after the pandemic. In particular, while a critical decrease may be observed in the travel demand for public buses, shared minibuses and LRT in public transport in post-pandemic period, a high increase in demand for private car use is highly probable. In addition, the research results indicate that COVID-19 can cause an increase in use of e-scooter/hoverboard and active travel modes. The results obtained through the statistical analysis and the discussions based on these results can make a significant contribution to the post-pandemic transport policies of cities with high university student populations and various transport modes, such as Istanbul.

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Section: Methodsmentioning

confidence: 99%

Transport mode preferences of university students in post-COVID-19 pandemic

Bağdatlı

Ipek

2022

Transport Policy

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“…Respecting the complexity of logistics, we considered a well-formulated geographicalarea-and-mathematical-model combination for evaluating and selecting the appropriate location of logistics centers [42]. Optimal clustering of numerous logistics activities can be approached by employing GIS as a spatial analyzer and BPSO as a metaheuristic model [29]. Therefore, the aim of this study was to integrate GIS and metaheuristic algorithms that will manage large-scale, real-world logistics instances.…”

Section: Background and Literature Reviewsmentioning

confidence: 99%

Hybrid Metaheuristic-Based Spatial Modeling and Analysis of Logistics Distribution Center

Khairunissa

Lee

2021

IJGI

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The location analysis of logistics distribution centers is one of the most critical issues in large-scale supply chains. While a number of algorithms and applications have been provided for this end, comparatively fewer investigations have been made into the integration of geographical information. This study proposes logistic distribution center location analysis that considers current geographic and embedded information gathered from a geographic information system (GIS). After reviewing the GIS, the decision variables and parameters are estimated using spatial analysis. These variables and parameters are utilized during mathematical problem-based analysis stage. While a number of existing algorithms have been proposed, this study applies a hybrid metaheuristic algorithm integrating particle swarm optimization (PSO) and genetic algorithm (GA). Using the proposed method, a more realistic mathematical model is established and solved for accurate analysis of logistics performance. To demonstrate the effectiveness of the proposed method, Korea Post distribution centers were considered in South Korea. Through tests with several real-world scenarios, it is proven experimentally that the proposed solution is more effective than existing PSO variations.

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“…Feature selection is essential in the data processing [6]. The large size of the data to be processed causes problems such as prolonged training and over-compliance [16].…”

Section: Binary Particle Swarm Optimizationmentioning

confidence: 99%

Diagnosis of COVID-19 Using Deep CNNs and Particle Swarm Optimization

Gürcan

Atıcı

Biçer

et al. 2021

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation

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Coronavirus pandemic (COVID-19) is an infectious illness. A newly explored coronavirus caused it. Currently, more than 112 million verified cases of COVID-19, containing 2,4 million deaths, are reported to WHO (February 2021). Scientists are working to develop treatments. Early detection and treatment of COVID-19 are critical to fighting disease. Recently, automated systems, specifically deep learningbased models, address the COVID-19 diagnosis task. There are various ways to test COVID-19. Imaging technologies are widely available, and chest X-ray and computed tomography images are helpful. A publicly available dataset was used in this study, including chest X-ray images of normal, COVID-19, and viral pneumonia. Firstly, images were preprocessed. Three deep learning models, namely DarkNet-53, ResNet-18, and Xception, were used in feature extraction from images. The number of extracted features was decreased by Binary Particle Swarm Optimization. Lastly, features were classified using Logistic Regression, Support Vector Machine, and XGBoost. The maximum accuracy score is 99.7% in a multi-classification task. This study reveals that pre-trained deep learning models with a metaheuristic-based feature selection give robust results. The proposed model aims to help healthcare professionals in COVID-19 diagnosis.

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Analyzing the location of city logistics centers in Istanbul by integrating Geographic Information Systems with Binary Particle Swarm Optimization algorithm

Cited by 28 publications

References 31 publications

Transport mode preferences of university students in post-COVID-19 pandemic

Transport mode preferences of university students in post-COVID-19 pandemic

Hybrid Metaheuristic-Based Spatial Modeling and Analysis of Logistics Distribution Center

Diagnosis of COVID-19 Using Deep CNNs and Particle Swarm Optimization

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