Integrated Fuzzy Multi-Criteria Decision Making Application within An Environmental Evaluation Framework: A Case Study in Türkİye

The selection process of eligible suppliers in supply chains entails numerous challenges under rapidly evolving conditions. Environmental considerations in public discourse, competitive market structures, and emerging technological capabilities influence the decision-making procedures. Rather than the conventional criteria of cost and service, different criteria have more recently been taken into consideration. In this study, presenting a Turkish case study, environmental management, environmental agility and environmental technology dimensions and the criteria related to these dimensions are defined. A fuzzy SWARA-BWM method was implemented in an integrated way to cope with the supplier selection problem. Different scenarios were created and benchmarked. The results of the study indicate that environmental agility is the prominent dimension, while the most significant criterion is delivery speed. The optimal supplier alternative among the four alternatives was identified as . This study was carried out to contribute to the examination and modeling of supply chain management issues

Exploring the Design Guidelines of Large Screen UI for Optimal Viewing Experience on Smart TV

With the prevalence of large-screen smart TVs, viewing experiences are becoming more interactive. This technological advance makes an increase in information density and diverse layouts. This study aims to derive ergonomic design guidelines for TV UI to optimize the viewing experience. Fifteen subjects participated in the main test, performing tasks consisting of visual navigation and remote manipulation according to twelve different alternatives. The experimental variables were selected as screen size, information quantity and layout. The dependent measures were collected on performance time, visual cognitive satisfaction and manipulation satisfaction. The results revealed that screen size, information quantity, and layout had significant effects on visual satisfaction. Larger screens with more information enhanced visual cognitive satisfaction. Screen size and information quantity influenced on manipulation satisfaction. Meanwhile, information quantity and layout had greater impacts on user performance. This research provides appropriate design guidelines for smart TV UI and practical implications for product developers

Optimal Resource Allocation Model for Multi-Function Radar on Navy Warships

Efficiently detecting targets using radar on a warship is critical for defending the warship itself at the initial stage of engagement. To cope with various uncertain threats (or targets), radar resources must be used efficiently because of their limited availability in terms of power, dwell time, and bandwidth. In this study, we develop an optimal multi-function radar resource allocation model in which we maximize the survival probability of a warship from all detected targets under limited resources allocated to the subsystems. The proposed problems are convex optimization models with concave objective functions. We propose two exact (polynomial and pseudo-polynomial time) algorithms that optimally solve the problem via the well-known KKT conditions. In addition, we establish special conditions for problem instances in which resources are allocated equally to all subsystems. Computational experiments show that the algorithms not only guarantee optimality but are also computationally intensive. Finally, we perform regression analysis to investigate the relationship between resource allocation and targets’ threat levels, and it shows that threat level influences more on the resource allocation than the exponent of the detection probability function

MODELING OF CROSS-NETWORK COLLABORATION SUPPLY CHAIN AND SOLUTION VIA REINFORCEMENT LEARNING-BASED EVOLUTIONARY ALGORITHM

Despite the increasing importance of collaboration in achieving cost-related advantages for companies, existing studies lack a systematic framework for determining how multiple supply chains can collectively facilitate strategic decision-making. In this study, we propose a multi-network collaborative mixed integer programming model and a reinforcement learning enhanced evolutionary algorithm to optimize cross-enterprise supply chains. The model facilitates collaborative decision-making among supply chains of different enterprises by incorporating collaborative cost management, partner sharing, collaborative transportation, and horizontal logistics. Our algorithm integrates an adaptive reinforcement learning process and an evolutionary structure with multi-branch tree encoding, allowing for the effective accumulation of solving experiences in different states to enhance the efficiency and accuracy of the solving process. We conducted extensive experiments using real data collected from electric automotive manufacturing supply chains. The experimental results show that the obtained solution quality is close to optimal with negligible margin for both small- and large-scale instances. Overall, our proposed approach enables the joint optimization of cross-enterprise collaborative supply chains and holds the potential for improving supply chain management in various industries

Performance Evaluation of Performance Evaluation of Emergency Medical Service Systems with Multiple Ambulance Types and Patient Types

Emergency medical services (EMS) are an important part of the modern healthcare system that tries to provide timely medical care and transportation to patients to reduce morbidity and mortality. Performance evaluation of such EMS systems to determine measures such as mean service rates, dispatch probabilities, busy probabilities, and on-scene times is necessary to design effective and efficient systems. In this paper, we consider an urban EMS system that employs three types of emergency vehicles, including advanced life support (ALS), basic life support (BLS) and first responder vehicle (FRV). We consider two types of patients: type A requires ALS to be dispatched, while type B patients are expected to be served by BLS ambulances. We also consider co-located servers so ambulances of different types can be co-located at the same station. The presence of different types of servers (ambulances) and the patients with different dispatch policies, along with co-located servers, makes it applicable to a more realistic system. We first discuss a modification of the hypercube queueing model for the proposed system and then present an approximate approach for application in large EMS systems. These approaches are compared against a simulation-based model by computing server utilization, service times and on-scene time of ambulances

Navigating the Digital Evolution: I 4.0 Technologies and their Roles in Reconfiguring Dynamic Resource Management Networks

This research delves into the re-configuration of resource networks, focusing on the promising implications of Industry 4.0 technologies. These technologies, such as Artificial Intelligence (AI), Cloud-ERP Systems (CERP), Big Data Predictive Analytics (BDPA), Internet of Things (IoT), and Blockchain Adoption (BCA), are scrutinized for their potential to enhance efficiency and reconfiguration of resource networks. N=206 participants took part in the survey process. Data analysis based on Smart-PLS revealed some significant findings. AI found no statistical significance with dynamic resource network development; however, cloud findings. The AI found no statistical significance with dynamic resource network development; the cloud showed a significant relation. A significant relationship exists between using Big Data and IoT with dynamic resource network development. However, blockchain adoption (BCA) did not significantly impact resource networks. Overall, this study contributes to the theoretical area of dynamic capability, providing practical insights into the influence of Industry 4.0 technologies

Network Structure and Realization Path of Urban Green Sustainable Development Based on Improved Social Network Analysis Hybrid Method

Telecommunication is one of the essential necessities of everyday life. In India, the telecommunications sector has seen a significant increase in the day-to-day. Telecommunications service companies hold data about their customers, and crisp graphs are used to depict these records. Examining and selecting the best mobile phone service providers (MPSPs) based on operational restrictions will help determine the best MPSPs. The analysis of MPSPs may be regarded as a difficult decision-making issue. The aim of this article is to provide an outline to examine the performance of MPSPs and the selection of the best MPSP for customers in India. The statistical data were obtained from the Telecom Regulatory Authority of India between April 2019 and March 2021. A novel approach for cosine similarity measures (CSM) among hesitancy fuzzy graphs (HFG) and estimating the certified repute scores of the experts by determining the ambiguous information of hesitancy fuzzy preference relations (HFPRs) and the regular cosine similarity grades from one separable HFPR to some others. And consider “objective” and “subjective” information given by experts. According to CSMs, we define the Laplacian energy of an HFG. This research provides a solution to a decision-making problem by applying the newly developed cosine similarity measure and the Laplacian energy of hesitancy fuzzy graphs. The ranking order of all alternatives and the best one is determined by calculating the cosine similarity between each alternative and the ideal alternative. Finally, an illustrated example is provided to show the applicability of the proposed approach to the decision-making problem as well as its effectiveness

Using The Flexible Analytic Hierarchy Process Method to Solve The Emergency Decision-Making of Public Health Emergency of International Concern (PHEIC)

The occurrence of a public health emergency of international concern (PHEIC) can lead to massive deaths, economic recession, and changes in the lifestyles of people in various countries. Addressing the problem of a public health emergency involves multiple experts and criteria, making it a multi-expert and multi-criteria decision-making problem. The assessment information of the criteria simultaneously includes complete, incomplete and hesitant fuzzy linguistic information in PHEIC problems. However, typical calculation methods cannot process the incomplete information and hesitant fuzzy linguistic information associated with PHEIC problems. In order to overcome these issues, this paper proposed a novel flexible AHP method to solve PHEIC problems. A numerical case study on public health emergency decision-making for COVID-19 was adopted to verify the effectiveness and correctness of the proposed flexible analytic hierarchy process (AHP) method. The numerical simulation results were also compared with the simple additive weighting (SAW) method, the traditional AHP method, the fuzzy set method, and the fuzzy AHP method. The simulation results show that the proposed method can provide a more reasonable and flexible decision analysis

A Hybrid Deep Learning based Automatic Target Detection and Recognition of Military Vehicles in Synthetic Aperture Radar Images

ATR SAR Imagery is the major application in detection and recognition of military vehicles such as armored vehicles, tank, bulldozer, cannon etc. A robust method that employs Markov Random Field and hybrid Googlenet and VGGnet Convolutional Neural Networks (CNNs) have been proposed in this paper. The performance of Synthetic Aperture Radar (SAR) images is degraded by speckle noise and hence in the first module, we performed SAR image despeckling in order to reduce speckle noise in the images using the improved Adaptive Morphological filter. After despeckling, in the second module, the military vehicles or targets are detected from the despeckled images by Markov Random Field segmentation algorithm. Finally in the third module, hybrid Googlenet and VGGnet Convolutional Neural network with SVM classifier is adopted for classifying and recognizing the military targets from the SAR images.  The proposed  markov random field with the hybrid VGGnet and Googlenet (VGG-GoogleNet) pretrained Convolutional Neural Networks notably improves the recognition accuracy compared with the conventional deep learning CNN based methods

Assessing the Impact of Urban Morphology on Metro-Bicycle Sharing Transfers Using Random Forest Classification

The urban built environment shapes the city's morphology, which possesses the capacity to influence the use of bike-sharing systems. Bike-sharing offers a solution to the "first-last mile" problem associated with metro systems, providing flexible and cost-effective means to enhance transit accessibility and reduce travel expenses. This study employs a bike-sharing trajectory dataset to analyze usage patterns and integrates urban morphology—defined by land use, Points of Interest (POIs), and spatial clusters of transportation facilities—to determine if the urban form can affect cycling behavior. The findings reveal that, in addition to urban morphological factors, bike-sharing usage patterns exhibit strong classification performance. The misclassification rates were 0.3439 for departures and 0.2472 for arrivals. The difference in misclassification rates can be attributed to the diverse urban contexts surrounding metro stations. For instance, stations located in residential areas tend to have more predictable bike-sharing patterns, resulting in lower misclassification rates. In contrast, stations in commercial zones with higher land-use diversity and Points of Interest (POIs) exhibit more variability in cycling behavior, leading to higher error rates. The research demonstrates that the spatial characteristics of urban morphology—such as land-use diversity and clustering of POIs—play a pivotal role in influencing metro-bicycle sharing patterns. The model achieved an 83.5% accuracy rate in distinguishing between bike-sharing rides to or from metro stations. These findings underscore the integrated role of urban form in shaping travel behavior, especially regarding the synergy between metro systems and bicycle-sharing