Location Problem Research Articles

Decision-analytics-based Sustainable Location Problem - Neutrosophic CRITIC-COPRAS Assessment Model

School is the place where students get the opportunity to accrue knowledge and quality education. Choosing a suitable location for establishing a new school is dependent on various factors like population density, socio-economic situation, environment of that place, land availability, accessibility, infrastructure etc. In this paper, our aim is to identify the optimal site for establishing a new school in Paschim Bardhaman district using Multi Criteria Decision Making (MCDM) method. Here, two MCDM method namely Criteria Importance Through Intercriteria Correlation (CRITIC) method is used for evaluating criteria weight and further the Complex Proportional Assessment (COPRAS) method is applied for ranking the sites chosen as alternatives. We consider Intuitionistic fuzzy number to incorporate uncertainty in data set. Further sensitivity and comparative analysis are performed to verify the accuracy and stability of the result. Thus we obtain a framework which will be very helpful for urban planners and government policy makers to make informed decisions for educational development.

Implementation and Performance Comparison of Novel Optimization Approaches to Counter Starvation in Wireless Networks

Data packets in Wireless Mesh Networks (WMNs) are routed across several nodes in a multi-hop fashion. The Quality of Service (QoS), seamless connectivity, reliability, and scalability of Wireless Mesh Networks are all significantly impacted by routing approaches. Routing protocols should enforce the fair utilization of resources i.e. bandwidth or channel among network nodes irrespective of their spatial location from the Gateway. The two-hop or multi-hop nodes in wireless mesh networks experience resource starvation due to the functioning of the MAC protocol and TCP/TP networking protocol. The Starvation issue has a significant impact on the QoS requirements of wireless mesh networks. It is known that using appropriate scheduling techniques in network planning substantially minimizes starvation. To reduce the starving of resources to the multi-hop network nodes, novel optimized routing algorithms have been proposed and implemented in this work. To address the starvation, a GA-based cross-layer optimized scheduling method that operates at the MAC and Network layers is implemented. A hybrid approach that combines the features of the Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) is also implemented to solve the local minimum problem in GA. Results show that the suggested optimization methods greatly improve the fairness performance of wireless mesh networks.

Robust model for commercial complex location problem with uncertain customer thresholds

Robust model for commercial complex location problem with uncertain customer thresholds

Noise-immune zeroing neural dynamics for dynamic signal source localization system and robotic applications in the presence of noise

Time angle of arrival (AoA) and time difference of arrival (TDOA) are two widely used methods for solving dynamic signal source localization (DSSL) problems, where the position of a moving target is determined by measuring the angle and time difference of the signal's arrival, respectively. In robotic manipulator applications, accurate and real-time joint information is crucial for tasks such as trajectory tracking and visual servoing. However, signal propagation and acquisition are susceptible to noise interference, which poses challenges for real-time systems. To address this issue, a noise-immune zeroing neural dynamics (NIZND) model is proposed. The NIZND model is a brain-inspired algorithm that incorporates an integral term and an activation function into the traditional zeroing neural dynamics (ZND) model, designed to effectively mitigate noise interference during localization tasks. Theoretical analysis confirms that the proposed NIZND model exhibits global convergence and high precision under noisy conditions. Simulation experiments demonstrate the robustness and effectiveness of the NIZND model in comparison to traditional DSSL-solving schemes and in a trajectory tracking scheme for robotic manipulators. The NIZND model offers a promising solution to the challenge of accurate localization in noisy environments, ensuring both high precision and effective noise suppression. The experimental results highlight its superiority in real-time applications where noise interference is prevalent.

Investigations on Local Minimum Problem for Dual‐Quaternion‐Based Sliding Mode Control With Artificial Potential Functions

ABSTRACTThis paper investigates a sliding mode control law based on dual quaternions for six‐degree‐of‐freedom (six‐DOF) proximity operations between a chaser and a target spacecraft. Dual quaternions, which can simultaneously represent both the rotation and translation of rigid bodies, effectively model the coupled relative motion during these operations. The sliding surface design incorporates artificial potential functions, enabling the consideration of motion constraints including field‐of‐view restrictions, approach path constraints, and collision avoidance. However, these functions are prone to local minima, where attractive and repulsive forces balance each other out, leading to undesired convergence. This paper presents a comprehensive mathematical analysis of the local minimum problem, elucidating the impact of individual and combined constraints on the chaser behavior. To address the local minimum issue, an active escape mechanism is proposed to apply additional corrective forces to help the chaser overcome these traps and achieve reliable control. Numerical simulation evaluates the proposed solution, confirming its effectiveness in resolving local minima challenges and enhancing mission reliability.

An Overview Study of Multi-sensor Fusion for Map Building and Localization

This paper reviews the application of multi-sensor fusion in simultaneous localisation and map construction (SLAM) technology. With the development of robotics, autonomous driving and virtual reality, there is an increasing demand for precise localisation and map construction.SLAM technology has emerged to solve the problem of autonomous robot localisation and map construction in unknown environments. However, single-sensor SLAM systems have limitations, such as limited sensing capability and susceptibility to noise interference. Multi-sensor fusion SLAM significantly improves the performance and robustness of the system by integrating the advantages of multiple sensors. The multi-sensor fusion SLAM system includes key components such as sensor data reading, front-end visual odometry, back-end optimisation, loopback detection and map building. The sensor data is first optimised to reduce noise and then further processed according to the task requirements. Sensors are categorised into internal sensors (e.g. IMUs and wheeled odometers) and external sensors (e.g. cameras, LIDAR, UWB sensors, etc.). Common data fusion methods include filter-based fusion (e.g., Extended Kalman Filter, Particle Filter), optimisation-based fusion (e.g., Graph Optimisation, Nonlinear Least Squares) and deep learning-based fusion (e.g., Convolutional Neural Network, Recurrent Neural Network). These methods are able to handle different types of sensor data and improve the performance of SLAM systems. Multi-sensor fusion SLAM technology has a wide range of applications in fields such as robot navigation, autonomous driving, virtual reality, geographic information systems and mapping. In the future, the technology will pay more attention to the combination of multimodal fusion and deep learning, optimising the computational efficiency of the algorithms, improving the real-time and robustness of the system, as well as the fusion of cloud SLAM and edge computing, to promote the development and advancement of the related fields.

The location problem of emergency materials in uncertain environment

The location problem of emergency materials in uncertain environment

Ambulation Protocol for Adult Patients Receiving Extracorporeal Membrane Oxygenation: A Quality Improvement Initiative.

For patients receiving extracorporeal membrane oxygenation, early mobility decreases mechanical ventilation time, delirium incidence, and length of intensive care unit stay and improves physical functioning. Individual centers use institutional guidelines to develop ambulation protocols. Local Problem A quality improvement initiative was used to evaluate an ambulation protocol for adult intensive care unit patients receiving extracorporeal membrane oxygenation. Adult patients receiving extracorporeal membrane oxygenation who walked according to the protocol were compared with a historical control group of patients who walked without the protocol. Data analysis included descriptive statistics and independent t tests. Outcomes included adverse safety events, number of patients and ambulation sessions, standing and ambulation time, and distance. From January to March 2021, 13 of 46 patients receiving extracorporeal membrane oxygenation (28%) walked according to the protocol. In the control group, 14 of 147 patients (10%) walked in 2019; 21 of 144 patients (15%) walked in 2020. Some characteristics of the control group (hospitalized before the COVID-19 pandemic) differed from those of the protocol group (hospitalized during the pandemic). Mean number of ambulation sessions was not significantly different between groups (protocol group, 10; control group, 9). Differences in mean standing time (protocol group, 121.23 minutes; control group, 210.80 minutes), ambulation time (protocol group, 11.77 minutes; control group, 198.70 minutes), and ambulation distance were not significant. Standing time, ambulation time, and distance were not significantly different between the groups. The extracorporeal membrane oxygenation ambulation protocol demonstrated clinical significance by increasing the number of patients walking.

Fast EEG/MEG BEM-based forward problem solution for high-resolution head models.

A fast BEM (boundary element method) based approach is developed to solve an EEG/MEG forward problem for a modern high-resolution head model. The method utilizes a charge-based BEM accelerated by the fast multipole method (BEM-FMM) with an adaptive mesh pre-refinement method (called b-refinement) close to the singular dipole source(s). No costly matrix-filling or direct solution steps typical for the standard BEM are required; the method generates on-skin voltages as well as MEG magnetic fields for high-resolution head models within 90 s after initial model assembly using a regular workstation. The forward method is validated by comparison against an analytical solution on a spherical shell model as well as comparison against a full h-refinement method on realistic 1M facet human head models, both of which yield agreement to within 5 % for the EEG skin potential and MEG magnetic fields. The method is further applied to an EEG source localization (inverse) problem for real human data, and a reasonable source dipole distribution is found.

Synchronization of complex dynamical networks with saturated delayed impulsive control.

This paper considers the local exponential synchronization problem for a type of complex dynamical networks (CDNs) with both system delay and coupled delay using saturated delayed impulsive control. Based on the methods of average impulsive interval (AII), average impulsive delay (AID) and average impulsive estimation (AIE), a Razumikhin-type inequality with hybrid delayed impulses (which include delayed impulses and delay-free impulses) is derived. This inequality includes pure delayed impulsive inequalities. By utilizing the constructed inequality and the Lyapunov stability theory, the saturation nonlinearities are treated as the convex hulls, and sufficient synchronization criteria for local exponential synchronization of CDNs are presented in the framework of linear matrix inequalities (LMIs). Moreover, to enlarge the estimation of region of attraction (ROA) and the design of impulsive control gain, a convex optimization problem based on LMIs is formulated. Finally, a numerical example demonstrates the effectiveness of the obtained results.

LP-Rounding Based Algorithm for Capacitated Uniform Facility Location Problem with Soft Penalties

LP-Rounding Based Algorithm for Capacitated Uniform Facility Location Problem with Soft Penalties

Edge downgrades in the maximal covering location problem

Edge downgrades in the maximal covering location problem

The hub location problem with comparisons of compact formulations: A note

The hub location problem with comparisons of compact formulations: A note

Re-locative guided search optimized self-sparse attention enabled deep learning decoder for quantum error correction

Heavy hexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been presented, it is still difficult to construct the optimal decoder due to leakage errors and qubit collision. Therefore, this research proposes a Re-locative Guided Search optimized self-sparse attention-enabled convolutional Neural Network with Long Short-Term Memory (RlGS2-DCNTM) for performing effective error correction in quantum codes. The integration of the self-sparse attention mechanism in the proposed model increases the feature learning ability of the model to selectively focus on informative regions of the input codes. In addition, the use of statistical features computes the statistical properties of the input, thus aiding the model to perform complex tasks effectively. For model tuning, this research utilizes the RIGS nature-inspired algorithm that mimics the re-locative, foraging, and hunting strategies, which avoids local optima problems and improves the convergence speed of the RlGS2-DCNTM for Quantum error correction. When compared with other methods, the proposed RlGS2-DCNTM algorithm offers superior efficacy with a Minimum Mean Squared Error (MSE) of 4.26, Root Mean Squared Error of 2.06, Mean Absolute Error of 1.14 and a maximum correlation and of 0.96 and 0.92 respectively, which shows that the proposed model is highly suitable for real-time error decoding tasks.

Code and Data Repository for The Balanced Facility Location Problem: Complexity and Heuristics

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper The Balanced Facility Location Problem: Complexity and Heuristics by Malena Schmidt and Bismark Singh.

TRADITIONAL GOVERNANCE IN THE COPRODUCTION OF URBAN RESILIENCE: Institutional Enablers and Political Constraints

AbstractBuilding urban resilience in vulnerable global South settlements is a pressing twenty‐first century challenge. Building urban resilience involves addressing institutional deficiencies to mobilize resources for the delivery of urban services and infrastructures. State–civil society partnerships are effective in low‐capacity settings as a step forward in the consolidation of the state, and as an opportunity for the emancipation of vulnerable communities. Coproducing urban resilience requires recognizing marginalized communities (e.g. indigenous groups), their capacity for local problem solving and governance structures for community engagement. In this article, we explore a coalition of five periurban neighborhoods in Oaxaca City (Mexico), which collaborate with the state to address flooding and drought using traditional governance. We argue that, although the recognition and mobilization of traditional governance has enabled the coproduction of public services (adaptation), it has been limited in delivering radical governance transformations. Traditional governance may prevent neighborhood leaders from reaching government positions to secure further resources required for the construction of urban resilience. The article contributes to debates on coproduction, explaining how traditional governance enables the coproduction of infrastructures and service delivery, but is limited in forwarding deep societal transformations necessary for resilience building in vulnerable contexts of the global South.

Reviewing the quality of Environmental Impact Statements of Brazilian landfills: insights for promoting efficiency in final disposal

ABSTRACT Brazil advocates waste hierarchy in its solid waste policy by reducing landfills but still struggles to achieve adequate final disposal. This raises a huge demand for undertaking Environmental Impact Assessment (EIA) processes of landfills. To contribute to achieving efficiency in EIA processes, this work aims to review EIS quality of a representative set of Environmental Impact Statements (EISs) of landfills in the state of São Paulo (Brazil). The quality of a set of 18 landfill EISs prepared by 17 consultancies between 2010 and 2022 was analyzed using two assessment tools with qualitative and quantitative approaches. Both approaches confirm that this set of Brazilian EISs of landfills present good quality but highlight localized problems in specific EIS items. The good quality of this sample of EISs differs greatly from the recent achievements shared in the literature and a previous analysis of one Brazilian landfill EIS. These unexpected positive results of EIS quality can be related to the high experience of consultancies in waste management projects. These EISs reached a minimum quality requirement, and they are enough to support decision-making. The approach proposed herein can be applied to other contexts and used for environmental bodies to assess the quality of EIS.

Improving Timely Evaluation of Newborns at Risk for Hemolytic Disease From a Positive Maternal Antibody

Background: Hemolytic disease of the newborn (HDN) is a rare condition with the potential for high morbidity. Local Problem: Practice at a large maternity care center did not align with the American Academy of Pediatrics (AAP) revised hyperbilirubinemia guidelines. Methods: A pre/post-implementation design with Plan-Do-Study-Act cycles was used. Interventions: A clinical pathway was implemented to improve timely evaluation of newborns at risk for HDN due to a positive maternal antibody using cord blood. Results: Cord blood testing increased among the subset of newborns who did not meet collection criteria prior to the practice change (pre: 0% n = 7, post: 39% n = 23). Missed opportunities to test cord blood occurred less frequently post (35 versus 26 days between missed opportunities). Feasibility data identified the need for educational and clinical tools. Conclusions: A clinical pathway and tailored tools support the AAP recommendation. Electronic health record supports could bolster future efforts.

Out-of-Hospital Cardiac Arrest Ambulance Delay Zones and AED Placement in a Southern Brazilian City

Out-of-hospital cardiac arrests (OHCAs) have high mortality rates, worsened by limited access to automated external defibrillators (AEDs). This study analyzed OHCA response times, identified areas with prolonged ambulance travel times, and proposed optimal AED locations in a medium-sized city in southern Brazil. Data from 278 non-traumatic OHCA cases (2019–2022) in patients over 18 years old, with ambulance response times under 20 min, were included. Spatial survival analysis assessed the probability of exceeding the recommended 5-min (300 s) ambulance response time. The maximal covering location problem identified 100 strategic AED sites within a 150-s reach for bystanders. AED and ambulance travel times were compared using the Wilcoxon test (p < 0.01). Defibrillation occurred in 89 cases (31.01%), and bystander CPR was performed in 149 cases (51.92%). Despite these efforts, 77% of patients died. The median ambulance response time was 11.63 min, exceeding 5 min in most cases, particularly at peak times like 11 a.m. AED placement in selected locations could cover 76% of OHCA occurrences, with a mean AED travel time of 320 s compared to 709 s for ambulances. Strategic AED placement could enhance early defibrillation and improve survival outcomes.

Multiobjective model to optimize charging station location for the decarbonization process in Mexico

AbstractElectric vehicles (EVs) offer significant potential for advancing sustainable environmental goals. However, their widespread adoption has been concentrated in urban areas, raising challenges for interurban travel. In many countries, charging station networks are primarily located within cities, highlighting a key opportunity for expansion to support longer distance journeys. This article addresses the facility location problem for EV charging stations to enable interurban travel. We propose a multiobjective optimization model based on the flow refueling location model with three objectives: maximizing CO2 emissions reduction, minimizing total costs, and reducing user charging time. The model is solved using an epsilon constraint approach, and Mexico's charging station network is used as a case study. Through computational experiments, various scenarios are evaluated, and a comparative analysis is performed between electric and internal combustion vehicles. Results show that deploying 20 strategically located charging stations could mitigate 3.1 million tons of CO2, requiring an investment of nearly USD 3.9 million.