Location Problem Research Articles

Automated external defibrillator location selection considering myocardial infarction risk and medical resources

AbstractThe effective deployment of medical emergency equipment, such as automated external defibrillator (AED), is essential to myocardial infarction (MI) patients. However, there are shortcomings in current studies that simultaneously consider the risk of MI and the availability of medical resources when siting the AEDs. In this study, an AED site recommendation framework was proposed to address the lack of consideration for both the MI risk and medical resources when siting the AEDs. It conducts the AED sitting under different scenarios considering the spatial distribution of MI risk and healthcare accessibility in Chinese cities. First, an automated machine learning framework data is proposed to estimate the MI risk at the community scale based on multi‐source spatio‐temporal. Second, the accessibility of medical resources was calculated by an improved Gaussian two‐step moving search algorithm. Finally, the AED siting in multiple scenarios is conducted based on the coverage model. The performance of the AED siting model was evaluated at Wuhan city. The results show that MI risk is impacted by both socioeconomic and cultural characteristics (municipal utilities, streetscape environment, educational and commercial facilities). There is a strong spatial heterogeneity in the distribution of both MI risk and medical resources in Wuhan, and an unreasonable match between the two was detected in some regions. Medical resources need to be strengthened in some high‐risk areas, such as rural areas and tourist attractions. In addition, 1015 AED candidate sites were identified by the location set covering problem model, with a 15‐min accessibility rate of 96.5%. Given the limited resources, mobile AEDs which have about 15‐min service range can be deployed based on the maximum covering location problem model to meet the demand in central urban areas efficiently. This study can contribute to more rational selection of AED sites and the prevention of myocardial infarction among residents, particularly when supported by policies that promote balanced regional development of pre‐hospital medical emergency networks.

Aerial Map-Based Navigation by Ground Object Pattern Matching

This paper proposes a novel approach to map-based navigation for unmanned aircraft. The proposed approach employs pattern matching of ground objects, not feature-to-feature or image-to-image matching, between an aerial image and a map database. Deep learning-based object detection converts the ground objects into labeled points, and the objects’ configuration is used to find the corresponding location in the map database. Using the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern-matching problem. The pattern-matching algorithm proposed in this paper does not require altitude information or a camera model to estimate the horizontal geographical coordinates of the vehicle. Moreover, it requires significantly less storage because the map database is represented as a set of tuples, each consisting of a label, latitude, and longitude. Probabilistic data fusion with the inertial measurements by the Kalman filter is incorporated to deliver a comprehensive navigational solution. Flight experiments demonstrate the effectiveness of the proposed system in real-world environments. The map-based navigation system successfully provides the position estimates with RMSEs within 3.5 m at heights over 90 m without the aid of the GNSS.

Non-Line-of-Sight Positioning Method for Ultra-Wideband/Miniature Inertial Measurement Unit Integrated System Based on Extended Kalman Particle Filter

In the field of unmanned aerial vehicle (UAV) control, high-precision navigation algorithms are a research hotspot. To address the problem of poor localization caused by non-line-of-sight (NLOS) errors in ultra-wideband (UWB) systems, an UWB/MIMU integrated navigation method was developed, and a particle filter (PF) algorithm for data fusion was improved upon. The extended Kalman filter (EKF) was used to improve the method of constructing the importance density function (IDF) in the traditional PF, so that the particle sampling process fully considers the real-time measurement information, increases the sampling efficiency, weakens the particle degradation phenomenon, and reduces the UAV positioning error. We compared the positioning accuracy of the proposed extended Kalman particle filter (EKPF) algorithm with that of the EKF and unscented Kalman filter (UKF) algorithm used in traditional UWB/MIMU data fusion through simulation, and the results proved the effectiveness of the proposed algorithm through outdoor experiments. We found that, in NLOS environments, compared with pure UWB positioning, the accuracy of the EKPF algorithm in the X- and Y-directions was increased by 35% and 39%, respectively, and the positioning error in the Z-direction was considerably reduced, which proved the practicability of the proposed algorithm.

Nested evolutionary algorithms for solving a bi-level warehouse location problem that considers inventory decisions

Nested evolutionary algorithms for solving a bi-level warehouse location problem that considers inventory decisions

Hybrid deconvolution method based on mode composition beamforming for separating sound sources with different motion modes

The presence of sound sources that rotate can cause interference with stationary acoustic imaging, especially if the intensity of the rotating sound source is strong. Vice versa, stationary sound sources can also interfere with rotational acoustic imaging. Current algorithms for separating and locating rotating and stationary sound sources have problems such as weak separation capabilities, complex calculations, and limited array layouts. To address these challenges more effectively, a frequency-domain hybrid deconvolution method based on modal composition beamforming is proposed to separate and localize rotating and stationary sound sources. This algorithm operates within the frequency domain and is not restricted by array layout or the tonal single-frequency nature of sound sources. First, the point spread function and cross-point spread function matrices of the array are derived based on the modal transfer function. Then, a system of linear equations is constructed to separate rotating and stationary sound sources. Finally, the sound source separation and localization problems are solved through Gaussian Seidel and sparse-constrained deconvolution, respectively. Compared with the virtual rotating array method combined with matrix completion, simulation and experimental results demonstrate that this method can accurately locate and separate rotating and stationary sound sources, even when there is a substantial difference in intensity between rotating and stationary sound sources.

Recurrent DQN for radio fingerprinting with constrained measurements collection

In this paper, we address the problem of fingerprinting-based radio localization with a particular focus on the measurements collection part. We consider the crucial circumstance where the operator that builds the fingerprinting map by collecting measurements can only travel a limited distance. We propose an iterative formulation that increases the accuracy of the position prediction task by using a recurrent deep reinforcement learning algorithm. Numerical results on a real dataset show the effectiveness of the proposed method, and the comparison with other measurement collection strategies corroborates its value.

Local vs. federated cooling control for an office space with heat pump and photovoltaic systems

Most of existing heat pump controls have been treated as local control problems where their control actions are solely based on local information, e.g. indoor air temperature. This study presents a real-life online federated control that can account for the interconnected dynamic behaviors in outdoor environment, an indoor thermal zone, and HP systems assisted by PV systems. The in-house federated simulation model consists of three artificial neural networks (ANNs) that predict dynamic behavior of the thermal zone temperature, HP energy consumption, and electricity generated by the PV, respectively. In other words, the dynamic inter-influences of the three systems are taken into consideration in the federated model where the time-varying input-outputs are cross-related. As a result, the federated model predictive control (MPC) demonstrated net energy saving by 40.4% compared to the local control over a week-long real-time implementation (local: 7,138 Wh vs. federated: 4,254 Wh). This study showcases how to federate the aforementioned entangled dynamic interactions between different dynamic systems for better control strategies. In other words, energy saving potential of multi-dynamic systems, e.g. room-HP-PV, would not be properly recognized unless they are federated. In addition, this study also emphasizes variations in the effectiveness of the federated control.

Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration

This study offers a new swarm-based metaheuristic: random-guided optimizer (RGO). RGO has novel mechanics in shifting the random motion into a guided motion strategy during the iteration. In RGO, the iteration is divided into three equal size phases. In the first phase, the unit walks randomly inside the search space to tackle the local optimal problem earlier. In the second phase, each unit uses a unit selected randomly among the population as a reference in conducting the guided motion. In the third phase, each unit conducts guided motion toward or surpasses the best unit. Through simulation, RGO successfully finds the acceptable solution for 23 benchmark functions. Moreover, RGO successfully finds the global optimal solution for four functions: Branin, Goldstein-Price, Six Hump Camel, and Schwefel 2.22. RGO also outperforms slime mold algorithm (SMA), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO) in solving 12, 20, 12, and 1 function consecutively. In the future, improvement can be made by transforming RGO into solid multiple-phase strategy without losing its identity as a metaheuristic with multiple strategy in every iteration.

Mathematical formulations for the multi-period alternative fuel refueling station location problem with routing under decision-dependent flow dynamics

The refueling station location problem with routing considers vehicles’ ranges and drivers’ preferences about their routes to improve the alternative fuel station infrastructure. Comprehensive planning is necessary for developing a mature infrastructure to overcome budgetary constraints and spatial limitations. Hence, adopting a multi-period planning approach becomes crucial when taking into account the evolving demand for alternative fuel vehicles over time. The evolution of demand can be dependent on exogenous and endogenous factors. Although it is typical to account for exogenous demand growth in multi-period planning, a few studies also take into account an endogenous factor which is the refueling opportunity of drivers on their paths. In this study, in addition to the refueling opportunities, we consider the proximity of each individual station to the flow-based demands. We draw attention to the significance of considering the effects of individual station locations on demand evolution, as these strategic locations can play an important role in reducing the drivers’ range anxiety and increasing their acceptance of the technology. Hence, we introduce a multi-period alternative fuel refueling station location problem with routing under different vehicle flow evolution dynamics that employ various weights for the factors where the natural growth rate is exogenous and the decisions of station locations and flow coverage are endogenous to the problem. We propose three mixed integer linear programming formulations for different evolution dynamics. We carry out computational experiments on the real road networks of Belgium, California, and Turkey and present our findings on the performances of the proposed mathematical models and the gains that can be obtained by considering multi-period planning and incorporating the effects of decisions into the vehicle flow evolution.

Research on fault location method of active distribution network based on improved multiverse algorithm

Abstract After the distributed power supply is connected to the distribution network, the network structure becomes more complex, and the fault characteristics change greatly compared with the traditional distribution network fault, so the original traditional fault location method is not satisfactory. This paper presents a fault location method for active distribution networks based on improved multi-node optimization (IMVO). According to the fault characteristics of the distribution network, the multiverse optimization algorithm is binary coded, and the algorithm parameters are improved to optimize the iterative process and enhance the performance of the algorithm. The simulation results show that this method can effectively solve the fault location problem of active distribution networks and has good advantages.

Cosserat constitutive theory and one of its higher-order forms: A rediscussion on the mesh dependence problem

When the finite element method (FEM) is adopted for studying strain localization problems, the mesh dependence phenomenon often ensues. The occurrence of mesh dependency will reduce the reliability of FEM simulations, so it is still worth studying. Herein, a constitutive model with decent mesh stability named the multiscale Cosserat (MC) model which contains higher-order rotation variables based on the conventional Cosserat (CC) theory, was introduced. The theory derivation indicates that the MC model has an extra internal length scale vector Dq that can consider the microscopic geometrical characteristics of the simulated material and can easily regress to the conventional Cosserat model when Dq = 0. After revisiting the mesh dependence problem through numerical simulations of plane strain compression tests, the mechanisms and advantages of the CC and MC models in solving the mesh dependence problem were discussed. The analysis demonstrated that the CC theory can alleviate the mesh dependence problem but cannot eliminate it; when the divergence of the computation occurs, due to a stricter accuracy requirement for convergence, the computation result of the MC model tends to stabilize along with the refinement of the elements. The mesh advantage of the MC model is influenced by both the length scales l and Dq. This study can provide new insight into understanding the mesh dependence problem, and the MC model introduced here is a potential model for comprehensively eliminating the influence of mesh dependence problems.

In pursuit of happiness: motivations for urban to rural migration in Turkey

ABSTRACT In recent years, there has been a growing global trend in which people seek to establish a simpler, slower, and more sustainable lifestyle away from the pressures of city life. This research analyses globally shared social, political, and planetary concerns, with a specific focus on the urban to rural migration phenomenon in Turkey. The project is based on participatory field research over a 12-month period with individuals who have made life decisions to quit their middle-class jobs to settle in small towns and villages in the West and South-West coastal parts of Turkey to engage in natural and ecological farming. Motivated by the pursuit of a meaningful life in the midst of the happiness and wellness industry intensified by social media, this study examines the key motivations behind migration to rural Turkey. It argues that although these motivations may seem personal, they are entangled with national, local, and global problems that push individuals to migrate to rural areas.

Customized Bus Stop Location Model Based on Dual Population Adaptive Immune Algorithm

Selecting optimal locations for customized bus stops is crucial for enhancing the adoption rate of customized bus services, reducing operational costs, and, consequently, mitigating traffic congestion. This study leverages ride-hailing data to analyze the distance between passengers and bus stops, as well as the operational costs associated with establishing these stops, to construct a customized bus stop location model. To address the limited local search capability of conventional immune algorithms, we propose a Dual Population Adaptive Immunity Algorithm (DPAIA) to solve the bus stop location problem. Finally, we conduct simulation experiments using passenger travel data from a ride-hailing company in Chengdu to evaluate the proposed customized bus stop location model. Through simulations with Chengdu ride-hailing data, the DPAIA algorithm minimized the weighted cost to CNY 28.95 ten thousand, outperforming all counterparts. Although proposing 9–11 more stops than competitors, this increase slightly impacts costs while markedly reducing passenger walking distances. Optimizing station placement to meet demand and road networks, our model endorses 50 strategic bus stops, enhancing service accessibility and potentially easing urban congestion while boosting operator profits.

Facility Location Problem: Modeling Joint Disruptions Using Subordination

We study the facility location problem with disruptions where the objective is to choose a set of locations that minimizes the sum of expected servicing and setup costs. Disruptions can affect multiple locations simultaneously and are caused by multiple factors like geography, supply chain characteristics, politics, and ownership. Accounting for the various factors when modeling disruptions is challenging due to a large number of required parameters, the lack of calibration methodologies, the sparsity of disruption data, and the number of scenarios to be considered in the optimization. Because of these reasons, existing models neglect dependence or prespecify the dependence structures. Using partially subordinated Markov chains, we present a comprehensive approach that starts from disruption data, models dependencies, calibrates the disruption model, and optimizes location choices. We construct a metric and a calibration algorithm that learns from the data the strength of dependence, the number of necessary factors (subordinators), and the locations each subordinator affects. We prove that our calibration approach yields consistent estimates of the model parameters. Then, we introduce a variant of the standard approach to the underlying optimization problem, which leverages partially subordinated Markov chains to solve it quickly and precisely. Finally, we demonstrate the efficacy of our approach using twelve different disruption data sets. Our calibrated parameters are robust, and our optimization algorithm performs better than the simulation-based algorithm. The solutions from our model for disruptions have lower costs than those from other disruption models. Our approach allows for better modeling of disruptions from historical data and can be adapted to other problems in logistics, like the hub location, capacitated facility location, and so on., with joint disruptions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0103 .

On bias and its reduction via standardization in discretized electromagnetic source localization problems

In electromagnetic source localization problems stemming from linearized Poisson-type equation, the aim is to locate the sources within a domain that produce given measurements on the boundary. In this type of problem, biasing of the solution is one of the main causes of mislocalization. A technique called standardization was developed to reduce biasing. However, the lack of a mathematical foundation for this method can cause difficulties in its application and confusion regarding the reliability of solutions. Here, we give a rigorous and generalized treatment for the technique using the Bayesian framework to shed light on the technique’s abilities and limitations. In addition, we take a look at the noise robustness of the method that is widely reported in numerical studies. The paper starts by giving a gentle introduction to the problem and its bias and works its way toward standardization.

Location Problem in Relativistic Positioning: Relative Formulation

A relativistic positioning system is a set of four emitters broadcasting their proper times by means of light signals. The four emitter times received at an event constitute the emission coordinates of the event. The covariant quantities associated with relativistic positioning systems are analysed relative to an observer in Minkowski space-time by splitting them in their relative space-like and time-like components. The location of a user in inertial coordinates from a standard set of emission data (emitted times and satellite trajectories) is solved in the underlying 3+1 formalism. The analytical location solution obtained by Kleusberg for the GPS system is recovered and interpreted in a Minkowskian context.

A Model-Free Approach for Solving Choice-Based Competitive Facility Location Problems Using Simulation and Submodularity

This paper considers facility location problems in which a firm entering a market seeks to open facilities on a subset of candidate locations so as to maximize its expected market share, assuming that customers choose the available alternative that maximizes a random utility function. We introduce a deterministic equivalent reformulation of this stochastic problem as a maximum covering location problem with an exponential number of demand points, each of which is covered by a different set of candidate locations. Estimating the prevalence of these preference profiles through simulation generalizes a sample average approximation method from the literature and results in a maximum covering location problem of manageable size. To solve it, we develop a partial Benders reformulation in which the contribution to the objective of the least influential preference profiles is aggregated and bounded by submodular cuts. This set of profiles is selected by a knee detection method that seeks to identify the best tradeoff between the fraction of the demand that is retained in the master problem and the size of the model. We develop a theoretical analysis of our approach and show that the solution quality it provides for the original stochastic problem, its computational performance, and the automatic profile-retention strategy it exploits are directly connected to the entropy of the preference profiles in the population. Computational experiments on existing and new benchmark sets indicate that our approach dominates the classical sample average approximation method on large instances of the competitive facility location problem, can outperform the best heuristic method from the literature under the multinomial logit model, and achieves state-of-the-art results under the mixed multinomial logit model. We characterize a broader class of problems, which includes assortment optimization, to which the solving methodology and the analyses developed in this paper can be extended. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This research was supported by Fonds de Recherche du Québec-Nature et Technologies and Institut de Valorisation des Données through scholarships to R. Legault. E. Frejinger was partially supported by the Canada Research Chair program [Grant 950-232244]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/ijoc.2023.0280 .

On Some families of Path-related graphs with their edge metric dimension

Locating the origin of diffusion in complex networks is an interesting but challenging task. It is crucial for anticipating and constraining the epidemic risks. Source localization has been considered under many feasible models. In this paper, we study the localization problem in some path-related graphs and study the edge metric dimension. A subset LE⊆VG is known as an edge metric generator for G if, for any two distinct edges e1,e2∈E, there exists a vertex a⊆LE such that d(e1,a)≠d(e2,a). An edge metric generator that contains the minimum number of vertices is termed an edge metric basis for G, and the number of vertices in such a basis is called the edge metric dimension, denoted by dime(G). An edge metric generator with the fewest vertices is called an edge metric basis for G. The number of vertices in such a basis is the edge metric dimension, represented as dime(G). In this paper, the edge metric dimension of some path-related graphs is computed, namely, the middle graph of path M(Pn) and the splitting graph of path S(Pn).

Renormalization group analysis of the Anderson model on random regular graphs

We present a renormalization group (RG) analysis of the problem of Anderson localization on a random regular graph (RRG) which generalizes the RG of Abrahams, Anderson, Licciardello, and Ramakrishnan to infinite-dimensional graphs. The RG equations necessarily involve two parameters (one being the changing connectivity of subtrees), but we show that the one-parameter scaling hypothesis is recovered for sufficiently large system sizes for both eigenstates and spectrum observables. We also explain the nonmonotonic behavior of dynamical and spectral quantities as a function of the system size for values of disorder close to the transition, by identifying two terms in the beta function of the running fractal dimension of different signs and functional dependence. Our theory provides a simple and coherent explanation for the unusual scaling behavior observed in numerical data of the Anderson model on RRG and of many-body localization.

Neural network learning and Quantum Gravity

The landscape of low-energy effective field theories stemming from string theory is too vast for a systematic exploration. However, the meadows of the string landscape may be fertile ground for the application of machine learning techniques. Employing neural network learning may allow for inferring novel, undiscovered properties that consistent theories in the landscape should possess, or checking conjectural statements about alleged characteristics thereof. The aim of this work is to describe to what extent the string landscape can be explored with neural network-based learning. Our analysis is motivated by recent studies that show that the string landscape is characterized by finiteness properties, emerging from its underlying tame, o-minimal structures. Indeed, employing these results, we illustrate that any low-energy effective theory of string theory is endowed with certain statistical learnability properties. Consequently, several local learning problems therein formulated, including interpolations and multi-class classification problems, can be concretely addressed with machine learning, delivering results with sufficiently high accuracy.