Location Problem Research Articles

Corrosion detection and localisation utilising a novel thin film guided wave sensor sparse array

Detecting and pinpointing corrosion-related damage in infrastructure poses significant financial and technical difficulties. It is simply not feasible to manually inspect all infrastructure for corrosion. Permanently installed systems can monitor critical failure points without the need for manual scanning but these are not always known. Ultrasonic guided wave technology can be used for full system coverage, however, there is a trade-off between range and resolution. This has found a place for long distance monitoring of significant defects, however high- resolution systems require too dense a network to be feasible to install, cost and maintain. This research seeks a middle ground between these two options by employing machine learning and a scalable setup of easy to install, printed detection sensors. A flexible array of printed thin film sensors is placed around two identical mild steel pipes, each 0.5m long with a 6 mm wall thickness and 115 mm outer diameter. Corrosion is then induced artificially in one sample. Two transmit transducers on the top and bottom of the pipes are used to emit an ultrasonic, circumferential guided wave. The transmitted waveform was then received on all sensors throughout the array for data collection. Through machine learning and a search algorithm, sub-millimetre resolution is achieved, detecting millimetre-scale corrosion and localisation to the nearest detection sensor. Crucially, the system maintains scalability for large-scale asset inspection. This technology shows promise in addressing inspection challenges, including Corrosion Under Pipe Supports (CUPS) and Corrosion Under Insulation (CUI). It offers a potential solution to the costly problem of infrastructure damage detection and localisation while retaining scalability and a low-profile design philosophy.

Robust optimisation for vertiport location problem considering travel mode choice behaviour in urban air mobility systems

Robust optimisation for vertiport location problem considering travel mode choice behaviour in urban air mobility systems

Map-based localization for intelligent vehicles based on fusion of multiple visual features in underground parking

The ‘last mile’ problem in intelligent car localization within underground parking lots presents challenges such as localization blind spots and feature extraction difficulties. To tackle these issues, this paper proposes a mapping and multiscale matching localization method based on the fusion of multiple feature sources using a ‘binocular mapping, monocular localization’ approach. By combining traditional features with deep learning features, the proposed method enhances feature matching accuracy and addresses feature extraction challenges. A sparse three-dimensional reconstruction method based on local feature-optical flow is also proposed to mitigate localization blind spots. During the mapping process, the underground parking lot scene is segmented into several nodes to store map information, each consisting of a feature layer, scene structure layer, and trajectory layer. In the multiscale matching localization process, ORB, LoFTR, and BoW multisource features are fused to achieve feature matching, determine the nearest node, and estimate vehicle pose using the PnP model through optical flow. Finally, a localization optimization algorithm outputs the localization result. Experiments were conducted across multiple underground parking lots encompassing a total area of over 100,000 square meters. The results demonstrated that the average error of map construction using the proposed algorithm is approximately 15 mm, and the average error rate of localization is 4.45%.

Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar

Battlefield surveillance radar is usually 2-D radar, which cannot realize target three-dimensional localization, leading to poor resolution for the air target in the elevation dimension. Previous researchers have used the Traditional Height Finder Radar (HFR) or multiple 2-D radar networking to estimate the target three-dimensional location. However, all of them face the problems of high cost, poor real-time performance and high requirement of space–time registration. In this paper, Reconfigurable Intelligent Surfaces (RISs) with low cost are introduced into the 2-D radar to realize the target three-dimensional localization. Taking advantage of the wide beam of 2-D radar in the elevation dimension, several Unmanned Aerial Vehicles (UAVs) carrying RISs are set in the receiving beam to form multiple auxiliary measurement channels. In addition, the traditional 2-D radar measurements combined with the auxiliary channel measurements are used to realize the target three-dimensional localization by solving a nonlinear least square problem with a convex optimization method. For the proposed RIS-assisted target three-dimensional localization problem, the Cramer–Rao Lower Bound (CRLB) is derived to measure the target localization accuracy. Simulation results verify the effectiveness of the proposed 3-D localization method, and the influences of the number, the positions and the site errors of the RISs on the localization accuracy are covered.

P-facility capacitated location problem with customer-equilibrium decisions: a recreational case study in Mazandaran province

Purpose This paper aims to propose a bi-level mixed integer linear location-allocation problem. The upper-level objective function is dedicated to minimizing the total distances covered by customers to meet the p-selected facilities and the fixed cost values for establishing these facilities. While in the lower level, a customer preference function evaluates the priority of customers in selecting facilities. Design/methodology/approach The solution approach to the proposed model uses the Karush–Kuhn–Tucker (KKT) optimality conditions to the lower-level problem where a set of p-selected facilities are introduced as the selection of the upper-level decision maker. The bi-level model reduces to a single-level model with some added binary variables. Findings Sensitivity analysis of the proposed bi-level model concerning variations of such different parameters as customers’ preferences and the number of selected facilities have been provided, using some numerical examples. Also, locating a recreational facility in Mazandaran province, Iran, has been provided to evaluate the reliability of the proposed model and efficiency of the solution approach, as well. Originality/value To the best of the authors’ knowledge, this paper is original and its findings are not available elsewhere.

Constructing climate change: Exploring how cities frame climate change in the Arctic

ABSTRACT Framing climate policy actions to be acceptable by various stakeholders in cities poses a critical task for urban governance. Despite the proliferation of studies on urban climate change governance in the past decades, framing as a mechanism for agenda-setting in local policymaking has received little attention. This paper draws on the literature on climate change discourse to analyze the content of framing and its reasoning in the two municipalities located in the Arctic: Murmansk (Russia) and Tromsø (Norway). Using qualitative research methods, including semi-structured interviews, document reviews, and discourse analysis, the paper finds that climate change is absent in the political discourse in Murmansk. At the same time, Tromsø authorities recognize climate change as a global and a local problem and incorporate it into local policies. It also identifies three factors that explain the (non)framing of climate change by the municipal authorities: (1) local authority to frame climate issues, (2) local political party branches’ climate preferences, and (3) prior experiences with weather extremes and scientific knowledge. Given that cities in the Arctic are on the front lines of climate change, learning from their framing experiences, other cities across the globe can seize the opportunities for their efforts to address climatic impacts.

Round-trip hub location problem

In this paper, we introduce a novel network design for the Hub Location Problem, inspired by the round-trip structure commonly used by transport service providers. Our design integrates spoke nodes assigned to a central hub node, creating round-trips where the hub node serves as the starting point, visits all assigned spoke nodes, and returns to the hub. To enhance transportation services and provide additional redundancy, we introduce a new type of nodes called runaway nodes to the network. The motivation for this research arises from two real-life cases encountered during consultancy projects, underscoring the necessity for an optimized network design in transportation services. To address the proposed problem, we introduce a mixed-integer linear programming (MIP) mathematical model. However, due to the problem's complexity, the feasibility of the MIP model is limited to small-scale instances. To tackle medium and large-scale instances, we introduce two hyper-heuristic approaches based on reinforcement learning. These hyper-heuristic approaches harness the power of reinforcement learning to guide the selection of low-level heuristics and improve solution quality. We conduct extensive computational experiments to evaluate the efficiency and effectiveness of the proposed approaches. The results of our experiments affirm the efficiency of the proposed hyper-heuristic approaches, showcasing their ability to discover high-quality solutions for the Hub Location Problem.

Coordinated Control of the Onboard and Wayside Energy Storage System of an Urban Rail Train Based on Rule Mining

AbstractThere are three major challenges to the broad implementation of energy storage systems (ESSs) in urban rail transit: maximizing the absorption of regenerative braking power, enabling online global optimal control, and ensuring algorithm portability. To address these problems, a coordinated control framework between onboard and wayside ESSs is proposed in this study, and the related control strategy is obtained by transforming the global optimization problem into a combination of rule-based and local optimization problems. The design process of the strategy is divided into three parts: offline optimization, rule extraction, and local optimization. First, a genetic algorithm is used to optimize the charge/discharge threshold curves under typical operational conditions. Then, the rules of offline optimization results are obtained from an expert system, and the nonlinear rules are mined with local optimization methods. Finally, a coordinated strategy is presented based on the outcomes of the three parts. A power hardware-in-the-loop (PHIL) experimental platform based on RTLAB is built, and the above coordination strategy is verified based on data for the Beijing Metro Batong Line. The experimental results show that the strategy can effectively improve the energy-saving rate and reduce the regeneration failure rate substantially, with the effect being close to the offline global optimal solution. The algorithm proposed in this paper achieves near global optimal energy-saving optimization results with lower computational costs, and has strong portability, providing a good solution for the large-scale application of onboard and wayside energy storage coordination control.

An improved algorithm for solving the Weber location problem

An improved algorithm for solving the Weber location problem

Accurate calculation method of terrain viewshed for wireless signal as line of sight

AbstractViewshed analysis is an important research content of digital terrain analysis. The terrain viewshed refers to the range that can be seen at the current position, which varies with the nature of the observer. When the observer is a wireless signal tower, it is the communication viewshed, which refers to the area consisted of grid cells where the receiving antennas can receive the signals from a transmitting antenna set up on a grid cell of terrain. The core of base station location problem includes two aspects: combinatorial optimization and the calculation of the coverage rate of signal. The calculation of communication viewshed is an important research content for determining signal coverage range. In this article, we propose an accurate communication viewshed computation algorithm for wireless signal (CVCWS) using the projection curve of 3D Fresnel zone analysis based on DEM. The CVCWS method can calculate the signal reception quality at different locations more precisely. Besides, a signal attenuation model is proposed to compute the theoretical attenuation value according to the signal receiving quality index. The proposed algorithm is compared with the existing DEM‐based communication viewshed algorithms, and the theoretical attenuation values are compared with the measured values. The experimental results show that the theoretical values gained by the CVCWS algorithm are close to the measured values, indicating high accuracy of the CVCWS algorithm. The proposed method can provide theoretical support for communication tower location planning and other related applications.

Assessment of superconducting and structural stability of advanced Y-123 and Y-358 ceramics with Tb/Y substitution in main matrices

In this study, the performances of Y1-x(Tb)xBa2Cu3O7-δ (Y-123) and Y3-x(Tb)xBa5Cu8O18-δ (Y-358) advanced ceramics prepared by sol-gel preparation route are investigated by the advanced characterization methods including X-ray diffraction (XRD), temperature dependent resistivity (ρ-T), vibrating sample magnetometry (VSM), scanning electron microscopy (SEM) and energy distribution spectroscopy (EDS) measurements and theoretical approaches. The EDS results illustrated that the Tb impurities were replaced successfully by the yttrium sites in the YBa2Cu3O7-y main matrix. Similarly, it was noted that the presence of excess impurities in the crystal structure led to the oxygenation problems. Electrical properties were determined by temperature dependent resistivity (ρ-T) measurements. ρ-T results indicated that Tb doping reduced the critical transition temperature (Tc) values for the transition to superconductivity for all samples in two YBCO phases. Although it appears to have negative effects of Tb ions on the number of mobile charge carrier concentrations in the σ antibonding in-plane Cu-O bonds, bipolaron formations in the polarizable lattices, oxidation states, and amplitude of the pair wave function of both phases, the other valuable properties (morphology, crystallinity quality, interaction between adjacent layers, critical current density, and flux pinning ability) were noted to improve remarkably in the case of the optimum amount in the Y-123 superconducting phase. In this context, the Y-358 superconducting phase was harshly affected by the substitution due to the change of the ortorombicity, localization problem, impurity phases, structure stabilization, crystal structure quality, crystallization system, homogeneities of oxidation states, oxygen ordering degree, and impurity scatterings. Besides, the analysis of the fluctuation induced conductivity indicated that the Y-123 ceramics with longer c-axis coherence length and less anisotropic nature exhibited well superconducting properties. Additionally, the doping ratio of x=0.01 led to the formation and distribution of more nucleation centers for the thermal fluxon motions of 2D pancake vortices. Similarly, the optimum Tb doped Y-123 system exhibited much durable to applied field strengths due to the best interaction quality between grains. Accordingly, this study recommended that the Y1-x(Tb)xBa2Cu3O7-δ advanced ceramic structure with new functionalities can find much more application areas in innovative, heavy-industrial technologies, and advanced engineering-related sectors.

Location strategy for logistics distribution centers utilizing improved whale optimization algorithm

Abstract An enhanced whale optimization algorithm is introduced to address the challenges associated with the logistics distribution center location problem, aiming to overcome the traditional heuristic algorithm’s susceptibility to local optima and enhance the overall efficiency of the logistics system. This improvement involves the integration of stochastic sinusoidal inertia weights and a variational strategy to augment the global search capability and convergence accuracy of the conventional algorithm. The results of experimental simulations indicate that the upgraded whale algorithm exhibits superior computational performance compared to other heuristic algorithms. Furthermore, it demonstrates the ability to effectively compute optimal distribution center locations, thereby enhancing the delivery efficiency of the logistics distribution system.

A Hierarchical Adaptive Selection Neural Network for Dynamic Impact Localization of Wind Turbine Blades

Abstract This paper focuses on the localization problem of dynamic impacts that can lead to significant damages on wind turbine blades (WTBs). Localization of dynamic impacts on WTBs is essential for wind turbines (WTs) due to their vulnerability to dynamic impacts such as birds, stones, hails. The proposed deep learning methodology contributes to accurately locate the impacted blade and specific position using the measuremnts from a limited number of sensors. In particular, a novel hierarchical adaptive selection neural network (HASNN) is proposed, which integrates a classification subnetwork and a regression subnetwork. Specifically, an adaptive blade selection mechanism (ABSM) is designed to determine the impacted blade for classification while an adaptive window selection mechanism (AWSM) is developed to highlight the representative time period for regression. By deploying a limited number of sensors to acquire measured vibration data, the proposed method can accurately identify the collision locations of transient impacts loaded on WTBs. In both simulated and real-world experiments, the proposed method achieves the mean absolute error of 0.189 centimeter and 1.088 centimeter for impact localization. The experimental results demonstrate the superior performance of the proposed model in comparison with the existing methods for localizing impulsive loads on WTBs.

Optimal station placement based on grey wolf optimizer for regional target localization

The accuracy of target passive localization is influenced by the placement of signal receiving stations; therefore, many studies have been performed to optimize station placement. However, most of the present placement methods focus on the localization error of one target, and if the exact position of the target cannot be determined, but only the range of the target activity is known, how to study the localization station placement in a region is a problem that needs to be solved. This paper proposes a grey wolf optimization algorithm based on the regional target error model to solve the optimal station placement problem. Firstly, a regional target localization error model is established using the measured TDOA, and the overall error matrix within a region is derived. Then, by taking the trace of the error matrix as a criterion, the objective function is established to find the optimal location of the receiving station by grey wolf optimizer. The optimization parameters are also improved to increase the global search ability of the algorithm. Finally, the feasibility and reliability of the overall error model and the grey wolf algorithm proposed are verified by experiments from multiple perspectives. The station placement method proposed in this paper can effectively solve the localization problem of targets that are only known to be in a general activity region in advance, which is more realistic.

An efficient estimator for source localization in WSNs using RSSD and TDOA measurements

Range-based localization has received considerable attention in wireless sensor networks due to its ability to efficiently locate the unknown source of a signal. However, the localization accuracy with a single set of measurements may be inadequate, especially in dynamic and noisy environments. To mitigate this problem, received signal strength difference (RSSD) and time difference of arrival (TDOA) measurements are used to develop an efficient estimator to reduce the bias and improve localization accuracy. First, the RSSD/TDOA-based maximum likelihood (ML) localization problem is transformed into a hybrid information nonnegative constrained least squares (HI-NCLS) framework. Then, this framework is used to develop an effective bias-reduction localization approach (BRLA) with a two-step linearization process. The first step employs a linear solving method (LSM) which exploits an active set method to obtain a sub-optimal estimator. The second step uses a bias reduction method (BRM) to mitigate the correlation from linearization and a weighted instrumental variables matrix (IVM) which is weakly correlated with the noise but strongly correlated with the data matrix (DM) is used in place of the DM. Performance results are presented which demonstrate that the proposed BRLA provides better localization performance than state-of-the-art methods in the literature.

Solving the waste bin location problem with uncertain waste generation rate: A bi-objective robust optimization approach.

An efficient municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and liveability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, for example, the waste generation rate in an urban area. With this in mind, this article contributes with a robust optimization model to design the network of collection points (i.e. location and storage capacity), which are the first points of contact with the MSW system. A central feature of the model is a bi-objective function that aims at simultaneously minimizing the network costs of collection points and the required collection frequency to gather the accumulated waste (as a proxy of the collection cost). The value of the model is demonstrated by comparing its solutions with those obtained from its deterministic counterpart over a set of realistic instances considering different scenarios defined by different waste generation rates. The results show that the robust model finds competitive solutions in almost all cases investigated. An additional benefit of the model is that it allows the user to explore trade-offs between the two objectives.

Optimizing Last-Mile Delivery: A Multi-Criteria Approach with Automated Smart Lockers, Capillary Distribution and Crowdshipping

Background: This publication presents a review, multiple criteria optimization models, and a practical example pertaining to the integration of automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. This publication addresses challenges in logistics and transportation, aiming to enhance efficiency, reduce costs and improve customer satisfaction. This study integrates automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. Methods: A review of the existing literature synthesizes key concepts, such as facility location problems, vehicle routing problems and the mathematical programming approach, to optimize supply chain operations. Conceptual optimization models are formulated to solve the complex decision-making process involved in last-mile delivery, considering multiple objectives, including cost minimization, delivery time optimization, service level minimization, capacity optimization, vehicle minimization and resource utilization. Results: The multiple criteria approaches combine the vehicle routing problem and facility location problem, demonstrating the practical applicability of the proposed methodology in a real-world case study within a logistics company. Conclusions: The execution of multi-criteria models optimizes automated smart locker deployment, capillary distribution design, crowdshipping and last-mile delivery strategies, showcasing its effectiveness in the logistics sector.

A risk-averse distributionally robust optimisation approach for drone-supported relief facility location problem

Drone-supported last-mile humanitarian logistics applications play a crucial role in the rapid and efficient delivery of essential relief items, such as medicine, blood, and vaccines, during disaster and emergency situations. This paper explores a novel drone-supported relief facility location problem (DSRFLP) aimed at establishing an effective drone-supported last-mile humanitarian logistics system. The problem involves making joint decisions for both pre-disaster and post-disaster phases while considering the characteristics of drone-based delivery operations and uncertain demands. In the pre-disaster phase, we make the decisions regarding the locations of drone-supported relief facilities, inventory prepositioning of relief items, assignment of drones to the opened facilities, and allocation of drones to disaster demand sites. In the post-disaster phase, we make decisions related to delivery quantities. To tackle the challenge of incomplete demand distribution information in chaotic disaster environments, we establish a distributionally robust optimisation (DRO) model to handle the uncertainty of demands. This model adopts worst-case mean Conditional Value-at-Risk as the risk measurement, reflecting the risk-averse attitude of humanitarian organisers. In this paper, three ambiguity sets (box, ellipsoidal, and polyhedral) are considered to describe the ambiguity distributions of demands. To overcome the computational challenge, we reformulate the DRO model under three ambiguity sets into two mixed-integer linear programming models and one second-order cone programming model, which can be efficiently solved by off-the-shelf solvers. Furthermore, we validate our proposed DRO model through a small-scale example and a large-scale case study based on the Lushan earthquake in China. The computational outcomes underscore the superior performance of the proposed DRO model to mitigate the impact arising from incomplete probability distributions. We propose managerial implications and insights to support the decision-making of humanitarian organisations based on the experimental results. Finally, we propose two extended models to incorporate multiple relief items and equity constraints in priority settings and conduct numerical experiments to adapt to various real-world disaster scenarios.

Population decline and public attitudes toward multicultural immigration policies in South Korea

AbstractPopulation decline due to low fertility and aging causes socioeconomic challenges such as a shrinking labour force and regional decline. In response to these challenges, there is a growing discussion about accepting immigrants to mitigate the side effects of population decline. This implies the importance of analyzing local peoples' perceptions as a basis of policy and planning in anticipation of demographic transitions toward a multicultural society. While there are many studies on local problems caused by depopulation and perceptions of immigrants based on contact theory and group threat theory, there has been relatively little research on the relationship between depopulation and individual perceptions of immigrants. This study fills this gap by using survey data of Seoul, Korea, the city experiencing both population decline and an increase in immigrants. It employs the multilevel ordered logit model to explore how the decrease in local population associates with individuals' perception on the need for multicultural immigration policies. The results suggest that individuals are more likely to have a positive view of multicultural immigration policies if their region is experiencing a higher population decline. This finding is significant as it demonstrates that a shift to an unfavourable demographic structure can have a positive impact on perceptions of immigrant acceptance, offering a new perspective on the relationship between immigrants and native residents.

Uncertainty model-based edge detection technology in badminton

As virtual reality technology develops, the analysis and processing of video content have become hot spots in the field of computer vision. Video Action Detection aims to locate features in network video, and its research spans many fields, such as computer vision and spatial prediction. In view of the problem of low-efficiency classification models and inaccurate localization of small-scale targets in complex scenes, we propose a novel method to generate candidate intervals for action detection. The action recognition model is adopted to generate the action score sequence on the video time series. We also propose the uncertainty model of the descending pose detection algorithm. The pre-reaction phase generates a candidate list in the form of concatenated videos containing exactly the same pose to detect action poses that are not identical and of non-maximum duration. Experiments with traditional target detection and multiple deep learning models show that the proposed Non-Maximum Suppression algorithm has a strong ability to extract neural network features. Furthermore, compared with traditional ATSS and Faster R-CNN methods, the detection quality and performance are improved by more than 15.2% and 7.8%, respectively. Our method can fully utilize perception information to improve the quality of decision planning and plays a connecting role between perception fusion and decision planning.