Location Problem Research Articles

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.

Low Carbon Logistics Research Review

The logistics industry is one of the main industries of "carbon emission", and low-carbon logistics is also related to the realization of the goal of "carbon peak" and "carbon neutrality". This paper uses the literature research method, takes the concept of low-carbon logistics as the starting point, and combs the relevant research progress. Firstly, this paper introduces the concept of low-carbon logistics from the perspective of low-carbon and circular economy, and introduces the realization method of low-carbon logistics. Secondly, it reviews the progress of literature research from the aspects of low-carbon transport, low-carbon packaging, low-carbon storage, and low-carbon policy. It is concluded that the main direction of future research will be to shift from traditional local low-carbon research to systematic low-carbon research, use modern information technology and intelligent tools to help low-carbon logistics, and pay attention to the local problems brought by China's logistics practice.

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.

Resolving Charging Station Placement Issues for Electric Vehicles: Hybrid Optimization-Assisted Multi-Objective Framework

As new energy technology has advanced to a respectable level, Electric Vehicles (EV) have gained recognition from people all over the world and have become increasingly popular in a number of nations. Effective charging station placement is critical for the rapid growth of electric vehicles, as it is vital to provide convenience for electric vehicles while also ensuring the effectiveness of traffic networks. The location of an EV charging station is simply an application situation for the facility location problem. Traditional works often pay attention to the mileage concerns of electric vehicle customers while ignoring their competitive and strategic charging tactics. This research aims to frame the allocation of charging stations issue as a multi-faceted venture by assessing the aspects economically along with the characteristics of the power grid like “cost, Voltage stability, Reliability, and Power loss (VRP) index, waiting time, and accessibility index”. Further, we proposed a hybrid artificial intelligence to resolve the allocation issue. The proposed new Artificial Intelligence (AI) based algorithm is termed as Hybridized Salp and Harris Algorithm (HS-HA). The effectiveness and scalability of the presented algorithm for the charging station placement issue are also assessed through different plans in the IEEE 33-bus distribution systems. Finally, the efficiency of the presented approach is proved over the existing approaches with respect to various measures.

The grand bargain on local government debt: Evidence from China's intergovernmental relations

IMPACT This article provides a deeper understanding of policy implementation in local government, highlighting the key role of subnational governments in public risk governance. In fiscally centralized countries with significant local government debt problems, officials at the central government level cannot effectively mitigate local government debt risks solely through restrictive financing policies. The authors explain why policy-makers at the central government level in these countries need to revaluate the structure of the local government financial system.

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

There 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.

Highly robust and accurate multi-sensor fusion localization system for complex and challenging scenarios

Highly robust and accurate localization using on-board sensors in the absence of GNSS is a key issue for long-term autonomous navigation of vehicles. However, the limited sensing capabilities of a single sensor cannot resist the effects of feature degradation and high-speed motion in complex and challenging scenarios, which results in the fragile of existing LiDAR-based and visual-based localization algorithms. To solve these problems, we propose a highly robust and accurate localization system by fusing information from multi-sensors. Firstly, we propose a global localization method that leverages deep learning models for processing visual information to provide an accurate initial position for vehicles, which solves the problem of global localization of vehicles in the absence of GNSS. Then, we propose a highly robust and accurate LiDAR-Vision-IMU localization method based on pose graph optimization, which integrates LiDAR-based poses and visual-inertial-based poses. The proposed method detects sensor degradation in real time to dynamically adjust the fusion weights, which solves the problem of low localization performance of a single sensor. Finally, we conduct experiments in complex and challenging scenarios, such as urban, tunnels, and unstructured mountains. Experimental results show that our method can achieve a global localization error of less than 0.5 m in complex scenes. In unstructured mountainous scenarios of length 21.1 km, the root mean square errors (RMSE) of translation and rotation are 0.67 m and 0.048rad, respectively. Compared to most existing algorithms, our method achieves the most robust and accurate localization performance.

Relaxations and Exact Solutions to Quantum Max Cut via the Algebraic Structure of Swap Operators

The Quantum Max Cut (QMC) problem has emerged as a test-problem for designing approximation algorithms for local Hamiltonian problems. In this paper we attack this problem using the algebraic structure of QMC, in particular the relationship between the quantum max cut Hamiltonian and the representation theory of the symmetric group.The first major contribution of this paper is an extension of non-commutative Sum of Squares (ncSoS) optimization techniques to give a new hierarchy of relaxations to Quantum Max Cut. The hierarchy we present is based on optimizations over polynomials in the qubit swap operators. This is in contrast to the "standard" quantum Lasserre Hierarchy, which is based on polynomials expressed in terms of the Pauli matrices. To prove correctness of this hierarchy, we exploit a finite presentation of the algebra generated by the qubit swap operators. This presentation allows for the use of computer algebraic techniques to manipulate and simplify polynomials written in terms of the swap operators, and may be of independent interest. Surprisingly, we find that level-2 of this new hierarchy is numerically exact (up to tolerance 10−7) on all QMC instances with uniform edge weights on graphs with at most 8 vertices.The second major contribution of this paper is a polynomial-time algorithm that computes (in exact arithmetic) the maximum eigenvalue of the QMC Hamiltonian for certain graphs, including graphs that can be "decomposed" as a signed combination of cliques. A special case of the latter are complete bipartite graphs with uniform edge-weights, for which exact solutions are known from the work of Lieb and Mattis \cite{lieb1962ordering}. Our methods, which use representation theory of the symmetric group, can be seen as a generalization of the Lieb-Mattis result.

Maximally smooth cubic spline quasi-interpolants on arbitrary triangulations

We investigate the construction of C2 cubic spline quasi-interpolants on a given arbitrary triangulation T to approximate a sufficiently smooth function f. The proposed quasi-interpolants are locally represented in terms of a simplex spline basis defined on the cubic Wang–Shi refinement of the triangulation. This basis behaves like a B-spline basis within each triangle of T and like a Bernstein basis for imposing smoothness across the edges of T. Any element of the cubic Wang–Shi spline space can be uniquely identified by considering a local Hermite interpolation problem on every triangle of T. Different C2 cubic spline quasi-interpolants are then obtained by feeding different sets of Hermite data to this Hermite interpolation problem, possibly reconstructed via local polynomial approximation. All the proposed quasi-interpolants reproduce cubic polynomials and their performance is illustrated with various numerical examples.

Enhancing Procedure Documentation in Neonatal Intensive Care Unit (NICU): A Quality Improvement Initiative at a Tertiary Neonatal Hospital.

Background Accurate and comprehensive procedure documentation in Electronic Medical Records (EMR) is crucial for high-quality patient care, especially in high-acuity settings like Neonatal Intensive Care Units (NICU). Gaps in documentation at Corniche Hospital's NICU that were affecting patient safety and continuity of care were identified and addressed by following a pre and post-intervention design in the research. The process involved the initial audit, educational sessions with healthcare providers, and follow-up audits to measure improvements.Results post-intervention showed a significant increase in compliance with documentation standards, pointing out the effectiveness of educational interventions in improving EMR documentation practices. The local problem is demonstrated through the observation ofincomplete and inconsistent procedure documentation in the NICU,hindering effective patient management and multi-disciplinary team communication. Methods A Quality Improvement Project (QIP) was implemented, including a baseline audit, educational interventions targeting healthcare providers, and subsequent re-audits to assess improvement. The project involved tailored educational sessions focused on correct EMR usage, adherence to documentation standards, and practical aspects of documenting procedures. Results Post-intervention, there was a significant increase in documentation compliance. The percentage of compliance in procedure encounter placement in EMR increased from 81% to 100%, and nursing documentation compliance improved from 11 (52.4%) to 18 (85.7%). However, a slight decrease in the completeness of physician documentation was noted. Conclusions The QIP effectively improved procedure documentation in the NICU. Continuous education and periodic review are essential for maintaining and further enhancing documentation standards. This initiative underscores the importance of targeted training and consistent audits in improving clinical documentation in healthcare settings.

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.

Revisiting reachability-driven explicit MPC for embedded control

The real-time implementation of the explicit MPC suffers from the evaluation of the, potentially large, lookup table. The paper revisits the original approach and presents an efficient reachability-sets-driven-based explicit MPC method addressing this issue by splitting the look-up table into the set of the “relevant” subsets. Simultaneously, effective binary encoding is introduced to minimize the runtimes and the memory footprint. Further acceleration is achieved by introducing the “smart” order of the considered critical regions. Then, the significant real-time complexity reduction is ensured by online pruning and traversing the sorted lookup table associated with the optimal control law evaluation. Technically, the number of critical regions to be explored is reduced and the order is redefined to accelerate the point location problem and minimize the computational effort. While the optimality of the control law is still preserved, the cost that we need to pay for the accelerated point location problem lies in an additional offline computation effort and a minor increase in memory requirements of the underlying controller. The benefits of the proposed method are demonstrated using an extensive case study. The complexity reduction strategy was investigated on two fast-dynamic benchmark systems and the computational burden was analyzed by implementing the designed controllers on an embedded hardware.

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 multitask incremental least mean square algorithm using orthonormal codes

A new multitask incremental least mean-square (MILMS) algorithm using orthonormal codes is developed. In the multitask topology, nodes belong to different clusters, with each cluster performing its own ILMS estimation. The closed-form expressions of both the theoretical transient and steady-state mean squared deviation (MSD) are derived. The proposed MILMS algorithm is further reinforced by two combination strategies, referred to here as the combine-then-adapt (CTA), and adapt-then-combine (ATC), giving rise to two new improved algorithms, the CTA-MILMS and ATC-MILMS that substantially improve the MSD. Two further variants of these two improved algorithms are also developed based on the combination of the variable step size (VSS) and discrete cosine transform (DCT) techniques, leading to the two combined algorithms, termed here as CTA-VSSMILMS and ATC-VSSMILMS, respectively. Analysis of the first and second moments of the step size, under the transient and steady-state conditions, is also included.The problem of agent localization is solved using the MILMS algorithm by deploying a fixed number of anchors with known positions that surround agents with unknown positions. Extensive experiments were carried out to show the excellent match between the theoretical transient and steady-state expressions and their corresponding empirical results in terms of MSD performance. Moreover, we show that both the ATC-VSSMILMS and CTA-VSSMILMS algorithms improve the accuracy and computational speed of the MSD results of the proposed MILMS algorithm, thanks to the VSS-induced reduction of the number of needed steps and the DCT-induced data compression effect.

A congested facility location problem with strategic customers

This study addresses a user-equilibrium congested facility location problem with delay-, accessibility-, and price-sensitive customers. A profit-maximizing service provider first makes location, service rate, and pricing decisions, and then strategic customers decide which facilities to patronize (if any). By incorporating the customers’ choice behavior as a set of equilibrium constraints into the service provider’s decision problem, the problem is modeled as a mixed integer non-linear program which is then reformulated as a mixed integer second-order cone program. The proposed model, tested on several standard instances, is shown to be efficiently solvable using commercial software packages.

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.