Location Problem Research Articles

Certain Domination Parameters and Their Resolving Versions of Fractal Cubic Networks

Networks are designed to communicate, operate, and allocate tasks to respective commodities. Operating supercomputers became challenging, which was handled by the network design commonly known as hypercube, denoted by Qn. In a recent study, the hypercube networks were insufficient to hold supercomputers’ parallel processors. Thus, variants of hypercubes were discovered to produce an alternative to the hypercube. A new variant of the hypercube, the fractal cubic network, can be used as the best alternative in the case of hypercubes. Our research investigates that the fractal cubic network is a rooted product of two graphs. We try to determine its domination and resolving domination parameters, which could be applied to resource location and broadcasting-related problems.

Локализация движущегося шумового источника в мелководной акватории с нерегулярной батиметрией

The article presents the results of a numerical experiment on reconstructing the parameters of a moving underwater noise source in a shallow water area with irregular bathymetry in the form of a coastal wedge. The source localization problem is solved using holographic processing of noise signals. The case of horizontal refraction of sound field modes is considered. A single vector-scalar receiver is used as a receiving system.

The Optimization and Application Research of the RRT-APF-Based Path Planning Algorithm

To address the shortcomings of the original rapidly-exploring random tree (RRT) algorithm, such as long and non-smooth paths, slow convergence to the goal region, and limited adaptability in dynamic environments, this paper proposes a hybrid path planning method combining the artificial potential field (APF) approach with the RRT algorithm. This integrated approach leverages the strengths of both methods to achieve efficient, collision-free path planning in both two-dimensional and three-dimensional environments. The algorithm overcomes the local minima problem inherent in APF while maintaining the RRT’s efficiency in high-dimensional spaces and complex environments. A dynamic adjustment strategy is introduced to adapt to specific application scenarios and varying environmental complexity. Additionally, Bezier curve fitting is applied to smooth the resulting paths. Simulations conducted in various environments demonstrate the effectiveness of the proposed method, highlighting its efficiency and robustness in generating collision-free paths. Compared to the original RRT algorithm, the proposed method reduces path length by 13.4% to 24.9% and decreases search time by 9.8% to 56.5%, improving both path quality and planning efficiency.

Direct and Digital Democracy in the Ideology and Politics of the Five Star Movement

The article analyses the role of direct and e-democracy in the ideology and political practice of the Italian populist party Five Star Movement (M5S). Founded as a party in 2009, Movement stood out among European populists with its pronounced leftgreen agenda aimed at solving local problems of an urban nature. However, an even more remarkable feature of the Five Stars has become an equally clearly noticeable technoutopianism mixed with direct democracy, which argues for the need to expand democratic participation for ordinary citizens through the tools of direct democracy and digital technologies. Based on the study of primary sources (party documents, Beppe Grillo's blog and the official party blog), as well as text media materials, an attempt is made to trace how during two periods of activity of Five Star Movement in 2013–2022 the attitude of the party leaders to the role of direct and electronic democracy in the political activity of Five Stars gradually changed. The article analyses changes in Five Star Movement’s organisational structure and tools of the e-democracy in the course of the development of the party and its institutionalisation into the Italian party-political system. The article concludes that with the process of Five Stars’ incorporation into the political regime, the party has pushed technoutopianism out of its agenda and practice, strengthening party centralisation. Thus, innovative practices in the digitalisation of the political process did not lead to a real expansion of democratic participation, but were subordinated to a party discipline.

GAUSSIAN TRANSFER FUNCTIONS BASED BINARY PARTICLE SWARM OPTIMIZATION FOR ENHANCED PERFORMANCE IN UN-CAPACITATED FACILITY LOCATION PROBLEM

This study introduces Gaussian Binary Particle Swarm Optimization (G-BPSO), designed to address binary optimization challenges effectively. G-BPSO employs new transfer functions of the Gaussian type derived from the power functions to enable mapping of real-valued vectors of individual encodings into binary form. This ensures smooth change between steps and improved convergence. To assess the effectiveness of G-BPSO, a host of complex optimization problems such as the un-capacitated facility location problem are investigated. Enhanced efficiency and improvement over existing methods in binary optimization is observed. The MATLAB code of G-BPSO is made open-access through https://github.com/kanak02/GBPSO.

An optimisation model of hierarchical facility location problem for urban last-mile delivery with drones

Drones have, in recent years, been considered as a promising way for the last-mile delivery of urban logistics. In view of limited or short battery life of drones in every operation, the drone recharging stations and their locations are crucial to ensure delivery accessibility and to accomplish the required mission. Therefore, in addition to considering the selection of service centres and demand nodes, the location of recharging stations should also be incorporated into the proposed model of hierarchical facility location problem (HFLP). Many existing researches on drone recharging station locations assume that the delivery demand is known and only a single path is designed to meet the demand by using queuing theory models to describe node-based service waiting time. However, the reality is that we are still in the infrastructure planning stage for drone delivery, in which the demand is unknown in most cases. Our research proposes a novel mix-integer programming model to solve the HFLP for the last-mile drone delivery in urban areas, aiming to minimise the deployment, operation, and maintenance (DOM) costs of recharging stations, service centres, and paths. We have also incorporated a demand satisfaction constraint into the selection of effective alternative paths that meet the delivery accessibility. On the other hand, a sensitivity analysis in our study reveals varying degrees of impact on DOM costs and effective alternative path quantities, whose results would be useful to practical drone logistics delivery in urban areas.

The Appropriate Translation of the Nurse Practitioner Will Foster Health Equity.

Background: In September 2020, Governor Gavin Newsom of California signed Assembly Bill 890 (AB 890) into law, which changed Title 16 of the Nurse Practice Act, granting California nurse practitioners (NPs) a stepwise approach to full practice authority. Written within the law, NPs are required to verbally identify themselves specifically to all Spanish-speaking patients, as enfermera(o) especializada (o). Local Problem: AB 890 requires NPs to identify themselves using inappropriate Spanish terminology. There are concerns regarding the translational accuracy of nurse practitioners under AB 890. To avoid confusion, it is essential to ensure the translation of the NP role is precise and unambiguous to ensure health equity. Context: This law has a significant impact on approximately 37,000 NPs in the state of California who provide essential healthcare services to the Spanish-speaking community. Critically, the Hispanic/Latino community accounts for almost 40% of California state's population, and Intervention: This literature review will delve into the current literature for the accurate Spanish terminology of the "Advanced Practice Nurse," "Advanced Practice Registered Nurse," and Nurse Practitioner." Outcome Measures: To accurately translate "Advanced Practice Nurse," "Advanced Practice Registered Nurse," and "Nurse Practitioner."

Maximum and set covering problem considering the internal structure of production units

Purpose In many real-world problems, this paper faces some production processes that have internal structures that should be taken into account to have a comprehensive analysis. This situation is settled in the network data envelopment analysis (DEA) literature. Therefore, in this article, this paper aims to develop two types of covering location problems joint with network data envelopment analysis. This paper propose biobjective mixed integer programming models associated with the aforementioned development. In the first model, the goal is to select locations of facilities such that the total efficiency score is maximized and the total establishing cost for covering all demands is minimized. In the second model, this paper considers the location of facilities for maximizing the total efficiency score and covering the demands. Design/methodology/approach Covering location problems is considered a very important issue in the decision-making of organizations and companies. The purpose of these problems is to assign a set of demand points to a set of candidate locations so that optimal covering is provided for the demand points. Considering the efficiency of facility location with the help of DEA helps the decision-maker to reach more effective information and better analysis of the problem. Findings This paper applied the proposed models in the health centers of Shahrood City, so that each of the centers is considered a decision-making unit, and each of the decision-making units consists of three subunits that are connected in a series network. The primary results highlight the importance of the internal units beside the overall performance of healthcare centers. Originality/value Therefore, in this article, this paper develops two novel types of covering location problems joint with network data envelopment analysis. This paper proposes biobjective mixed integer programming models associated with the aforementioned development.

Agronegócio, atores e território: visão da população das localidades agrícolas da província de Buenos Aires (Argentina, 2022)

The expansion of agribusiness in the Argentine Pampas region has generated important economic, social and environmental consequences, particularly affecting agrarian localities, those local spaces linked to agro-exports. In this context, it is relevant to investigate how the inhabitants of these localities understand agricultural activity and its territorial impacts. Based on the data constructed through an online survey carried out in the province of Buenos Aires (main productive region of Argentina), we identified, firstly, that the inhabitants of agricultural localities define these spaces by their particular forms of sociability, and participate in cultural practices linked to rural life. Secondly, we find that they consider agribusiness as the main generator of employment in the territories but do not identify the “spillover” of the sector's profits in the communities. Thirdly, we see that they have a fairly positive image of producers and do not identify agricultural activity as the cause of the main local problems, but there is a majority view of its polluting impacts. And, finally, we identify the influence on opinions of certain discursive topics present in the public debate on agriculture and, mainly, of the political position.

Adaptive Fuzzy Bipartite Time‐Varying Formation Tracking Control for Multiple Lagrangian Systems With Lumped Uncertainties via Finite‐Time Hierarchical Mechanism

ABSTRACTThis article explores the bipartite time‐varying formation tracking problem for multiple Lagrangian systems in the presence of lumped uncertainties, including friction, external disturbances, and actuator faults. To tackle this challenging problem, a finite‐time hierarchical mechanism is designed without prior knowledge of uncertainties, which decomposes the above issue into two sub‐control problems: 1) the distributed finite‐time estimation problem and 2) the local adaptive fuzzy tracking problem. The distributed estimator algorithm is developed to achieve a finite‐time bipartite time‐varying formation configuration under a cooperative‐antagonistic interaction topology. Further, these estimated states are employed to construct the adaptive fuzzy tracking controller based on fuzzy‐logic systems and fault‐tolerant techniques. Lyapunov functions are utilized to ensure that the considered systems attain the desired formation while maintaining finite‐time stability of tracking errors. Finally, simulation experiments are performed to verify the effectiveness of the proposed control algorithm.

Machine Learning Based Localization of LoRa Mobile Wireless Nodes Using a Novel Sectorization Method

Indoor localization of wireless nodes is a relevant task for wireless sensor networks with mobile nodes using mobile robots. Despite the fact that outdoor localization is successfully performed by Global Positioning System (GPS) technology, indoor environments face several challenges due to multipath signal propagation, reflections from walls and objects, along with noise and interference. This results in the need for the development of new localization techniques. In this paper, Long-Range Wide-Area Network (LoRaWAN) technology is employed to address localization problems. A novel approach is proposed, based on the preliminary division of the room into sectors using a Received Signal Strength Indicator (RSSI) fingerprinting technique combined with machine learning (ML). Among various ML methods, the Gated Recurrent Unit (GRU) model reached the most accurate results, achieving localization accuracies of 94.54%, 91.02%, and 85.12% across three scenarios with a division into 256 sectors. Analysis of the cumulative error distribution function revealed the average localization error of 0.384 m, while the mean absolute error reached 0.246 m. These results demonstrate that the proposed sectorization method effectively mitigates the effects of noise and nonlinear signal propagation, ensuring precise localization of mobile nodes indoors.

Optimal energy management of MG for cost-effective operations and battery scheduling using BWO

The rapid growth of renewable standalone systems, with their inherent uncertainties and intermittency, poses a huge challenge to system stability. In this paper, an intelligent microgrid is proposed that fully utilizes the complementarity characteristics in PV and battery storage system (BESS), and is capable of maintaining an economic, reliable, and environmental equilibrium in daily generation scheduling. A black widow optimization (BWO) based controller is designed to determine the best possible solution under different scheduling scenarios. In addition, an exponential cost function based on the battery’s degree of discharge (DOD) is introduced to calculate the battery generation cost. It will include the battery lifetime cost in the overall power generation cost. Furthermore, the cost of the diesel generator includes the CO2 emission cost which is inversely proportional to the generator efficiency. The BWO algorithm optimized the generation cost based on these functions and increase the reliability of the grid using reliability index. The BWO algorithm finds the optimal global solution in less than 30 iterations and avoid the local optima problem. The effectiveness and convergence speed of BWO is far better than other algorithms and is validated by simulation results.

Robust Domain Adaptive Object Detection With Unified Multi-Granularity Alignment.

Domain adaptive detection aims to improve the generalization of detectors on target domain. To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. However, they neglect the relationship between multiple granularities and different features in alignment, degrading detection. Addressing this, we introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning. The key is to encode the dependencies across different granularities including pixel-, instance-, and category-levels simultaneously to align two domains. Specifically, based on pixel-level features, we first develop an omni-scale gated fusion (OSGF) module to aggregate discriminative representations of instances with scale-aware convolutions, leading to robust multi-scale detection. Besides, we introduce multi-granularity discriminators to identify where, either source or target domains, different granularities of samples come from. Note that, MGA not only leverages instance discriminability in different categories but also exploits category consistency between two domains for detection. Furthermore, we present an adaptive exponential moving average (AEMA) strategy that explores model assessments for model update to improve pseudo labels and alleviate local misalignment problem, boosting detection robustness. Extensive experiments on multiple domain adaption scenarios validate the superiority of MGA over other approaches on FCOS and Faster R-CNN detectors.

Sustainable site planning for new launch facilities at spaceports

The New Space industry not only demands expanded spaceport capacity but also careful consideration of the environmental impact of ground infrastructure development. Focusing on the Alcântara Space Center in Brazil (CEA - Centro Espacial de Alcântara), a data-driven site selection framework is presented, emphasizing cost-effectiveness and environmental compliance. When applied to the CEA case, this framework incorporates expert preferences and aligns Geographic Information System-Multi-Criteria Decision Analysis (GIS-MCDA) with the development of launch site infrastructure and the assessment of environmental impact. The real case application results in robust ranking outcomes, despite some level of divergence amongst the experts. Contributions include expert clustering for sensitivity analysis, insights into spaceport impact on surrounding communities, and the proposal of an easily deployable decision-making process. The framework exhibits flexibility and can be adapted to different variations of the spaceport locational problems. Recommendations include the implementation of advanced geotechnical assessment protocols, enhanced quantification methodologies for environmental variables, and the utilization of comprehensive risk assessment techniques. These measures hold the potential to provide valuable insights for research and regulatory efforts within this rapidly evolving domain.

Approximation and Heuristic Algorithms for the Priority Facility Location Problem with Outliers

Approximation and Heuristic Algorithms for the Priority Facility Location Problem with Outliers

Research on roll profile thermal-force driving characteristics and joint control strategy in the multi-zone roll profile electromagnetic control technology

Roll profile electromagnetic control technology (RPECT) is a new roll gap shape control technology that can flexibly control the roll profile to meet the flatness control requirements of cold rolling mill. As basic control elements, electromagnetic sticks (ES) can drive the local zone bulging of electromagnetic control roll (ECR) to generate thermal-force contribution roll profiles. Due to the high order profile defects in the thin-gauge strip rolling process, the requirement of roll gap shape control in cold rolling mills is usually characterized by multi-zone and micro-scale. Therefore, a multi-zone roll profile electromagnetic control technology is proposed in this paper, which includes a multi-zone joint control strategy and a parameter preset method for basic control elements targeting roll profile. To conduct the parameterized impact analysis of stick distance and multi-stick control modes, a multi-zone joint control simulation model is established. Through the simulation analysis, the results show that it is feasible to use multiple basic control elements to adjust the multi-zone roll profile in the mechanism of action, and the total roll profile curve can be equivalent to the superposition of the individual bulging curve of different basic control elements with different space-time parameters. Meanwhile, the reasonable distance setting can effectively avoid the instability of thermal contribution roll profile control caused by too small distance between sticks and too strong thermal bulging driving. For the double-stick control mode or the three-stick control mode, using different temperature control method can assist ECR to realize the roll profile control goal that the low-order roll profile of the basic control element is superimposed to construct the high-order roll profile curve. According to strip flatness electromagnetic control experiment, the multi-zone joint control of RPECT can adjust the flatness defects above 100 IU to flat state, and can flexibly regulate local shape problems and control local buckling.

Efficient FPGA Implementation of Sine Cosine Algorithm using High Level Synthesis

Sine Cosine Algorithm (SCA) finds the best solution to the optimization problem by the periodicity of sine and cosine trigonometric functions. However, it is computationally intensive and contains many parameters to be determined. Fortunately, there are FPGA platforms that can be used to overcome these limitations by improving latency. Sine and cosine calculation in library functions is very complex and time-consuming. Therefore, this paper proposes a hardware-accelerated CORDIC algorithm to improve the sine cosine trigonometric function that needs to be computed in the SCA algorithm. The proposed algorithm (HSCA) combines the accelerated SCA algorithm and the CORDIC algorithm. HSCA performance is tested by using six test functions run on the FPGA. The experimental results show that HSCA is 3.25 times faster and 33% fewer resource utilizations for solving optimization problems, and runs significantly faster on FPGAs with IP cores than on Soc chips in FPGAs. The performance of the HSCA algorithm is demonstrated by applying it to the TDOA localization problem.

Traditional Modular and End-to-End Methods in Vehicle Trajectory Planning and Decision Control in Complex Scenario: Review

Modular traditional autonomous driving and end-to-end autonomous driving have their own characteristics and play an important role in different scenarios in autonomous driving. The comprehensive performance of these two methods is compared and evaluated systematically in this paper. Modularity Traditional autonomous driving achieves high controllability and interpretability by breaking the system into multiple independent functional modules. However, the efficiency of information transfer between modules is low, and local optimal problems may occur when dealing with complex scenes. The end-to-end autonomous driving system realizes direct mapping from perception to control through deep learning, showing strong global optimization capabilities and the potential to deal with complex scenarios, but also faces black box problems and dependence on a large number of labeled data. This paper discusses the advantages and disadvantages of these two methods in practical applications, and suggests possible future research directions, including the integration of modular and end-to-end methods, improving the interpretability and security of the system, and improving data efficiency and system generalization. Taken together, modular traditional autonomous driving and end-to-end autonomous driving can achieve a safer and more efficient autonomous driving system by combining their respective advantages.

EHFusion: an efficient heterogeneous fusion model for group-based 3D human pose estimation

AbstractStimulated by its important applications in animation, gaming, virtual reality, augmented reality, and healthcare, 3D human pose estimation has received considerable attention in recent years. To improve the accuracy of 3D human pose estimation, most approaches have converted this challenging task into a local pose estimation problem by dividing the body joints of the human body into different groups based on the human body topology. The body joint features of different groups are then fused to predict the overall pose of the whole body, which requires a joint feature fusion scheme. Nevertheless, the joint feature fusion schemes adopted in existing methods involve the learning of extensive parameters and hence are computationally very expensive. This paper reports a new topology-based grouped method ‘EHFusion’ for 3D human pose estimation, which involves a heterogeneous feature fusion (HFF) module that integrates grouped pose features. The HFF module reduces the computational complexity of the model while achieving promising accuracy. Moreover, we introduce motion amplitude information and a camera intrinsic embedding module to provide better global information and 2D-to-3D conversion knowledge, thereby improving the overall robustness and accuracy of the method. In contrast to previous methods, the proposed new network can be trained end-to-end in one single stage. Experimental results not only demonstrate the advantageous trade-offs between estimation accuracy and computational complexity achieved by our method but also showcase the competitive performance in comparison with various existing state-of-the-art methods (e.g., transformer-based) when evaluated on two public datasets, Human3.6M and HumanEva. The data and code are available at doi:10.5281/zenodo.11113132

A comprehensive survey of recent advances in facility location problems: Models, solution methods, and applications

Logistics management research has significantly benefited from the increased prominence of warehouse management challenges in recent years, which are now extensively utilized in analyzing logistics distribution and transportation networks. Concerning Facility Location Problems and their variations, this work attempts to perform an extensive literature assessment of current model advances and improvements. This study also aims to ascertain the influence and developments in Facility Location Problems, which are classic optimization problems with numerous applications in manufacturing, distribution, transportation, and supply chain management decisions. This study thoroughly analyzes current developments in facility location problems, a category of optimization issues frequently used in network architecture, logistics, and supply chain management. The review analyzes literature from the past decade, categorizing works based on problem variants, mathematical formulations, solution methods, and exact approaches. Key findings highlight significant progress in solving complex facility location problems through advances in mathematical programming, scalable exact methods, and hybrid metaheuristics. Emerging trends such as uncertainty modeling, multi-objective optimization, and sustainability are also discussed. Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 12: Responsible Consumption and Production; SDG 13: Climate Action