Location Optimization Research Articles

An Improved Bayesian Learning Method for Distribution Grid Fault Location and Meter Deployment Optimization

An Improved Bayesian Learning Method for Distribution Grid Fault Location and Meter Deployment Optimization

Joint optimization of location and allocation for spare parts depots under ($ r, Q $) inventory policy

<p>The ability to replace failed spare parts in time directly affects the supportability level of equipment systems. The selection of spare parts' depot locations, inventory mode, and allocation are often separate and independent operations. However, in these situations, the total supply cost is usually relatively high with the consideration of spare parts shortage and maintenance delays. Therefore, this article dealt with a depot location-inventory-allocation problem based on the $ (r, Q) $ inventory method and analyzed a combined network of centralized spare part depot locations, inventory, and allocation. Meanwhile, considering the convenience and speed of spare parts transportation brought about by the improvement of transportation capacity, a network is proposed to adopt a centralized storage and point-to-point allocation strategy for parts replacement, which reduces supportability costs without affecting supply efficiency. An optimization model has been developed that reduces the overall cost of support, including inventory, construction, transportation, and logistics. Three equipment support efficiency metrics were used as constraints in this model to assess the location of open depots: selection availability, fill rate, and predicted downtime. Additionally, due to the knowledge asymmetry, there are some shortage issues which always lead to extra expenditure. The model also introduces uncertain distribution to demand measurement and adopts a genetic algorithm for model solving. Ultimately, a numerical instance was developed so as to verify our results.</p>

An Efficient Metaheuristic algorithm for optimization for Location of Optimum Connectivity Services with Drones in Healthcare

Drones are being extensively used for a variety of practical purposes, including providing medical care. For instance, giving vaccinations, healthcare equipment, and samples of blood to individuals in isolated locations as well as during calamities. This study examined an efficient metaheuristic algorithm called MAP-LOCS, which optimizes the location of optimal connectivity amenities using drones. The use of drones in relation to facility placement and navigation is the issue. Within certain drone range limits, it entails choosing drone launch spots to optimize patient care availability. This work develops a heuristic called Location of Optimal Connection Services. The algorithm's basic premise is to start by randomly selecting a few services to open from among those that are most capable of meeting patient requests. Following that, patients are matched with the nearest open facility that can accommodate them.

Location Optimization Enhancement of Wireless Sensor Networks in Intelligent Transportation System Based on Reverse Elite Large Model

Location Optimization Enhancement of Wireless Sensor Networks in Intelligent Transportation System Based on Reverse Elite Large Model

Location Optimization of Charging Stations for Electric Vehicles Based on Heterogeneous Factors Analysis and Improved Genetic Algorithm

Location Optimization of Charging Stations for Electric Vehicles Based on Heterogeneous Factors Analysis and Improved Genetic Algorithm

Random Scenario-based Dynamic Location Optimization for EV Fast-charging Station

Random Scenario-based Dynamic Location Optimization for EV Fast-charging Station

Inter-Plan Relationship regarding the Regional Urban Facility Location Optimization Policies

Inter-Plan Relationship regarding the Regional Urban Facility Location Optimization Policies

Adaptive Datum Points Location Optimization Method for Flexible Workpieces based on Centroid Characteristics

This paper proposes a method for adapting the layout of flexible workpieces of various shapes, in order to reduce the difficulty of location design for personalized shapes. Different from the previous location design that relied on the experiences of engineers, this method establishes an optimization model that determines the number of datum points and the optimal solution based on the centroid characteristics of the workpieces. The optimization index of this model is to control the minimum deformation of the overall flexible workpieces and the minimum variance of the deformation at the maximum deformation point. Taking the characteristics of the workpieces as a priori constraints for location determination, this model obtains the optimal solution combined with finite element analysis. Finally, the effectiveness of the method is verified through a numerical example.

Enhancing Meteorological Mobile Radar Observations Through Radar Location Optimization

Enhancing Meteorological Mobile Radar Observations Through Radar Location Optimization

Integrated optimization of facility location, casualty allocation and medical staff planning for post-disaster emergency response

PurposeDisaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.Design/methodology/approachThis study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.FindingsUsing a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.Originality/valueThis study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

A Comprehensive Review of FACTS Controllers in Nigeria Power Systems Network for Enhanced Performance on Variable Loads

Interest on FACTS controllers in the scientific and engineering community have increased due to their high reliability, low cost, and absence of infrastructural overhauling. In the context of global sustainability, it is required that FACTS controllers must fulfill physical, technological, and environmental requirements, through design and optimization studies. This study provides a comprehensive evaluation of the state-of-the-art technologies relevant to multi-objective optimization and performance analysis of FACTS controllers. Types, general overview, FACTS deployed in the Nigerian power systems and multi-objective location optimization and their impact were discussed. Several efficient optimization strategies from recent research works are highlighted and compared, showing that there are research gaps on the integration of advanced control algorithms and use of robust optimization methods.

A Survey on Air-to-Sea Integrated Maritime Internet of Things: Enabling Technologies, Applications, and Future Challenges

Future generation communication systems are exemplified by 5G and 6G wireless technologies, and the utilization of integrated air-to-sea (A2S) communication infrastructure is employed to extend network coverage and enhance data throughput to support data-driven maritime applications. These ground-breaking techniques have promoted the rapid development of the maritime internet of things (MIoT). In particular, the integration of air base stations (ABSs) in the MIoT can achieve broadband, low-delay, and reliable wireless transmissions. Considering the potential of ABS-enabled communications, this survey presents the state of the art in the A2S integrated MIoT. More specifically, relevant A2S integrated MIoT architectures are discussed together with the role of their building blocks. Next, we introduce the enabling technologies, including the sensor, communication, data processing and storage, and security and privacy protection techniques. Then, resource allocation, cloud/edge computing and caching, routing protocols, and spatial location optimization in the maritime environment are discussed and grouped based on their performance targets. Additionally, we also show the potential applications of the A2S integrated MIoT in marine environment monitoring, traffic, navigation safety, and resources management. Finally, several future challenges in the area of the A2S integrated MIoT are given, related to the technical security, reliability, and energy efficiency, etc.

A framework for the optimal deployment of police drones based on street-level crime risk

Drones are increasingly adopted for policing in many countries, as they can aid police officers to detect hazards and respond to incidents with timely and low-cost services. However, the planning and deployment of police drones are subject to several challenges, including the proper distance metric for drone flying and the risk-based location optimisation of drone base stations. This study proposes a new framework that enables the optimal deployment of police drones to address crime risk issues on urban street networks. This risk-based decision framework takes into account three potential distance metrics that regulate and shape the flying routes of drones, which in turn affects the optimal location of drone base stations. In addition, this framework takes into account the major risk constraints of flying drones in urban areas, including domestic privacy and elevation. The proposed risk-based decision framework is validated using the real case study of Liverpool with historical crime data and street network layouts. The findings contribute to the operations and management of police drones in urban areas and shift the paradigm of policing drones towards a risk-based regime.

A novel lifting point location optimization method of transmission line tower based on improved grey wolf optimizer

In the erection process of transmission line tower, the appropriate lifting point position is an important factor in ensuring the stability and balance of the lifting process and preventing deformation and damage to the towers. In this paper, a improved grey wolf optimization algorithm is proposed to solve the issues of low optimization efficiency and easily getting trapped in local minima when optimizing the lifting point position of transmission line towers. The improved algorithm includes the use of a good point-set strategy to enhance the initialization method of grey wolf individuals, ensuring a more uniform distribution of the population and reducing ineffective searches in the early stages of optimization. Furthermore, two random operators are utilized to combine and mutate the optimal grey wolf position, thereby enhancing the algorithm's ability to escape local optima. Finally, the trend information of the optimization process is considered, and the median value of the population is used to improve the stability of the optimization algorithm. Experimental results demonstrate that the proposed algorithm has better optimization performance and faster convergence speed compared to genetic algorithm, particle swarm optimization algorithm, and artificial fish swarm algorithm. It effectively addresses the optimization problem of lifting point position for transmission line towers.

A Comprehensive Review of Artificial Intelligence Algorithms Applications for Distributed Generation Sizing and Location Optimization to Enhance Efficiency and Reliability Indices in Radial Distribution Systems

The research aimed to understand radial distribution systems by defining them and exploring their strategies, and to explore methods for applying artificial intelligence algorithms in radial distribution systems, It also aimed to clarify types of indicators in radial distribution systems, highlighting the importance of providing a scientific and applied framework for integrating artificial intelligence in the field of energy distribution. The significance of the research lies in presenting a comprehensive and up-to-date approach based on the applications of artificial intelligence algorithms to improve the design and organization of distributed production systems in radial distribution systems. By employing a descriptive and analytical approach, the researcher was able to reach several conclusions, with the most prominent being that the application of artificial intelligence algorithms in radial distribution systems contributes to improving energy efficiency by directing it more accurately and effectively, the ability of artificial intelligence to provide precise data-dependent analyses enhances decision-making processes in radial distribution systems, The integration of artificial intelligence techniques contributes to improving overall efficiency in radial distribution systems, reducing energy losses, and enhancing energy distribution effectively. The research recommends enhancing integration between artificial intelligence techniques and radial distribution systems to ensure effective and smooth interaction, thereby increasing efficiency, Priority should be given to the safety and protection of data, and effective cybersecurity measures should be implemented to address the growing challenges in this context.

Hydrogen station location optimization coupling hydrogen sources and transportation along the expressway

The appropriate siting of hydrogen refueling stations (HRSs) can contribute to reducing hydrogen costs and promoting the widespread adoption of hydrogen fuel cell vehicles (HFCVs). This study integrates hydrogen sources and transportation, aiming to minimize the unit hydrogen cost. Two models for hydrogen station siting along the expressway are established. For the selection of feasible station siting schemes under discrete conditions, we design a screening process and algorithm Taking a specific highway section as a case study, both models are optimized, resulting in the unit hydrogen cost of 50.44 CNY/kg and 51.69 CNY/kg, respectively. Optimization verification is conducted, with percentage variances of 6.61 % and 18.77 %, respectively. Furthermore, the study delves into the impacts of different hydrogen demand, hydrogen production capacities, and the maximum allowable spacing between HRSs on station siting for both models. This research provides significant insights for hydrogen station siting along the expressway.

The intelligent optimization of perforation cluster locations incorporating the fiber optics monitoring results

During horizontal well multi-stage fracturing (HWMF), superfractures are often identified. To promote the uniform propagation of multiple fractures, it is necessary to finely optimize the perforation cluster locations based on the geological and engineering parameters. This work proposes an efficient method to design the perforation cluster locations in consideration of the geoengineering sweet spots with similar mechanical properties. Well log data and the precise fiber optics (FO) monitoring results are combined to find the main influencing factors. The principal component is conducted by introducing correlation analysis and Random Forest. Moreover, the K-means++ clustering method is used to evaluate reservoir quality. The fracturing sweet index (FSI) is proposed to measure the fracturing performance of each category quantitatively. The proposed workflow is effectively validated by two production scenarios. Moreover, the workflow can automatically evaluate reservoir quality based on intelligent clustering methods. Compared with the original design, the updated design lowers the gap among multiple fractures within one stage and increases the well production by 20%–50%. This work is beneficial for the on-site treatment of its feasibility and generalizability.

Pantograph Pose Measurement Based on Keypoint Location and Edge Shape Optimization Under Complex Conditions

Pantograph Pose Measurement Based on Keypoint Location and Edge Shape Optimization Under Complex Conditions

Location optimization of phase change material for thermal energy storage in concrete block for development of energy efficient buildings

Location optimization of phase change material for thermal energy storage in concrete block for development of energy efficient buildings

Repurposing existing skeletal spatial structure (SkS) system designs using the Field Information Modeling (FIM) framework for generative decision-support in future construction projects

Skeletal spatial structure (SkS) systems are modular systems which have shown promise to support mass customization, and sustainability in construction. SkS have been used extensively in the reconstruction efforts since World War II, particularly to build geometrically flexible and free-form structures. By employing advanced digital engineering and construction practices, the existing SkS designs may be repurposed to generate new optimal designs that satisfy current construction demands of contemporary societies. To this end, this study investigated the application of point cloud processing using the Field Information Modeling (FIM) framework for the digital documentation and generative redesign of existing SkS systems. Three new algorithms were proposed to (i) expand FIM to include generative decision-support; (ii) generate as-built building information modeling (BIM) for SkS; and (iii) modularize SkS designs with repeating patterns for optimal production and supply chain management. These algorithms incorporated a host of new AI-inspired methods, including support vector machine (SVM) for decision support; Bayesian optimization for neighborhood definition; Bayesian Gaussian mixture clustering for modularization; and Monte Carlo stochastic multi-criteria decision making (MCDM) for selection of the top Pareto front solutions obtained by the non-dominant sorting Genetic Algorithm (NSGA II). The algorithms were tested and validated on four real-world point cloud datasets to solve two generative modeling problems, namely, engineering design optimization and facility location optimization. It was observed that the proposed Bayesian neighborhood definition outperformed particle swarm and uniform sampling by 34% and 27%, respectively. The proposed SVM-based linear feature detection outperformed k-means and spectral clustering by 56% and 9%, respectively. Finally, the NSGA II algorithm combined with the stochastic MCDM produced diverse “top four” solutions based on project-specific criteria. The results indicate promise for future utilization of the framework to produce training datasets for generative adversarial networks that generate new designs based only on stakeholder requirements.