Location Optimization Research Articles

Optimization of sample size and location for evaluating the hydraulic performance of microirrigation subunits using simulation and genetic algorithms

AbstractRapid and accurate field evaluation of hydraulic performance is critical for the operation of a microirrigation system. However, the optimal sample size and the specific locations of the emitters selected in one subunit for field tests have not been determined. A model (Hydraulic Analysis of Pressurized Irrigation System,HAPIS) was constructed for hydraulic analysis of a pressurized irrigation system by coupling MATLAB and EPANET. The random sampling method (RSM) and uniform sampling method (USM) were optimized for emitters selected through simulation, aiming to achieve higher estimation accuracies of the mean emitter discharge rate of the subunit ( and the Christiansen uniformity coefficient (CU) while minimizing the number of emitters tested. In addition, a linear sampling method at predetermined emitter locations (LSMPE) was developed to simplify the evaluation process using a genetic algorithm (GA). The results indicate that the appropriate sample size range for RSM was 20–40, in which the maximum percentage difference between and CU was maintained at ±10%. For the USM, a sample size of approximately 18 can provide relatively accurate estimations of and CU, while it is recommended that the sampled emitters be distributed over three to five laterals. The optimal sample size of LSMPE could be decreased to approximately 10, and the selected emitters were arranged along the sampling line. The absolute relative estimation error of and CU could be maintained at <1%.

Efficient optimization of fracturing parameters with consideration of fracture propagation and heterogeneity in tight gas reservoirs

The parameters of fractures, such as fracture geometries, placement, and conductivity, are critical for the production of horizontal wells in tight gas reservoirs. Current studies on fracture parameter optimization, especially those using the optimization algorithms, are often based on the assumptions that the fractures are uniformly placed with equal length and conductivity. However, non-uniform designs of fracture spacing have been proved to be effective in reducing fracture interference as well as increasing the well production, especially with strong rock heterogeneity. In addition, most of the current literature focuses on identifying optimal hydraulic fracture geometries without considering the fracture propagation process, which may oversimplify the evaluation of completion cost and result in fracture geometries that cannot be realized in practice.In this paper, an automatic optimization process, which could efficiently couple the fracture propagation, is proposed to optimize the fracture parameters. The genetic algorithm (GA) with penalty function method is used to optimize the constraint and non-linear problem. A modified PKN fracture propagation model is established to quickly predict the hydraulic fracture geometries. Fracture locations and fluid injection volume are sequentially optimized with consideration of heterogeneous rock properties. Model parameters are taken from typical tight gas horizontal wells in Jinqiu gas field in Sichuan Province, China. The results show the importance of permeability on both fracture length and production in tight gas well. The lower the permeability, the more significant the production increase after fracture location optimization. More fractures tend to be placed in area with small permeability and fewer fractures are required in highly permeable regions. With an increase in rock heterogeneity, the distribution of fractures becomes more uneven. Conversely, increasing the number of fractures can lead to a more even distribution of fractures. More fluid should be injected in low-permeability regions according to the optimization results. The optimization method is applied to a practical tight gas well in Sichuan Basin and the cumulative gas production can be increased by 6.5% to 8% when compared with the initial uniform designs.

Reducing the Environmental Impact of Health Care Conferences: A Study of Emissions and Practical Solutions.

We aimed to examine the impact of different conference formats (in-person, virtual, and hybrid) of the ASCO conference on greenhouse gas (GHG) emissions and to recommend sustainable options for future conferences. This study used data on the number of attendees, their departure locations, and the type of attendance (in-person v virtual) provided by ASCO between 2019 and 2022. The GHG emissions resulting from air and ground travel, remote connectivity, conference space utilization, hotel stays, distributed conference materials, and electricity use were estimated for each year. Emissions were stratified by attendee country of origin, type of attendance, and year. Simulations were conducted to evaluate how changes in conference size, location, and format impact emissions, as well as estimate the resulting mitigations from adopting the proposed changes. The highest estimated GHG emissions, calculated in carbon dioxide equivalents (CO2e), were associated with the 2019 in-person conference (37,251 metric tons of CO2e). Although international attendees had the largest contribution to emissions in all years (>50%), location optimization models, which selected conference locations that most minimized GHG emissions, yielded only minimal reductions (approximately 3%). Simulations examining changes to the conference format, location, and attendance percentage suggested that hub-and-spoke, where multiple conference locations are selected by global region, or hybrid models, with both in-person and virtual components, are likely to cause the largest drops in emissions (up to 86%). Using historical conference data, this study identifies key aspects that can be modified to reduce emissions and consequently promote more sustainable and equitable conference attendance. Hybrid conferences may be the best solution to maintain the networking opportunities provided by conferences while balancing out their environmental footprint.

DC fast charging stations for electric vehicles: A review

AbstractThe expansion of the DC fast‐charging (DCFC) network is expected to accelerate the transition to sustainable transportation by offering drivers additional charging options for longer journeys. However, DCFC places significant stress on the grid, leading to costly system upgrades and high monthly operational expenses. Incorporating energy storage into DCFC stations can mitigate these challenges. This article conducts a comprehensive review of DCFC station design, optimal sizing, location optimization based on charging/driver behaviour, electric vehicle charging time, cost of charging, and the impact of DC power on fast‐charging stations. The review is closely aligned with current state‐of‐the‐art technologies and encompasses academic research contributions. A critical assessment of 146 research articles published from 2000 to 2023 identifies research gaps and explores avenues for future study based on the literature review.

Exploratory evaluation of spinal cord stimulation with dynamic pulse patterns: a promising approach to improve stimulation sensation, coverage of pain areas, and expected pain relief.

The societal burden of chronic pain and the contribution-in-part to the opioid crisis, is a strong motivation to improve and expand non-addictive treatments, including spinal cord stimulation (SCS). For several decades standard SCS has consisted in delivery of tonic pulses with static parameter settings in frequency, pulse width, and amplitude. These static parameters have limited ability to personalize the quality of paresthesia, the dermatomal coverage, and thus may affect SCS efficacy. Further, static settings may contribute to the build-up of tolerance or loss of efficacy of the therapy over time in some patients. We conducted an acute exploratory study to evaluate the effects of SCS using time-dynamic pulses as compared to time-static (conventional tonic) stimulation pulses, with the hypotheses that dynamic pulse SCS may enable beneficial tailoring of the sensation and the patient's expectation for better pain relief with SCS. During a single clinic visit, consented subjects undergoing a standard SCS trial had their implanted leads temporarily connected to an investigational external stimulator capable of delivering time-static and six categories of time-dynamic pulse sequences, each characterized by continuously varying a stimulation parameter. Study subjects provided several assessments while blinded to the stimulation pattern, including: drawing of paresthesia maps, descriptions of sensation, and ratings for comfort and helpfulness to pain relief. Even without optimization of the field location, a majority of subjects rated sensations from dynamic stimulation as better or equal to that of static stimulation for comfortableness and for helpfulness to pain relief. The initial data showed a gender and/or pain dermatomal location related preference to a stimulation pattern. In particular, female subjects and subjects with pain at higher dermatomes tended to rank the sensation from dynamic stimulation better. Dynamic stimulation produced greater pain coverage without optimization; in 70% (9/13) of subjects, maximal pain coverage was achieved with a dynamic stimulation pattern. There was also greater variety in the words used by patients to describe stimulation sensation in the free text and free form verbal descriptions associated with dynamic stimulation. With the same electrode configuration and comparable parameter settings, acute SCS using dynamic pulses produced more positive ratings, expanded paresthesia coverage, and greater variation in sensation as compared to SCS using static pulses, suggesting that dynamic stimulation has the potential to improve capabilities of SCS for the treatment of chronic pain. Further study is warranted. This study was registered at ClinicalTrials.gov under ID NCT02988713, November 2016 (URL: https://clinicaltrials.gov/ct2/show/NCT02988713).

Optimization of Charging-Station Location and Capacity Determination Based on Optical Storage, Charging Integration, and Multi-Strategy Fusion

Optimization of Charging-Station Location and Capacity Determination Based on Optical Storage, Charging Integration, and Multi-Strategy Fusion

Optimizing Rack Locations in the Mobile-Rack Picking System: A Method of Integrating Rack Heat and Relevance

The flexible movement of racks in the mobile-rack picking system (MRPS) significantly improves the picking efficiency of e-commerce orders with the characteristics of “one order multi–items” and creates a challenging problem of how to place racks in the warehouse. This is because the placement of each rack in the MRPS directly influences the distance that racks need to be moved during order picking, which in turn affects the order picking efficiency. To handle the rack location optimization problem (RLOP), this work introduces a novel idea and methodology, taking into account the heat degree and the relevance degree of racks, to enhance the efficiency of rack placements in the MRPS. Specifically, a two-stage solution strategy is implemented. In stage 1, an integer programming model (Model 1) is developed to determine the heat and relevance degree of racks, and it can be solved quickly by the Gurobi. Stage 2 entails developing a bi-objective integer programming model (Model 2) with the objective to minimize the travel distances of robots in both heavy load and no-load conditions, using the rack heat and relevance degree as inputs. In light of the challenge of decision coupling and the vast solution space in stage 2, we innovatively propose two lower bounds by slacking off the distance between storage locations. A matheuristic algorithm based on Benders decomposition (MABBD) is designed, which utilizes Benders-related rules to reconstruct Model 2, introduces an enhanced cut and an improved optimal cut with RLOP characteristics, and designs the warm start strategy and the master variable fixed strategy. Given the substantial size of real-life problems, the Memetic algorithm (MA) is specifically devised to address them. Instances of varying sizes are also employed to validate the science and efficacy of the model and algorithm.

A hybrid path finder-based vortex search algorithm for optimal energy-efficient node placing and routing in UWSN

In recent times, the research community has demonstrated significant interest in Underwater Wireless Sensor Networks (UWSNs), where extensive sensor deployments in oceans and rivers aim to monitor the underwater environment. Energy consumption poses a primary challenge due to the difficulty of replacing or recharging batteries in these environments. Existing studies have employed K-Means technology to minimize power consumption in underwater transmission nodes. However, these studies have often overlooked the consideration of residual energy and void region creation in their optimization approaches. To address these challenges, we introduce a novel Hybrid path finder-based vortex search (HPF-VS) algorithm, utilized for cluster head selection and optimization of node locations and remaining energy. To extend network coverage beyond limited transmission ranges, inaccessible nodes at the network periphery employ the improved Dwarf Mongoose Optimization (IDMO) algorithm. Our proposed techniques demonstrate superior performance compared to existing methods, showcasing minimized energy consumption, reduced delay, improved packet delivery ratio, and enhanced throughput. Specifically, the proposed approach achieves a delay of 2.01 s and a throughput of 32.21 Kbps, surpassing the performance of state-of-the-art methodologies we benchmarked against.

A multivariable model for identification of preferred location of wind plants

Unique features of renewable energies such as wind energy has caused increasing demands for such resources. In order to use wind energy as a natural resource, environmental circumstances and geographical location related to wind intensity must be considered. Different factors may affect on the selection of a suitable location for wind plants. These factors must be considered concurrently for optimum location identification of wind plants. This article presents an integrated approach for location of wind plants by Data Envelopment Analysis (DEA). Furthermore, an integrated DEA approach incorporating the most relevant parameters of wind plants is introduced.The prescribed approach is tested for twenty five different cities in Iran. This is the first study that considers an integrated DEA approach for geographical location optimization of wind plants. Implementation of the proposed approach would enable the energy policy makers to select the best possible location for construction of a wind power plant with lowest possible costs.

Optimization of new fire department location using an improved GIS algorithm for firefighters travel time estimation

PurposeThe rapid development of urban areas in Sleman District, Indonesia, has created new challenges for firefighting response services. One of the primary challenges is to identify the optimal locations for new fire stations, to improve service quality and maximize service coverage within the specified time.Design/methodology/approachThis paper proposes a method for precisely calculating travel time that integrates delay time caused by traffic lights, intersections and congestion. The study highlights the importance of precise calculation of travel time in order to provide a more accurate understanding of the service area covered by the fire stations. The proposed method utilizes network analysis in ArcGIS, the analytical hierarchy process (AHP) and simple additive weighting (SAW) to accurately calculate travel time and to identify the best locations for new fire stations. The identification of new site was based on service safety, service quality, service costs and demographic factors and applied to the Sleman district in Indonesia.FindingsThe results showed that the total area covered by old and new fire stations decreased from 61% to 31.8% of the study area when the adjusted default speed scenario was implemented.Practical implicationsThe results indicated that the default speed scenario could provide misleading information about the service area, while the adjusted default speed scenario improved service quality and maximized service coverage.Originality/valueThe proposed method provides decision-makers with an effective tool to make informed decisions on optimal locations for new fire stations and thus enhance emergency response and public safety.

A data center expansion scheme considering net-zero carbon operation: Optimization of geographical location, on-site renewable utilization and green certificate purchase

Recently, more and more global attention has been paid to data centers’ energy consumption and climate impact. Serving as the backbone of the digital world, the energy and carbon-intensive data centers have to find a green expansion way to fulfill their obligations in building a carbon-neutral society. Therefore, this paper proposes a net-zero carbon operation oriented data center expansion scheme for the central government to formulate incentive policies. The economic expansion model minimizes newly established data centers’ investment and electricity consumption costs. Geographical location, on-site renewable utilization, and green certificate purchase are integrated and considered to achieve net-zero carbon operation by reducing total electricity consumption, increasing green power consumption, and offsetting inevitable carbon emissions. A case study in China is conducted, and results under different geographical location scenarios, power supply mix scenarios, and green certificates market scenarios are analyzed and compared. It is found that the zero-carbon operation of new data centers mainly relies on clean main-grid power, but a more decentralized layout and a provincial green certificate market can increase the contribution of on-site renewable energy. Lastly, four detailed policy implications for stimulating China’s data center expansion are discussed.

Enhancing Cell-Free Network: Joint Beamforming and Location Optimization Via UAV-IRS

Enhancing Cell-Free Network: Joint Beamforming and Location Optimization Via UAV-IRS

Joint Optimization of IRS Location and Passive Beamforming for Enhanced Received Power

Joint Optimization of IRS Location and Passive Beamforming for Enhanced Received Power

Representation Learning Based on Co-Evolutionary Combined With Probability Distribution Optimization for Precise Defect Location.

Visual defect detection methods based on representation learning play an important role in industrial scenarios. Defect detection technology based on representation learning has made significant progress. However, existing defect detection methods still face three challenges: first, the extreme scarcity of industrial defect samples makes training difficult. Second, due to the characteristics of industrial defects, such as blur and background interference, it is challenging to obtain fuzzy defect separation edges and context information. Third, industrial defects cannot obtain accurate positioning information. This article proposes feature co-evolution interaction architecture (CIA) and glass container defect dataset to address the above challenges. Specifically, the contributions of this article are as follows: first, this article designs a glass container image acquisition system that combines RGB and polarization information to create a glass container defect dataset containing more than 60 000 samples to alleviate the sample scarcity problem in industrial scenarios. Subsequently, this article designs the CIA. CIA optimizes the probability distribution of features through the co-evolution of edge and context features, thereby improving detection accuracy in blurred defects and noisy environments. Finally, this article proposes a novel inforced IoU loss (IIoU loss), which can obtain more accurate position information by being aware of the scale changes of the predicted box. Defect detection experiments in three mainstream industrial manufacturing categories (Northeastern University (NEU)-Det, glass containers, wood) show that CIA only uses 22.5 GFLOPs, and mean average precision (mAP) (NEU-Det: 88.74%, glass containers: 95.38%, wood: 68.42%) outperforms state-of-the-art methods.

Optimization of Electric Bus Scheduling and Charger Location

In the pursuit of sustainable urban transportation, the transition from traditional fossil fuel-powered buses to battery electric buses (BEBs) presents a significant leap forward. However, this shift introduces a critical challenge in the form of the BEB scheduling and charger location (EBSCL) problem. The limited driving range of BEBs necessitates the strategic reassignment of buses for service trips, known as the electric vehicle scheduling problem. Simultaneously, the optimal placement of charging infrastructure adds complexity to the overall transit electrification process. Our study addresses this challenge by proposing an integrated model that optimizes both BEB scheduling and charger location. The source code is available at https://gitlab.com/AmirDavatgari/etcp.

Joint Optimization of Longitudinal and Lateral Locations of Autonomous-Vehicle-Dedicated Lanes on Expressways

Joint Optimization of Longitudinal and Lateral Locations of Autonomous-Vehicle-Dedicated Lanes on Expressways

Assessment and Optimization of Wind Turbine Locations and Configurations for Coastal Areas of Bangladesh: A Comparative Study

Assessment and Optimization of Wind Turbine Locations and Configurations for Coastal Areas of Bangladesh: A Comparative Study

Weakly Supervised Monocular 3D Object Detection by Spatial-Temporal View Consistency.

Monocular 3D object detection plays a crucial role In the field of self-driving cars, estimating the size and location of objects solely based on input images. However, a notable disparity exists between the training and inference of 3D object detectors. This discrepancy arises because during inference, monocular 3D detectors depend solely on images captured by cameras; while during training, these methods require 3D ground truths labeled on point cloud data, which is obtained using specialized devices like LiDAR. This discrepancy creates a break in the data loop, preventing the feedback data from production cars from being utilized to enhance the robustness of the detectors. To address this issue and establish a connection in the data loop, we present a weakly-supervised solution that trains monocular 3D object detectors solely using 2D labels, eliminating the requirement for 3D ground truths. Our approach considers two view consistency: spatial and temporal view consistency, which play a crucial role in regulating the prediction of 3D bounding boxes. Spatial view consistency is achieved by employing projection and multi-view consistency techniques to guide the optimization of the target's location and size. We leverage temporal viewpoint consistency to provide temporal multi-view image pairs, and we further introduce temporal movement consistency to tackle the challenge of dynamic scenes. With only 2D ground truths, our method achieves comparable performance to fully supervised methods. Additionally, our method can be employed as a pre-training method and achieves significant improvement when fine-tuned with a small proportion of fully supervised labels.

Location and User Association Optimization in Multiple Radio Access UAV-Assisted Heterogeneous IoT Networks

Location and User Association Optimization in Multiple Radio Access UAV-Assisted Heterogeneous IoT Networks

3D Hover Location and Drone Routing Optimization for One-to-Many Continuous Wireless Charging Problem

3D Hover Location and Drone Routing Optimization for One-to-Many Continuous Wireless Charging Problem