Explore the vast range of titles available.

Electric vehicles (EVs) are becoming more commonplace as they cut down on both fossil fuel use and pollution caused by the transportation sector. However, there are a number of major issues that have arisen as a result of the rapid expansion of electric vehicles, including an inadequate number of charging stations, uneven distribution, and excessive cost. The purpose of this study is to enable EV drivers to find charging stations within optimal distances while also taking into account economic, practical, geographical, and atmospheric considerations. This paper uses the Fez-Meknes region in Morocco as a case study to investigate potential solutions to the issues raised above. The scorching, arid climate of the region could be a deterrent to the widespread use of electric vehicles there. This article first attempts to construct a model of an EV battery on MATLAB/Simulink in order to create battery autonomy of the most widely used EV car in Morocco, taking into account weather, driving style, infrastructure, and traffic. Secondly, collected data from the region and simulation results were then employed to visualize the impact of ambient temperature on EV charging station location planning, and a genetic algorithm-based model for optimizing the placement of charging stations was developed in this research. With this method, EV charging station locations were initially generated under the influence of gas station locations, population and parking areas, and traffic, and eventually through mutation, the generated initial placements were optimized within the bounds of optimal cost, road width, power availability, and autonomy range and influence. The results are displayed to readers in a node-link network to help visually represent the impact of ambient temperatures on EV charging station location optimization and then are displayed in interactive GIS maps. Finally, conclusions and research prospects were provided.

The selection and planning of the location of emergency shelters have a crucial impact on the safety of residents and cities. In this paper, based on multivariate open geographic data, the Gaussian two-step floating catchment area method, K-means clustering, and particle swarm optimization algorithm methods are utilized to carry out a spatial accessibility analysis and location optimization of emergency shelters in Deyang City, Sichuan Province, China. The study shows that: (1) Deyang City's emergency shelters are higher than the government's relevant standard requirements in terms of major indicators such as single building area, total area and per capita shelter area. (2) The spatial distribution of emergency shelters in the study area is uneven and unreasonable, with accessibility from the urban center outwards, exhibiting a \"high-low\" distribution pattern. (3) The study suggests that 10 new emergency shelters can reduce the number of accessible blind areas by 43.31%. The study recommends that the assessment and construction of emergency shelter facilities in rural areas in China and globally should be emphasized. Reliable recommendations for improvement of emergency shelter planning in Deyang city are provided in the study results.

PurposeThe purpose of this study is to discuss the principles and factors that influence the site selection of emergency medical facilities for public health emergencies. This paper discusses the selection of the best facilities from the available facilities, proposes the capacity of new facilities, presents a logistic regression model and establishes a site selection model for emergency medical facilities for public health emergencies in megacities.Design/methodology/approachUsing Guangzhou City as the research object, seven alternative facility points and the points' capacities were preset. Nine demand points were determined, and two facility locations were selected using genetic algorithms (GAs) in MATLAB for programing simulation and operational analysis.FindingsComparing the results of the improved GA, the results show that the improved model has fewer evolutionary generations and a faster operation speed, and that the model outperforms the traditional P-center model. The GA provides a theoretical foundation for determining the construction location of emergency medical facilities in megacities in the event of a public health emergency.Research limitations/implicationsFirst, in this case study, there is no scientific assessment of the establishment of the capacity of the facility point, but that is a subjective method based on the assumption of the capacity of the surrounding existing hospitals. Second, because this is a theoretical analysis, the model developed in this study does not consider the actual driving speed and driving distance, but the speed of the unified average driving distance and the driving distance to take the average of multiple distances.Practical implicationsThe results show that the method increases the selection space of decision-makers, provides them with stable technical support, helps them quickly determine the location of emergency medical facilities to respond to disaster relief work and provides better action plans for decision makers.Social implicationsThe results show that the algorithm performs well, which verifies the applicability of this model. When the solution results of the improved GA are compared, the results show that the improved model has fewer evolutionary generations, faster operation speed and better model than the intermediate model GA. This model can more successfully find the optimal location decision scheme, making that more suitable for the location problem of megacities in the case of public health emergencies.Originality/valueThe research findings provide a theoretical and decision-making basis for the location of government emergency medical facilities, as well as guidance for enterprises constructing emergency medical facilities.

Pump location optimization is a key issue in the design of water pipe networks to reduce energy consumptions and associated costs. The pump location variables in previous papers were typically discrete, resulting in potentially sub-optimal solutions. If the discrete variables are densified, the solving time increases substantially. To overcome the shortcomings of the discrete pump location optimization method, a novel continuous location optimization method is proposed. The hydraulic calculations of pipes and operation of pumps are coupled in the model to make it practical. The objective function minimizes the installation and operating costs of pumps. A branch and a looped water pipe network are studied. The proposed continuous location optimization method is compared with the previous discrete method. The results show that the proposed method has advantages regarding cost-minimization and it reduces the time required to produce solutions. The method is generic and can be applied to other real cases to determine the optimal pump locations to achieve the goals of cost-minimization and energy saving.

We have developed a support vector regression (SVR) accelerated variant of the distributed derivative-free optimization (DFO) method using the limited-memory BFGS Hessian updating formulation (LBFGS) for subsurface field-development optimization problems. The SVR-enhanced distributed LBFGS (D-LBFGS) optimizer is designed to effectively locate multiple local optima of highly nonlinear optimization problems subject to numerical noise. It operates both on single- and multiple-objective field-development optimization problems. The basic D-LBFGS DFO optimizer runs multiple optimization threads in parallel and uses the linear interpolation method to approximate the sensitivity matrix of simulated responses with respect to optimized model parameters. However, this approach is less accurate and slows down convergence. In this paper, we implement an effective variant of the SVR method, namely ε -SVR, and integrate it into the D-LBFGS engine in synchronous mode within the framework of a versatile optimization library inside a next-generation reservoir simulation platform. Because ε -SVR has a closed-form of predictive formulation, we analytically calculate the approximated objective function and its gradients with respect to input model variables subject to optimization. We investigate two different methods to propose a new search point for each optimization thread in each iteration through seamless integration of ε -SVR with the D-LBFGS optimizer. The first method estimates the sensitivity matrix and the gradients directly using the analytical ε -SVR surrogate and then solves a LBFGS trust-region subproblem (TRS). The second method applies a trust-region search LBFGS method to optimize the approximated objective function using the analytical ε -SVR surrogate within a box-shaped trust region. We first show that ε -SVR provides accurate estimates of gradient vectors on a set of nonlinear analytical test problems. We then report the results of numerical experiments conducted using the newly proposed SVR-enhanced D-LBFGS algorithms on both synthetic and realistic field-development optimization problems. We demonstrate that these algorithms operate effectively on realistic nonlinear optimization problems subject to numerical noise. We show that both SVR-enhanced D-LBFGS variants converge faster and thereby provide a significant acceleration over the basic implementation of D-LBFGS with linear interpolation.

The amount of internet traffic generated during mass public events is significantly growing in a way that requires methods to increase the overall performance of the wireless network service. Recently, legacy methods in form of mobile cell sites, frequently called cells on wheels, were used. However, modern technologies are allowing the use of unmanned aerial vehicles (UAV) as a platform for network service extension instead of ground-based techniques. This results in the development of flying base stations (FBS) where the number of deployed FBSs depends on the demanded network capacity and specific user requirements. Large-scale events, such as outdoor music festivals or sporting competitions, requiring deployment of more than one FBS need a method to optimally distribute these aerial vehicles to achieve high capacity and minimize the cost. In this paper, we present a mathematical model for FBS deployment in large-scale scenarios. The model is based on a location set covering problem and the goal is to minimize the number of FBSs by finding their optimal locations. It is restricted by users’ throughput requirements and FBSs’ available throughput, also, all users that require connectivity must be served. Two meta-heuristic algorithms (cuckoo search and differential evolution) were implemented and verified on a real example of a music festival scenario. The results show that both algorithms are capable of finding a solution. The major difference is in the performance where differential evolution solves the problem six to eight times faster, thus it is more suitable for repetitive calculation. The obtained results can be used in commercial scenarios similar to the one used in this paper where providing sufficient connectivity is crucial for good user experience. The designed algorithms will serve for the network infrastructure design and for assessing the costs and feasibility of the use-case.

This study researches the dynamical location optimization problem of a mobile charging station (MCS) powered by a LiFePO 4 battery to meet charging demand of electric vehicles (EVs). In city suburbs, a large public charging tower is deployed to provide recharging services for MCS. The EV’s driver can reserve a real-time off-street charging service on the MCS through a vehicular communication network. This study formulates a multi-period nonlinear flow-refueling location model (MNFRLM) to optimize the location of the MCS based on a network designed by Nguyen and Dupuis (1984). The study transforms the MNFRLM model into a linear integer programming model using a linearization algorithm, and obtains global solution via the NEOS cloud CPLEX solver. Numerical experiments are presented to demonstrate the model and its solution algorithm.

One of the most complex problems in the field of upstream oil and gas industry is to optimally determine the location of production and injection wells. To do so, a variety of tools have been employed by reservoir engineers, including simplified reservoir models, reservoir quality maps, and automatic optimization techniques. Although the use of automatic optimization algorithms has facilitated the process of solving the optimization problem, one of the existing challenges in this regard is the selection of an appropriate algorithm that can avoid local optima and provide practically feasible results. In this study, the Shuffled Frog Leaping Algorithm (SFLA) was used, to the best of our knowledge for the first time, in a well placement problem to find the optimal location of production and injection wells. Two standard benchmark reservoir models were used to test the performance of the algorithm. The results were compared to those obtained by two most used optimization algorithms in the field of well location optimization, including the Particle Swarm Optimization and Genetic Algorithm. Results revealed that the SLF algorithm achieved better results in terms of higher objective function values and better well spacing both in intermediate and late stages of the optimization compared to the other algorithms. Also, the SFLA showed the most stable and smoothest progress among the algorithms.

Storage is an important part of commodity circulation. A certain amount of material must be stored to meet the needs of social production and consumption within a certain time to maintain the smooth process of social reproduction. This study focuses on warehousing optimization and goods location assignment when electronic products are stored in a stereoscopic storehouse. Moreover, this study is based on a theoretical study on genetic algorithm. On the basis of the background of the current warehouse management and cargo distribution of LCM module products warehouse belonging to W company, this study uses the dynamic goods location assignment strategy of stochastic inventory, and builds a multi-objective goods location assignment model of a stereoscopic warehouse. To simplify the calculations and improve the efficiency, we conduct a Matlab simulation on the basis of practical data by adopting a modified genetic operator and converting multi-objective optimization by the changing weight coefficient. The adaptive genetic algorithm can be used to make a multi-objective goods location assignment model that efficiently converges to the optimal solution.

The equitable allocation of COVID-19 vaccines is a critical challenge worldwide, given that the pandemic has been disproportionally affecting economically disadvantaged racial and ethnic groups. In the United States, the ongoing implementation efforts at different administrative levels and districts, to some extent, are standing in conflict with commitments to mitigate inequities. In this study, we developed a spatial optimization model to choose the best locations for vaccination sites. The model is a modified two-step maximal covering location problem (MCLP). It aims at maximizing the number of residents who can conveniently access the sites and mitigating inequity issues by prioritizing disadvantaged population groups who live in geographic areas identified through the CDC’s Social Vulnerability Index (SVI). We conducted our study using the case of Hillsborough County, Florida. We found that by reserving up to 30% of total vaccines for highly vulnerable communities, our model can optimize location choices for vaccination sites to provide effective coverage for residents at large while prioritizing disadvantaged groups of people. A series of sensitivity analyses have been performed to evaluate the impact of parameters such as site capacity and distance threshold. The model has the potential to guide the future allocation of critical medical resources in the U.S. and other countries.