Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.

Comparison of two theoretical models for electric leak location at landfill

To solve the disability of conventional model used in electrical leak location when measurement electrodes were buried under the liner, a new model of high voltage DC leak detection is developed. For single-liner landfill, the waste material layer, the geomembrane liner and the soil under the liner are simulated with infinite horizontal layers. The leak is regarded as two parts, one being negative current source at the entrance, and the other positive current source of the same size at the exit. Comparisons between the new theoretical model and conventional model show that conventional model is efficient in locating leaks in geomembane liner associating the dipole scanning above the liner but is ineffective when the measurement electrodes were buried under the liner. The new theoretical model data are in excellent agreement with experimental data not only above the liner but also under the liner.

Joint modelling of location and scale parameters of the skew-normal distribution

Joint location and scale models of the skew-normal distribution provide useful ex- tension for joint mean and variance models of the normal distribution when the data set under consideration involves asymmetric outcomes. This paper focuses on the maximum likelihood estimation of joint location and scale models of the skew-normal distribution. The proposed procedure can simultaneously estimate parameters in the location model and the scale model. Simulation studies and a real example are used to illustrate the proposed methodologies.

A skew–normal mixture of joint location, scale and skewness models

Normal mixture regression models are one of the most important statistical data analysis tools in a heterogeneous population. When the data set under consideration involves asymmetric outcomes, in the last two decades, the skew normal distribution has been shown beneficial in dealing with asymmetric data in various theoretic and applied problems. In this paper, we propose and study a novel class of models： a skew-normal mixture of joint location, scale and skewness models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population. The issues of maximum likelihood estimation are addressed. In particular, an Expectation-Maximization （EM） algorithm for estimating the model parameters is developed. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo experiments. Results from the analysis of a real data set from the Body Mass Index （BMI） data are presented.

Modeling the criminal's location choices in urban areas

With the method of dynamic programming, two spatial variables,the expected utility and the probability of success of each crime, are used to model the criminal's location choices in urban areas in this paper.The modeling results show that a criminal optimizes his crime locations according to the expected utility and the success probability during his planned period A criminal usually commits his first offense in the district that has the highest probability of success but a lower expected utility, and commits his last crime in the district where the expected utility is the highest and success probability is lower.If a location has both an expected utility and a higher probability of success, the criminal might commit all his offenses in thes place. The model also suggests that crime prevention measures should be adopted in accordance with local conditions. 'Covering' measures, such as patrolling, should be taken in the poor residential districts or juvenile delinquency districts, while more sophisticated and advanced measures should be introduced in the richer districts or the districts where professional criminals haunt.

Accurate Fault Location Modeling for Parallel Transmission Lines Considering Mutual Effect

:A new accurate algorithms based on mathematical modeling of two parallel transmissions lines system(TPTLS)as influenced by the mutual effect to determine the fault location is discussed in this work.The distance relay measures the impedance to the fault location which is the positive-sequence.The principle of summation the positive-,negative-,and zero-sequence voltages which equal zero is used to determine the fault location on the TPTLS.Also,the impedance of the transmission line to the fault location is determined.These algorithms are applied to single-line-to-ground(SLG)and double-line-to-ground(DLG)faults.To detect the fault location along the transmission line,its impedance as seen by the distance relay is determined to indicate if the fault is within the relay’s reach area.TPTLS under study are fed from one-and both-ends.A schematic diagrams are obtained for the impedance relays to determine the fault location with high accuracy.

耦合优化蚁群算法与P-Median model的选址模型设计

为节省城建部门对于公共体育设施的投入成本以及提高城市人民生活质量,以运动场所优化选址为例,提出一种新型设施选址模型。该模型主要基于P-Median model(最小化阻抗模型)根据需求点数量从全部候选设施选址中选择设施空间位置,让用户达到离自己最近设施距离成本总和最小的目的,对选址的基本原则和实际情况提出要求,构造目标函数用于优化后蚁群算法求解进行选址工作。优化蚁群算法实现基于Python语言模块,通过改进蚁群原始信息素,提升原有算法的收敛速度,求出目标函数最优解,可以很好地模拟对于运动场所的选址。用二者耦合进行优势互补所设计的选址模型来搜寻研究区蚁群信息素浓度残留最大的栅格像元,从而确定未被已有设施点服务半径覆盖的最佳设施点建立位置。实验结果表明,该新型选址模型相较于最小化阻抗模型与最大化覆盖模型,新增优化设施点使整体服务半径覆盖率分别高出10.42%和6.95%,适合求解较为精确且小规模空间下的选址问题。

Foreign Direct Investment with Chinese Characteristics:A Middle Path Between Ownership-Location-Internalization Model and Linkage-Leverage-Learning Model

The majority of multinational enterprises(MNEs) traditionally originate from developed countries.In the last ten years,however,there has been dramatic growth in foreign direct investment(FDI) from China.It is a comparatively new phenomenon that challenges the classic FDI theories.In this paper,we review the pros and cons of two important theories,known as the Ownership-Location-Internalization(OLI) model and Linkage-Leverage-Learning(LLL) model,and use the statistical data and company case studies from China to test the plausibility of these two models.We believe that neither of them suits totally: the OLI model is quite useful for understanding FDI from China to developing economies,while the LLL model is more powerful for explaining the FDI to developed economies.We argue that the companies from China attain a very advantageous position as intermediates in the global economy.They may catch up with the first movers if they integrate OLI-led and LLL-led FDI within one firm.This combination can bring together the most advanced knowledge acquired in developed economies with the knowledge about adaptation needs and the needs for cost reduction in production as expressed in developing economies.It may also accelerate the knowledge transfer globally.We thus fill a gap in research into the geographical pattern of Chinese FDI and offer a deeper understanding of the internationalization of Chinese MNEs and revolving knowledge transfer.

Improvements in seismic event locations in a deep western U.S. coal mine using tomographic velocity models and an evolutionary search algorithm

Methods of improving seismic event locations were investigated as part of a research study aimed at reducing ground control safety hazards. Seismic event waveforms collected with a 23-station three-dimensional sensor array during longwall coal mining provide the data set used in the analyses. A spatially variable seismic velocity model is constructed using seismic event sources in a passive tomographic method. The resulting three-dimensional velocity model is used to relocate seismic event positions. An evolutionary optimization algorithm is implemented and used in both the velocity model development and in seeking improved event location solutions. Results obtained using the different velocity models are compared. The combination of the tomographic velocity model development and evolutionary search algorithm provides improvement to the event locations.

The Dynamic Location Model to Consider Background Traffic

This study concerns to the determination of location of freight distribution warehouses. It is part of a series of research projects on a distribution system we developed to deal with cases in a public service obligation state-owned company (PSO-SOC). This current research is characterized by the consideration of background traffic of the entire time period of planning rather than one certain time target on location model. It is aimed that the location decision to be more applicable and accommodative to the dynamic of the traffic condition. Once the decision is implemented, it will give the best outcome for the entire time period, not only for the initial time, end time or certain time of time period. A heuristic approach is proposed to simplify complexity of the model and network representation technique is applied to solve the model. A hyphotetical example is discussed to illustrate the mechanism of finding the optimal solution in term of both its objective function and applicability.

Terminal Location Models for Intermodal Transport Network Optimization

Since rail-road transport uses road and rail networks and requires the transshipment infrastructures at the terminals, its competitiveness depends not only on the costs but also on the location of these terminals. This paper focused on providing a methodology for determining the optimal locations for intermodal freight transportation terminals in consolidation network. The goal is to minimize total costs in order to increase the efficiency of the transportation system. This paper also has allowed us to have an overview of the methods and models that exist for solving the problem of intermodal and terminal locating.

Variable selection for skew-normal mixture of joint location and scale models

Although there are many papers on variable selection methods based on mean model in the nite mixture of regression models,little work has been done on how to select signi cant explanatory variables in the modeling of the variance parameter.In this paper,we propose and study a novel class of models:a skew-normal mixture of joint location and scale models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population.The problem of variable selection for the proposed models is considered.In particular,a modi ed Expectation-Maximization(EM)algorithm for estimating the model parameters is developed.The consistency and the oracle property of the penalized estimators is established.Simulation studies are conducted to investigate the nite sample performance of the proposed methodolo-gies.An example is illustrated by the proposed methodologies.

Comparative Geospatial Planning Model for “Location Specific” Intervention and Continuous Improvement Strategy

Sustainable development can only be achieved by conscious planning and implementation of action plans. Decision making requires a careful selection of the right conceptual framework and models for planning and implementation processes. Planning process models dictate in very clear terms what must be done and how it is done to achieve a successful completion of a process of activity [1]. Since about 80% of data used to support decisions are geographically related [3], it is necessary to put Geographic Information Systems (GIS) at the core of the planning and implementation model. There exists a great disparity in a heterogeneous world. The locational disparity in achieving sustainable development, therefore, necessitates a planning model that is “location specific” i.e. identifies areas (locations) requiring intervention and areas (locations) requiring continuous improvement strategies. This was achieved in this study by reviewing Bell’s Information System Strategic Planning Model and Kaufman’s Strategic Planning Model, and the designing of new model to overcome the limitation of existing models. Practical application of the new model was carried out in education planning and administration in order to achieve the global goals for sustainable development 4 (quality education). Finding shows that the Comparative Geospatial Planning Model for “Location Specific” Intervention and Continuous Improvement Strategy is useful to support the achievement of sustainable development goals in multidisciplinary, multi-sector applicable instances.

Integration of Location-Allocation and Accessibility Models in GIS to Improve Urban Planning for Health Services in Al-Madinah Al-Munawwarah, Saudi Arabia

In this study, accessibility and location-allocation models have been integrated into GIS to improve spatial planning and environmental sustainability of health services in Al-Madinah Al-Munawwarah. This integration provides a planning framework in order to check the efficiency of the spatial allocation of health services and to generate alternatives either by proposing an active service or to improve an existing one. To achieve these objectives, the accessibility to the service area was analyzed within the analysis of health services networks, which are divided into eight types: public hospitals, specialized hospitals, health units, healthcare centers, infirmaries, clinic complexes, the Red Crescent Center, and ambulance facilities, with time intervals of (5 minutes - 10 minutes - 15 minutes) to access coverage ranges, and the location-allocation model was used based on the maximum coverage model within a response time not exceeding 15 minutes, The results of the study revealed the poor distribution of health services Al-Madinah Al-Munawwarah suffers from weak accessibility to health services coverage areas and is unable to meet the needs of its population at present. The current need for health services reached twenty-four locations, including two public hospitals, three specialized hospitals, two health centers, three ambulance facilities, four infirmaries, three clinic complexes, four health units, and three Red Crescent centers.

基于指定特征的加权co-location模式挖掘方法

co-location模式是空间特征集的一个子集,其特征实例在地理空间中频繁出现互相近邻,基于特征参与率进行模式挖掘,特征参与率定义为模式表实例中不重复的实例个数与特征总实例数的比率。针对基于指定特征的模式特征实例并置程度满足模式指导性要求,但部分特征总实例数过多而导致特征参与率小于阈值,模式被界定为非频繁模式的情况,提出一种基于指定特征的加权co-location模式挖掘方法。定义特征的权以及特征加权参与率计算规则,可以有效挖掘基于指定特征的加权co-location模式,其加权参与度随着模式阶数的增大而单调递减。实验结果证明了该算法在挖掘结果及算法运行时间上的有效性。

Vehicle Real-time Location Based on Visual Perception Model

Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.

Pervasive Computing Location-aware Model Based on Ontology

In order to integrate heterogeneous location-aware systems into pervasive computing environment,a novel pervasive computing location-aware model based on ontology is presented.A location-aware model ontology(LMO)is constructed.The location-aware model has the capabilities of sharing knowledge,reasoning and adjusting the usage policies of services dynamically through a unified semantic location manner.At last,the work process of our proposed location-aware model is explained by an application scenario.

径流预报误差的混合t Location-Scale分布模型及应用

基于混合t Location-Scale分布良好的自适应性,利用k均值聚类法挖掘数据隐含结构、良好的聚类效果特点,建立了不同预见期径流预报误差混合t Location-Scale分布模型。将模型应用于雅砻江流域官地水库,对预见期分别为6、12、18 h和24 h的区间径流预报误差进行了分析与建模,结果发现混合t Location-Scale分布模型弥补了单一分布难以描述径流预报误差特征多样性的局限,能更准确地描述不同预见期径流预报误差的统计特征,误差分布与实测径流预报误差的变化规律一致,可为水库水电站的径流预报和调度运行提供更加准确、可靠的来水数据。

Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage

Damage caused by people and organizations unconnected with the pipeline management is a major risk faced by pipelines,and its consequences can have a huge impact.However,the present measures to monitor this have major problems such as time delays,overlooking threats,and false alarms.To overcome the disadvantages of these methods,analysis of big location data from mobile phone systems was applied to prevent third-party damage to pipelines,and a third-party damage prevention system was developed for pipelines including encryption mobile phone data,data preprocessing,and extraction of characteristic patterns.By applying this to natural gas pipelines,a large amount of location data was collected for data feature recognition and model analysis.Third-party illegal construction and occupation activities were discovered in a timely manner.This is important for preventing third-party damage to pipelines.