Use of distributed data sources in facility location

Facility location decisions are usually determined by cost and coverage related factors although empirical studies show that such factors as infrastructure, labor conditions and competition also play an important role in practice. The objective of this paper is to develop a multi-objective facility location model accounting for a wide range of factors affecting decision-making. The proposed model selects potential facilities from a set of pre-defined alternative locations according to the number of customers, the number of competitors and real-estate cost criteria. However, that requires large amount of both spatial and non-spatial input data, which could be acquired from distributed data sources over the Internet. Therefore, a computational approach for processing input data and representation of modeling results is elaborated. It is capable of accessing and processing data from heterogeneous spatial and non-spatial data sources. Application of the elaborated data gathering approach and facility location model is demonstrated using an example of fast food restaurants location problem.     

Querying Multiple Data Sources via an Object-Oriented Spatial Query Interface and Framework

A spatial query interface has been designed and implemented in the object-oriented paradigm for heterogeneous data sets. The object-oriented approach presented is shown to be highly suitable for querying typical multiple heterogeneous sources of spatial data. The spatial query model takes into consideration two common components of spatial data: spatial location and attributes. Spatial location allows users to specify an area or a region of interest, also known as a spatial range query. Also, the spatial query allows users to query spatial orientation and relationships (geometric and topological relationships) among other spatial data within the selected area or region. Queries on the properties and values of attributes provide more detailed non-spatial characteristics of spatial data. A query model specific to spatial data involves exploitation of both spatial and attribute components. This paper presents a conceptual spatial query model of heterogeneous data sets based on the object-oriented data model used in the geospatial information distribution system (GIDS).     

基于互联网的市场行情可视化信息系统

提出了基于Internet的空间数据和非空间数据分布存储的空间数据仓库模型，并将其与数据挖掘技术相结合，开发了基于Internet的农产品市场行情时空演变可视化信息系统。该系统由来自Web网页的数据源、驻留Web数据自动抓取工具和非空间数据库的服务器以及驻留空间数据库、具有可视化展示和空间数据挖掘功能的客户机组成。为提高系统客户机的运行效率，系统采用了多种策略，如分布存储策略、瘦客户机策略、模糊匹配以及批处理策略等。系统具有界面友好、操作简便、可视化程度高等优点。     

A performance evaluation framework for association mining in spatial data

The evaluation of the process of mining associations is an important and challenging problem in database systems and especially those that store critical data and are used for making critical decisions. Within the context of spatial databases we present an evaluation framework in which we use probability distributions to model spatial regions, and Bayesian networks to model the joint probability distribution and the structural relationships among spatial and non-spatial predicates. We demonstrate the applicability of the proposed framework by evaluating representatives from two well-known approaches that are used for learning associations, i.e., dependency analysis (using statistical tests of independence) and Bayesian methods. By controlling the parameters of the framework we provide extensive comparative results of the performance of the two approaches. We obtain measures of recovery of known associations as a function of the number of samples used, the strength, number and type of associations in the model, the number of spatial predicates associated with a particular non-spatial predicate, the prior probabilities of spatial predicates, the conditional probabilities of the non-spatial predicates, the image registration error, and the parameters that control the sensitivity of the methods. In addition to performance we investigate the processing efficiency of the two approaches.     

Integrated use of fuzzy c-means and convex programming for capacitated multi-facility location problem

In this study a fuzzy c-means clustering algorithm based method is proposed for solving a capacitated multi-facility location problem of known demand points which are served from capacitated supply centres. It involves the integrated use of fuzzy c-means and convex programming. In fuzzy c-means, data points are allowed to belong to several clusters with different degrees of membership. This feature is used here to split demands between supply centers. The cluster number is determined by an incremental method that starts with two and designated when capacity of each cluster is sufficient for its demand. Finally, each group of cluster and each model are solved as a single facility location problem. Then each single facility location problem given by fuzzy c-means is solved by convex programming which optimizes transportation cost is used to fine-tune the facility location. Proposed method is applied to several facility location problems from OR library (Osman & Christofides, 1994) and compared with centre of gravity and particle swarm optimization based algorithms. Numerical results of an asphalt producer’s real-world data in Turkey are reported. Numerical results show that the proposed approach performs better than using original fuzzy c-means, integrated use of fuzzy c-means and center of gravity methods in terms of transportation costs.     

Modeling data envelopment analysis (DEA) efficient location/allocation decisions

Many types of facility location/allocation models have been developed to find optimal spatial patterns with respect to various location criteria that include cost, time, coverage, and access among others. In this paper we develop and test location modeling formulations that utilize data envelopment analysis (DEA) efficiency measures to find optimal and efficient facility location/allocation patterns. We believe that solving for the DEA efficiency measure, simultaneously with other location modeling objectives, provides a promising rich approach to multiobjective location problems.     

空间数据立方体的建模方法研究

研究了基于空间数据仓库的一种决策分析工具——空间在线分析处理(OLAP)的支撑技术。将普通数据立方体与空间数据立方体进行比较,提出空间数据立方体的维和度量的建模方法,解决了空间维与非空间维、空间度量与数值度量的集成建模问题。     

Comparison of local outlier detection techniques in spatial multivariate data

Outlier detection techniques in spatial data should allow to identify two types of outliers: global and local ones. Local outliers typically have non-spatial attributes that strongly differ from those observed on their neighbors. Detecting local outliers requires to be able to work locally, on neighborhoods, in order to take into account the spatial dependence between the statistical units under consideration, even though the outlyingness is usually measured on the non-spatial variables. Many procedures have been outlined in the literature, but their number reduces when one wants to deal with multivariate non-spatial attributes. In this paper, focus is on the multivariate context. A review of existing procedures is done. A new approach, based on a two-step improvement of an existing one, is also designed and compared with the benchmarked methods by means of examples and simulations.     

Cost modeling of spatial operators using non-parametric regression

In an object-relational database management system, a query optimizer requires users to provide cost models of user-defined functions. The traditional approach is analytical, that is, it builds a cost model generated as a result of analyzing the query processing steps. This analytical approach is difficult, however, especially for spatial query operators because of the complexity of the processing steps. In this paper, a new approach that uses non-parametric regression is proposed. This approach significantly simplifies the process of building a cost model, while achieving highly accurate cost estimation. We demonstrate the simplicity and efficacy of this approach through experiments for three spatial operators—the range query, the window query, and the k-nearest neighbor query—commonly used in spatial databases, using both real and synthetic data sets.     

Aggregation without Loss of Optimality in Competitive Location Models

In the context of competitive facility location problems demand points often have to be aggregated due to computational intractability. However, usually this spatial aggregation biases the value of the objective function and the optimality of the solution cannot be guaranteed for the original model. We present a preprocessing aggregation method to reduce the number of demand points which prevents this loss of information, and therefore avoids the possible loss of optimality. It is particularly effective in the frequent situation with a large number of demand points and a comparatively low number of potential facility sites, and coverage defined by spatial nearness. It is applicable to any spatial consumer behaviour model of covering type. This aggregation approach is applied in particular to a Competitive Maximal Covering Location Problem and to a recently developed von Stackelberg model. Some empirical results are presented, showing that the approach may be quite effective. This research was partially supported by the projects OZR1067 and SEJ2005-06273ECON.     

空间离群点的模型与跳跃取样查找算法

目前无论是查找一般的离群点，还是空间离群点，都强调非空间属性的偏离，但在图像处理、基于位置的服务等许多应用领域，空间与非空间属性要综合考虑。为此，首先提出了一个综合考虑两者的空间离群点定义，然后提出了一种新的基于密度的空间离群点查找方法——基于密度的跳跃取样空间离群点查找算法DBSODLS。由于已有的基于密度的离群点查找方法对每一点都要求进行邻域查询计算，故查找效率低，而该算法由于可充分利用已知的邻居信息，即不必计算所有点的邻域，从而能快速找到空间离群点。分析与试验结果表明，该算法时间性能明显优于目前已有的基于密度的算法。     

两种坐标校正方法的精度仿真分析

坐标校正方法的恰当选取是利用纸质地图进行空间数据录入过程中的重要步骤,它严重地影响着空间数据的质量。基于地理坐标的校正法与基于投影坐标的校正法是目前普遍使用的两种坐标校正方法。该文以MapGIS软件为主要工具,对基于高斯-克吕格(Gauss-Krüger)投影的多种比例尺地图分别进行了两种不同坐标校正过程的仿真,得到了不同坐标校正方法的误差值统计特征,定量地比较了两者之间的优劣性。对测量数据处理、计算机制图及空间数据生产具有指导意义。     

A multi-objective approach for determining optimal air compressor location in a manufacturing facility

Determining the optimal location of an air compressor in a manufacturing facility is a challenging problem that can offer significant energy savings. A novel simulation-optimization model is proposed to increase energy efficiency in a facility by determining optimal air compressor location. The optimization strategy is based on an objective function that minimizes the total energy consumption of the air compressor – hence, the energy cost for the facility – while considering the user's preference for the air compressor location. The proposed mathematical model first integrates the facility's characteristics based on user inputs, divides the facility into zones, and generates a rectilinear zone-to-zone distance matrix within the facility. The user location preference is incorporated into the proposed model via a five level user-preference index, assigned using preferential locations as suggested by twenty-two experienced facility managers. A sensitivity analysis is conducted to determine the relationship between the selected user preference level and the resulting energy consumption at each location in the facility. A simulation-driven analysis is performed using a real-life facility layout and typical compressed air equipment with corresponding nameplate data. In order to investigate and demonstrate the effectiveness of the proposed approach, the derived optimal zones are compared with five zones, including the most energy efficient zone, least energy efficient zone, and three other zones selected at random. The results of our study reveal that the proposed method achieves significant energy reductions while maintaining the user's desired air compressor location.     

一种有效的空间数据仓库区域聚集查询索引结构

空间数据仓库有效地支持对空间数据的管理和分析,提供更加全面的决策支持．讨论了一种有效的空间决策支持手段——空间区域聚集查询的实现．基于aggregate cubetree和aR—tree提出了一个可以有效地在空间维和非空间维上进行区域聚集查询的索引结构aCR-tree及其相关算法,并计算分析了查询算法的时间复杂度．与现有技术相比aCR-tree降低了存储代价和每次查询访问的节点数,通过实验证明,该索引结构可以提供较好的存储性能和查询性能．     

基于ArcSDE与Hibernate的空间数据库混合访问方法

为了解决在GIS运用系统中,直接使用ArcSDE访问空间属性数据效率较低的问题,分析和比较了在GIS应用系统中基于ArcSDE的空间数据访问方法及在非空间数据库系统中广泛使用的Hibernate访问机制,在此基础上提出一种在Java平台下基于ArcSDE和Hibernate的空间数据库混合访问方法.该方法通过ArcSDE及ArcEngme对空间属性数据访问,对非空间属性数据则通过Hibernate进行访问,进一步提高了空间数据库的访问效率.     

Supply facility and input/output point locations in the presence of barriers

This paper studies a facility location model in which two-dimensional Euclidean space represents the layout of a shop floor. The demand is generated by fixed rectangular-shaped user sites and served by a single supply facility. It is assumed that (i) communication between the supply point and a demand facility occurs at an input/output (I/O) point on the demand facility itself, (ii) the facilities themselves pose barriers to travel and (iii) distance measurement is as per the L₁-metric. The objective is to determine optimal locations of the supply facility as well as I/O points on the demand facilities, in order to minimize total transportation costs. Several, increasingly more complex, versions of the model are formulated and polynomial time algorithms are developed to find the optimal locations in each case.Scope and purposeIn a facility layout setting, often a new central supply facility such as a parts supply center or tool crib needs to be located to serve the existing demand facilities (e.g., workstations or maintenance areas). The demand facilities are physical entities that occupy space, that cannot be traveled through, and that receive material from the central facility, through a perimeter I/O (input/output or drop-off/pick-up) point. This paper addresses the joint problem of locating the central facility and determining the I/O point on each demand facility to minimize the total material transportation cost. Different versions of this problem are considered. The solution methods draw from and extend results of location theory for a class of restricted location problems. For practitioners, simple results and polynomial time algorithms are developed for solving these facility (re) design problems.     

Improvement of transportation cost estimation for prefabricated construction using geo-fence-based large-scale GPS data feature extraction and support vector regression

In panelized construction, transportation is an essential process linking a manufacturing facility to a project’s jobsite using hauling equipment (e.g., trucks and trailers). Accordingly, the cost associated with transportation operations is considerable compared to a traditional stick build. Nevertheless, transportation cost estimation has often relied on a fixed-cost approach, regarding the cost as part of the overhead cost, rather than conducting detailed estimation of actual transportation operations. This is because operation-level data might be challenging to collect and analyze in practice. In this regard, the prevalent use of GPS devices for construction equipment may provide an automated means of monitoring the operations of transportation equipment, and large and detailed spatial and temporal data can be generated from multiple pieces of equipment in multiple construction projects on a daily basis or even in real time. This study thus proposes a spatial and temporal data filtering and abstracting approach to transportation cost estimation using fleet GPS data which extracts equipment activities from the GPS data and accordingly predicts the transportation demands required for an individual project. From large-scale GPS data, key operation information, such as the number of trailers and durations required (i.e., transportation demands), is extracted using a geo-fence and a rule-based equipment operation analysis algorithm. Then, the extracted transportation demand information, along with related project specifications, is used to train support vector regression (SVR) models for the purpose of predicting the transportation demand in new projects, which is in turn utilized to estimate the transportation cost using the relevant transportation unit cost of the equipment. To evaluate the performance, GPS datasets collected from 221 panelized residential projects over a period of 8 months are used to train the prediction model and are compared with actual transportation costs estimated in practice. The results show that the SVR model has an accuracy of 86% and 88% in predicting the number of trailers and the duration, respectively. For the cost estimation performance, the results reveal that the average cost difference of 57% between the fixed cost and the actual transportation cost was reduced to 14% by implementing the GPS-data-based method in various project locations and for projects of various sizes. The GPS-data-based estimation approach thus is found to provide a more accurate transportation cost estimation result for various panelized construction projects, and the method improves the understanding of large-scale spatial and temporal equipment data while increasing the utilization of the GPS data already available.     

Solving the single-item, capacitated dynamic lot-sizing problem with startup and reservation costs by tabu search

A capacitated, single-item lot-sizing model is considered where a startup cost is incurred for switching the production facility on and a separate reservation cost is incurred for keeping the facility on whether it is used for production or not. Computationally, the problem corresponding to this model is NP-hard. The Tabu search scheme developed for solving the problem proved capable of reaching the optimal solution for a large number of varied problem instances tested. Characteristics of the problem relevant to the development of the Tabu search scheme are elaborated and results of computational testing are presented.     

基于客户/服务器结构的空间数据分布式处理研究

该文简要分析了当前空间数据的分布式处理状况,以面向跨区域的地籍管理应用为例,利用空间与非空间数据库的集成和基于客户／服务器体系结构的分布式系统技术,设计了空间数据的分布式处理的方案。阐述了具有空间分布特征的地籍数据库的分布式设计,详细介绍了其中的数据分布与分布透明、空间数据库与非空间的关系数据库集成、分布式查询处理及数据一致等内容。