Integrating simulation modelling and GIS: spatial decision support systems for evacuation planning

A prototype spatial decision support system (SDSS) has been designed for contingency planning for emergency evacuations which combines simulation techniques with spatial data handling and display capabilities of a geographical information system (GIS). It links together the topographical support and analysis provided by the GIS–ARC/INFO, with a simulation model designed to simulate the dynamics of an evacuation process in detail. Our aim has been to design a SDSS so that it provides an interactive evacuation simulator with dynamic graphics that allows for experimentation with policies by providing rapid feedback from the simulation. The idea is that emergency planners will be able to use the SDSS to experiment with emergency evacuation plans in order to plan for different contingencies. This paper concentrates on the issues involved in designing an effective integration link interface between the GIS and the simulation model when building a SDSS of this type.     

Strategic route extension in transit networks

This study proposes a methodology through which transportation analysts and policy makers can use spatial optimization to support strategic planning, with the goal of extending existing service networks. Based on modeling objectives common to many service industries, an approach is developed for integrating geographic information systems (GIS) and spatial optimization modeling in order to extend an existing transit system through prioritizing route and stop additions. Development of a strategic methodology such as this is vital for agencies interested in extending transit networks to accommodate urban growth and development. This is especially true in public transit applications, such as bus route planning, as the future of bus-based public transportation depends on the success of route expansion and modification. The developed approach is applied to the transit system in Columbus, Ohio.     

A computational approach for eliminating error in the solution of the location set covering problem

The location set covering problem continues to be an important and challenging spatial optimization problem. The range of practical planning applications underscores its importance, spanning fire station siting, warning siren positioning, security monitoring and nature reserve design, to name but a few. It is challenging on a number of fronts. First, it can be difficult to solve for medium to large size problem instances, which are often encountered in combination with geographic information systems (GIS) based analysis. Second, the need to cover a region efficiently often brings about complications associated with the abstraction of geographic space. Representation as points can lead to significant gaps in actual coverage, whereas representation as polygons can result in a substantial overestimate of facilities needed. Computational complexity along with spatial abstraction sensitivity combine to make advances in solving this problem much needed. To this end, a solution framework for ensuring complete coverage of a region with a minimum number of facilities is proposed that eliminates potential error. Applications to emergency warning siren and fire station siting are presented to demonstrate the effectiveness of the developed approach. The approach can be applied to convex, non-convex and non-contiguous regions and is unaffected by arbitrary initial spatial representations of space.     

A Geostatistical Approach to Linking Geographically Aggregated Data From Different Sources

The widespread availability of digital spatial data and the capabilities of Geographic Information Systems (GIS) make it possible to easily synthesize spatial data from a variety of sources. More often than not, data have been collected at different geographic scales, and each of the scales may be different from the one of interest. Geographic information systems effortlessly handle these types of problems through raster and geoprocessing operations based on proportional allocation and centroid smoothing techniques. However, these techniques do not provide a measure of uncertainty in the estimates and lack the ability to incorporate important covariate information that may be used to improve the estimates. They also often ignore the different spatial supports (e.g., shape and orientation) of the data. On the other hand, statistical solutions to change-of-support problems are rather specific and difficult to implement. In this article, we present a general geostatistical framework for linking geographic data from different sources. This framework incorporates aggregation and disaggregation of spatial data, as well as prediction problems involving overlapping geographic units. It explicitly incorporates the supports of the data, can adjust for covariate values measured on different spatial units at different scales, provides a measure of uncertainty for the resulting predictions, and is computationally feasible within a GIS. The new framework we develop also includes a new approach for simultaneous estimation of mean and covariance functions from aggregated data using generalized estimating equations.     

A Multiobjective Hybrid Metaheuristic Approach for GIS-based Spatial Zoning Model

This paper presents a multiobjective hybrid metaheuristic approach for an intelligent spatial zoning model in order to draw territory line for geographical or spatial zone for the purpose of space control. The model employs a Geographic Information System (GIS) and uses multiobjective combinatorial optimization techniques as its components. The proposed hybrid metaheuristic consists of the symbiosis between tabu search and scatter search method and it is used heuristically to generate non-dominated alternatives. The approach works with a set of current solution, which through manipulation of weights are optimized towards the non-dominated frontier while at the same time, seek to disperse over the frontier by a strategic oscillation concept. The general procedure and its algorithms are given as well as its implementation in the GIS environment. The computation has resulted in tremendous improvements in spatial zoning.     

A multi-objective evolutionary algorithm for facility dispersion under conditions of spatial uncertainty

Avoiding concentration or saturation of activities is fundamental in many environmental and urban planning contexts. Examples include dispersing retail and restaurant outlets, sensitivity to impacts in forest utilization, spatial equity of waste disposal, ensuring public safety associated with noxious facilities, and strategic placement of military resources, among others. Dispersion models have been widely applied to ensure spatial separation between activities or facilities. However, existing approaches rely on deterministic approaches that ignore issues of spatial data uncertainty, which could lead to poor decision making. To address data uncertainty issues in dispersion modelling, a multi-objective approach that explicitly accounts for spatial uncertainty is proposed, enabling the impacts of uncertainty to be evaluated with statistical confidence. Owing to the integration of spatial uncertainty, this dispersion model is more complex and computationally challenging to solve. In this paper we develop a multi-objective evolutionary algorithm to address the computational challenges posed. The proposed heuristic incorporates problem-specific spatial knowledge to significantly enhance the capability of the evolutionary algorithm for solving this problem. Empirical results demonstrate the performance superiority of the developed approach in supporting facility and service planning.     

Optimization of spatial tracks using a morphing approach

This paper describes an approach for optimizing multibody systems involving spatial tracks, addressing the problem of finding an optimal set of design parameters when no good initial guess is available. In this setting, it is common that, due to nonlinearities of the objective function and singularities in the kinematic model, the optimization fails to converge or to find feasible points at all. The present paper presents a three-stage procedure for sequentially guiding a standard optimization routine from an arbitrary initial guess to the optimal configuration. The approach has been tested when optimizing real roller coaster tracks with respect to passenger acceleration and compared with a genetic algorithm implementation, showing that objective function morphing renders faster for the regarded type of systems. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Envisioning uncertainty in geospatial information

Geospatial reasoning has been an essential aspect of military planning since the invention of cartography. Although maps have always been a focal point for developing situational awareness, the dawning era of network-centric operations brings the promise of unprecedented battlefield advantage due to improved geospatial situational awareness. Geographic information systems (GIS) and GIS-based decision support systems are ubiquitous within current military forces, as well as civil and humanitarian organizations. Understanding the quality of geospatial data is essential to using it intelligently. A systematic approach to data quality requires: estimating and describing the quality of data as they are collected; recording the data quality as metadata; propagating uncertainty through models for data processing; exploiting uncertainty appropriately in decision support tools; and communicating to the user the uncertainty in the final product. There are shortcomings in the state-of-the-practice in GIS applications in dealing with uncertainty. No single point solution can fully address the problem. Rather, a system-wide approach is necessary. Bayesian reasoning provides a principled and coherent framework for representing knowledge about data quality, drawing inferences from data of varying quality, and assessing the impact of data quality on modeled effects. Use of a Bayesian approach also drives a requirement for appropriate probabilistic information in geospatial data quality metadata. This paper describes our research on data quality for military applications of geospatial reasoning, and describes model views appropriate for model builders, analysts, and end users.     

Map-Route: a GIS-based decision support system for intra-city vehicle routing with time windows

This paper presents a Decision Support System (DSS) that enables dispatchers–schedulers to approach intra-city vehicle routing problems with time windows interactively, using appropriate computational methods and exploiting a custom knowledge base that contains information about traffic and spatial data. The DSS, named Map-Route, generates routes that satisfy time and vehicle capacity constraints. Its computational engine is based on an effective heuristic method for solving the underlying optimization problem, while its implementation is developed using MapInfo, a popular Geographical Information System (GIS) platform. Map-Route provides very efficient solutions, is particularly user-friendly, and can reach answers for a wide variety of ‘what if’ scenarios with potentially significant cost implications. We have implemented Map-Route in an actual industrial environment and we report on the experience gained from this real-life application.     

Spatial Lasso With Applications to GIS Model Selection

Geographic information systems (GIS) organize spatial data in multiple two-dimensional arrays called layers. In many applications, a response of interest is observed on a set of sites in the landscape, and it is of interest to build a regression model from the GIS layers to predict the response at unsampled sites. Model selection in this context then consists not only of selecting appropriate layers, but also of choosing appropriate neighborhoods within those layers. We formalize this problem as a linear model and propose the use of Lasso to simultaneously select variables, choose neighborhoods, and estimate parameters. Spatially dependent errors are accounted for using generalized least squares and spatial smoothness in selected coefficients is incorporated through use of a priori spatial covariance structure. This leads to a modification of the Lasso procedure, called spatial Lasso. The spatial Lasso can be implemented by a fast algorithm and it performs well in numerical examples, including an application to prediction of soil moisture. The methodology is also extended to generalized linear models. Supplemental materials including R computer code and data analyzed in this article are available online.     

Integrated spatial assessment: a multicriteria approach to sustainable development of cultural and environmental heritage in San Marco dei Cavoti,Italy

Starting from the concept of “territory” and from the definition of “integrated assessment”, the paper defines the operational dimensions of spatial integrated assessment (ISA), applying it to the territorial context of San Marco dei Cavoti (a rural village in Southern Italy). In particular, the paper proposes an integration between analytic hierarchy process (AHP) and geographic information system (GIS) for the strategies definition of planning choices, recognizing the relevant role of the environmental aspects in the decision-making process and alternatives selection.      

Integrating Geographical Information Systems and Multi-Criteria Methods: A Case Study

This paper presents an application of the integration between Geographical Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) to aid spatial decisions. We present a hypothetical case study to illustrate the GIS–MCDA integration: the selection of the best municipal district of Rio de Janeiro State, Brazil, in relation to the quality of urban life. The best municipal district is the one that presents the closest characteristics to those considered ideal by the decision-maker. The approach adopted is the Multi-Objective Linear Programming (MOLP) and the chosen method is the Pareto Race.     

EVALUATING ALTERNATIVE MANAGEMENT STRATEGIES FOR ABALONE

Industry participation in co‐management has become a cornerstone of assessing the blacklip abalone, Halitois rubra, fisheries of South‐Eastern Australia. Engaging industry in developing and implementing management strategies is aimed at stemming recent trends of stock depletion and reduced harvest. An alternative to the current strategy for determining harvests is under consideration. A central question is: which management strategy is most likely to maintain sufficient biomass to produce consistent harvest rates into the future? This question is addressed using a Systems Dynamics modeling approach. We evaluated the two strategies of harvest management planning under (i) unchanging environmental conditions and (ii) following a high‐mortality event. To assess the performance of each strategy, an existing abalone model was used to estimate yield and mature biomass over three decades (>5 generations). A strategy using mean length of commercial catch as a novel performance measure generally provided a better path to recovery from the high‐mortality event than the current harvest threshold strategy.     

Collaborative production planning of supply chain under price and demand uncertainty

This research is motivated by an automobile manufacturing supply chain network. It involves a multi-echelon production system with material supply, component fabrication, manufacturing, and final product distribution activities. We address the production planning issue by considering bill of materials and the trade-offs between inventories, production costs and customer service level. Due to its complexity, an integrated solution framework which combines scatter evolutionary algorithm, fuzzy programming and stochastic chance-constrained programming are combined to jointly take up the issue. We conduct a computational study to evaluate the model. Numerical results using the proposed algorithm confirm the advantage of the integrated planning approach. Compared with other solution methodologies, the supply chain profits from the proposed approach consistently outperform, in some cases up to 13% better. The impacts of uncertainty in demand, material price, and other parameters on the performance of the supply chain are studied through sensitivity analysis. We found the proposed model is effective in developing robust production plans under various market conditions.     

Mixed integer programming formulations for the Biomass Truck Scheduling problem

In this paper, we introduce the Biomass Truck Scheduling (BTS) problem that originated in a real-life herbaceous biomass supply chain (HBSC) around Pécs, Hungary. BTS can be considered as a Parallel Machine Scheduling with a Single Server problem, where identical trucks (parallel machines) deliver biomass from satellite storage locations to a central biorefinery operating a single unloader (single server). We make two particular assumptions regarding the server: the server operation has a unit time length for each trip and idle periods are not allowed for it (server no idle time constraint). We consider two objective functions associated with the revealed HBSC logistic cost structure. First, the number of trucks is minimized (resource availability cost) following which the total truck idle time is minimized. Three mixed integer programming formulations are constructed to solve BTS, and their efficiency is evaluated using a number of test cases. We found that, even if the number of trucks is locked at its minimum value, there is always a schedule with zero truck idle time—that is, there is no trade-off between these two objective functions.     

Process planning for circuit card assembly on a series of dual head placement machines

This paper proposes an approach for prescribing the inter-related decisions that prescribe a process plan for a series of dual head placement machines. The goal is to provide a means of rapidly prescribing a process plan that seeks to minimize cycle time (equivalently, maximize throughput rate) by balancing workloads assigned to heads when assembling a given type of circuit card. Our approach decomposes process planning decisions into four related problems. This paper explores list-processing heuristics for the first of these problems and adapts an optimizing method for the fourth. It also integrates these two methods with others to optimize the second and third of these problems, reporting computational tests that evaluate the overall approach to all four problems and assessing the degree to which the decomposition approach is able to balance workloads assigned and the run time required to do so. Resulting cycle times are also analyzed. These test results demonstrate that the overall approach provides a means of prescribing effective process plans within a run time acceptable to process planners.     

Introducing cost-plus-loss analysis into a hierarchical forestry planning environment

Cost-plus-loss analysis of data for forestry planning has often been carried out for highly simplified planning situations. In this study, we suggest an advance in the cost-plus-loss methodology that aims to capture the hierarchical structure and iterative nature of planning by the large forest owner. The simulation system that is developed to simulate the planning process of the forest owner includes the tactical and operational levels of a continuous planning process. The system is characterized by annual re-planning of the tactical plan with a planning horizon of ten year and with the option to reassess data for selected stands before operational planning. Operational planning is done with a planning horizon of two years and the first year of the plan is considered to have been executed before moving the planning process one year forward. The annual cycle is repeated 10 times, simulating decisions made over a ten-year time horizon. The optimizing planning models of the system consider wood flow requirements, available harvest resources, seasonal variation of ground conditions and spatiality. The data used are evaluated according to standard procedures in cost-plus-loss analysis. Results from a test case indicate high decision losses when planning at both levels is based on the type of data prevalent in the stand databases of Swedish companies. The losses can be reduced substantially if higher-quality data are introduced before operational planning. In summary, the results indicate that the method makes it possible to analyze where in the planning process one needs better data and their value.     

A Lagrangian decomposition approach for the pump scheduling problem in water networks

Dynamic pricing has become a common form of electricity tariff, where the price of electricity varies in real time based on the realized electricity supply and demand. Hence, optimizing industrial operations to benefit from periods with low electricity prices is vital to maximizing the benefits of dynamic pricing. In the case of water networks, energy consumed by pumping is a substantial cost for water utilities, and optimizing pump schedules to accommodate for the changing price of energy while ensuring a continuous supply of water is essential. In this paper, a Mixed-Integer Non-linear Programming (MINLP) formulation of the optimal pump scheduling problem is presented. Due to the non-linearities, the typical size of water networks, and the discretization of the planning horizon, the problem is not solvable within reasonable time using standard optimization software. We present a Lagrangian decomposition approach that exploits the structure of the problem leading to smaller problems that are solved independently. The Lagrangian decomposition is coupled with a simulation-based, improved limited discrepancy search algorithm that is capable of finding high quality feasible solutions. The proposed approach finds solutions with guaranteed upper and lower bounds. These solutions are compared to those found by a mixed-integer linear programming approach, which uses a piecewise-linearization of the non-linear constraints to find a global optimal solution of the relaxation. Numerical testing is conducted on two real water networks and the results illustrate the significant costs savings due to optimizing pump schedules.     

基于GIS与虚拟现实技术的土地整理规划研究

以湖北省赤壁市赤壁片土地整理规划为例,利用GIS(地理信息系统)与VR(虚拟现实技术)进行的土地整理规划,将实地测量数据在ArcGIS软件中进行数字化处理,采用GIS建立DEM(数字高程模型),同时将各种单体工程用三维制图软件3D MAX建模,并将3D模型嵌入在规划后的DEM中,获得规划后的虚拟场景,判断规划的合理性,同时对规划进行调整并加以完善.结果表明,基于GIS和VR技术进行土地整理规划,增强了规划后虚拟场景的仿真性,提高了土地整理规划结果的科学性.     

Evolutionary optimization of transition probability matrices for credit decision-making

Statistical transition probability matrices (TPMs), which indicate the likelihood of obligor credit state migration over a certain time horizon, have been used in various credit decision-making applications. A standard approach of calculating TPMs is to form a one-year empirical TPM and then project it into the future based on Markovian and time-homogeneity assumptions. However, the one-year empirical TPM calculated from historical data generally does not satisfy desired properties. We propose an alternative methodology by formulating the problem as a constrained optimization problem requiring satisfaction of all the desired properties and minimization of the discrepancy between predicted multi-year TPMs and empirical evidence. The problem is high-dimensional, non-convex, and non-separable, and is not effectively solved by nonlinear programming methods. To address the difficulty, we investigated evolutionary algorithms (EAs) and problem representation schemas. A self-adaptive differential evolution algorithm JADE, together with a new representation schema that automates constraint satisfaction, is shown to be the most effective technique.