Ian D. Wilson

Container stowage planning: a methodology for generating computerised solutions

The container stowage problem concerns the suitable placement of containers in a container-ship on a multi-port journey; it requires consideration of the consequences each placement has on decisions at subsequent ports. A methodology for the automatic generation of computerised solutions to the container stowage problem is shown; objective functions that provide a basis for evaluating solutions are given in addition to the underlying structures and relationships that embody this problem. The methodology progressively refines the placement of containers within the cargo-space of a container ship until each container is specifically allocated to a stowage location. The methodology embodies a two stage process to computerised planning, that of a generalised placement strategy and a specialised placement procedure. Heuristic rules are built into objective functions for each stage that enable the combinatorial tree to be explored in an intelligent way, resulting in good, if not optimal, solutions for the problem in a reasonable processing time.

Principles of Combinatorial Optimization Applied to Container-Ship Stowage Planning

In this paper, a methodology for generating automated solutions to the container stowage problem is shown. The methodology was derived by applying principles of combinatorial optimization and, in particular, the Tabu Search metaheuristic. The methodology progressively refines the placement of containers, using the Tabu search concept of neighbourhoods, within the cargo-space of a container ship until each container is specifically allocated to a stowage location. Heuristic rules are built into objective functions for each stage that enable the combinatorial tree to be explored in an intelligent way, resulting in good, if not optimal, solutions for the problem in a reasonable processing time.

Predicting the geo-temporal variations of crime and disorder

Traditional police boundaries—precincts, patrol districts, etc.—often fail to reflect the true distribution of criminal activity and thus do little to assist in the optimal allocation of police resources. This paper introduces methods for crime incident forecasting by focusing upon geographical areas of concern that transcend traditional policing boundaries. The computerised procedure utilises a geographical crime incidence-scanning algorithm to identify clusters with relatively high levels of crime (hot spots). These clusters provide sufficient data for training artificial neural networks (ANNs) capable of modelling trends within them. The approach to ANN specification and estimation is enhanced by application of a novel and noteworthy approach, the Gamma test (GT).

Residential property price time series forecasting with neural networks

The residential property market accounts for a substantial proportion of UK economic activity. Professional valuers estimate property values based on current bid prices (open market values). However, there is no reliable forecasting service for residential values with current bid prices being taken as the best indicator of future price movement. This approach has failed to predict the periodic market crises or to produce estimates of long-term sustainable value (a recent European Directive could be leading mortgage lenders towards the use of sustainable valuations in preference to the open market value). In this paper, we present artificial neural networks, trained using national housing transaction time series data, which forecasts future trends within the housing market.

Container stowage pre-planning: using search to generate solutions, a case study

Container-ships are vessels possessing an internal structure that facilitates the handling of containerised cargo. At each port along the vessels journey, containers destined for those ports are unloaded and additional containers destined for subsequent ports are loaded. Determining a viable arrangement of containers that facilitates this process, in a cost-effective way, constitutes the deep-sea container-ship stowage problem. This paper outlines a computer system that generates good sub-optimal solutions to the stowage pre-planning problem. This is achieved through an intelligent analysis of the domain allowing the problem to be divided into sub-problems: a generalised placement strategy and a specialised placement procedure. This methodology progressively refines the arrangement of containers within the cargo-space of a container ship until each container is specifically allocated to a stowage location. Good, if not optimal, solutions for the problem are obtained in a reasonable processing time through the use of heuristics incorporated into objective functions for each stage.

A knowledge based genetic algorithm approach to automating cartographic generalisation

Rendering map data at scales smaller than their source can give rise to map displays exhibiting graphic conflict, such that objects are either too small to be seen or too close to each other to be distinguishable. Furthermore, scale reduction will often require important features to be exaggerated in size, sometimes leading to overlapping features. Cartographic Map generalisation is the process by which any graphic conflict that arises during scaling is resolved. In this paper, we show how a Genetic Algorithm approach was used to resolve spatial conflict between objects after scaling, achieving near optimal solutions within practical time constraints.

A genetic algorithm approach to cartographic map generalisation

Rendering map data at scales smaller than their source can give rise to map displays exhibiting graphic conflict, such that objects are either too small to be seen or too close to each other to be distinguishable. Furthermore, scale reduction will often require important features to be exaggerated in size, sometimes leading to overlapping features. Cartographic map generalisation is the process by which any graphic conflict that arises during scaling is resolved. In this paper, we show how a Genetic Algorithm (GA) approach was used to resolve spatial conflict between objects after scaling, achieving near optimal solutions within practical time constraints.

Predicting Housing Value: Genetic Algorithm Attribute Selection and Dependence Modelling Utilising the Gamma Test

In this paper we show, by means of an example of its application to the problem of house price forecasting, an approach to attribute selection and dependence modelling utilising the Gamma Test (GT), a non-linear analysis algorithm that is described. The GT is employed in a two-stage process: first the GT drives a Genetic Algorithm (GA) to select a useful subset of features from a large dataset that we develop from eight economic statistical series of historical measures that may impact upon house price movement. Next we generate a predictive model utilising an Artificial Neural Network (ANN) trained to the Mean Squared Error (MSE) estimated by the GT, which accurately forecasts changes in the House Price Index (HPI). We present a background to the problem domain and demonstrate, based on results of this methodology, that the GT was of great utility in facilitating a GA based approach to extracting a sound predictive model from a large number of inputs in a data-point sparse real-world application.

A tabu search approach to automated map generalisation

Displaying map data at scales smaller than its source can result in objects that are either too small to be seen or too close to each other to be distinguishable. Furthermore, graphic conflicts become more likely when certain map symbols are no longer a true scale representation of the feature they represent. Map generalisation includes the processes by which such conflicts are resolved. The map generalisation technique presented here is exponential in the problem size and is, as such, combinatorially large (NP-hard). We show how the tabu search metaheuristic was used to resolve spatial conflict between objects after scaling, achieving near optimal solutions within practical time constraints.

Data Clustering and Rule Abduction to Facilitate Crime Hot Spot Prediction

Crime rates differ between types of urban district, and these disparities are best explained by the variation in use of urban sites by differing populations. A database of violent incidents is rich in spatial information and studies have, to date, provided a statistical analysis of the variables within this data. However, a much richer survey can be undertaken by linking this database with other spatial databases, such as the Census of Population, weather and police databases. Coupling Geographical Information Systems (GIS) with Artificial Neural Networks (ANN) offers a means of uncovering hidden relationships and trends within these disparate databases. Therefore, this paper outlines the first stage in the development of such a system, designed to facilitate the prediction of crime hot spots. For this stage, a series of Kohonen Self-Organising Maps (KSOM) will be used to cluster the data in a way that should allow common features to be extracted.