6 research outputs found
A dynamic programming model to solve optimisation problems using GPUs
This thesis presents a parallel, dynamic programming based model which is deployed on the GPU of a system to accelerate the solving of optimisation problems. This is achieved by simultaneously running GPU based computations, and memory transactions, allowing computation to never pause, and overcoming the memory constraints of solving large problem instances. Due to this some optimisation problems, which are currently not solved in an exact manner for real world sized instances due to their complexity, are moved into the solvable realm. The model is implemented to solve, a range of different test problems, where artificially constructed test data is used to ensure good performance even in the worst cases. Through this extensive testing, we can be confident the model will perform well when used to solve real world test cases. Testing of the model was carried out using a range of different implementation parameters in relation to deployment on the GPU, in order to identify both optimal implementation parameters, and how the model will operate when running on different systems. All problems, when implemented in parallel using the model, show run-time improvements compared to the sequential implementations, in some instances up to hundreds of times faster, but more importantly also show high efficiency metrics for the utilisation of GPU resources. Throughout testing emphasis has been placed on GPU based metrics to ensure the wider generic applicability of the model. Finally, the parallel model allows for new problems to be defined through the use of a simple file format, enabling wider usage of the model
Integrating artificial neural networks, simulation and optimisation techniques in improving public emergency ambulance preparedness for heterogeneous regions under stochastic environments.
Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.The Bulawayo Emergency Medical Services (BEMS) department continues to rely on judgemental
methods with limited use of historical data for future predictions, strategic, tactical
and operational level decision making. The rural to urban migration trend has seen the
sprouting of new residential areas, and this has put pressure to the limited health, housing
and education resources. It is expected that as population increases, there is subsequent
increase in demand for public emergency services. However, public emergency ambulance
demand trends has been decreasing in Bulawayo over the years. This trend is a sign of limited
capacity of the service rather than demand itself. The situation demanded for consolidated
efforts across all sectors including research, to restore confidence among residents, reduce
health risk and loss of lives.
The key objective was to develop a framework that would assist in integrating forecasting,
simulation and optimisation techniques for ambulance deployment to predefined
locations with heterogeneous demand patterns under stochastic environments, using multiple
performance indicators. Secondary data from the Bulawayo Municipality archives from
2010 to 2018 was used for model building and validation. A combination of methods based
on mathematics, statistics, operations research and computer science were used for data
analysis, model building, sensitivity analysis and numerical experiments.
Results indicate that feed forward neural network (FFNN) models are superior to traditional
SARIMA models in predicting ambulance demand, over a short-term forecasting
horizon. The FFNN model is more inclined to value estimation as compared to SARIMA
model, which is directional as depicted by the linear pattern over time. An ANN model with
a 7-(4)-1 architecture was selected to forecast 2019 public emergency ambulance demand
(PEAD). Peak PEAD is expected in January, March, September and December whilst lower
demand is expected for April, June and July 2019.
Simulation models developed mimicked the prevailing levels of service for BEMS with
six(6) operational ambulances. However. the average response times were well above 15
minutes, with significantly high average queuing times and number of ambulances queuing
for service. These performance outcomes were highly undesirable as they pose a great threat
to human based outcomes of safety and satisfaction with regards to service delivery.
Optimisation for simulation was conducted by simultaneously minimising the average
response time and average queuing time, while maximising throughput ratios. Increasing
the number of ambulances influenced the average response time below a certain threshold,
beyond this threshold, the average response time remained constant rather than decreasing
gradually. Ambulance utilisation inversely varied to increase in the feet size. Numerical
experiments revealed that reducing the response time results in the reduction in number of
ambulances required for optimal ambulance deployment. It is imperative to simultaneously
consider multiple performance indicators in ambulance deployment as it balances resource
allocation and capacity utilisation, while avoiding idleness of essential equipment and human
resources. Management should lobby for de-congestion and resurfacing of old and dilapidated
roads to increase access and speed when responding to emergency calls.
Future research should investigate the influence of varying service time on optimum
deployment plans and consider operational costs, wages and other budgetary constraints
that influence the allocation of critical but scarce resources such as personnel, equipment
and emergency ambulance response vehicles
Scalable Query Processing on Spatial Networks
Spatial networks (e.g., road networks) are general graphs with spatial information (e.g., latitude/longitude) information associated with the vertices and/or the edges of the graph. Techniques are presented for query processing on spatial networks that are based on the observed coherence between the spatial positions of the vertices and the shortest paths between them. This facilitates aggregation of the vertices into coherent regions that share vertices on the shortest paths between them. Using this observation, a framework, termed SILC, is introduced that precomputes and compactly encodes the N^2 shortest path and network distances between every pair of vertices on a spatial network containing N vertices. The compactness of the shortest paths from source vertex V is
achieved by partitioning the destination vertices into subsets based on the identity of the first edge to them from V. The spatial coherence of these subsets is captured by using a quadtree representation whose dimension-reducing property enables the storage requirements of each subset to be reduced to be proportional to the perimeter of the spatially coherent regions, instead of to the number of vertices in the spatial network. In
particular, experiments on a number of large road networks as well as a theoretical analysis have shown that the total storage for the shortest
paths has been reduced from O(N^3) to O(N^1.5). In addition to SILC, another framework, termed PCP, is proposed that also takes advantage of the spatial coherence of the source vertices and makes use of the Well Separated Pair decomposition to further reduce the storage, under suitably defined
conditions, to O(N).
Using these frameworks, scalable algorithms are presented to implement a wide variety of operations such as nearest neighbor finding and distance joins on large datasets of locations residing on a spatial network. These frameworks essentially decouple the process of computing shortest paths from that of spatial query processing as well as also decouple the domain of the participating objects from the domain of the vertices of the spatial network. This means that as long as the spatial network is unchanged, the
algorithm and underlying representation of the shortest paths in the spatial network can be used with different sets of objects
LIPIcs, Volume 248, ISAAC 2022, Complete Volume
LIPIcs, Volume 248, ISAAC 2022, Complete Volum