Resource allocation

Resource allocation for Spatial Network Big Database is challenging due to the large size of spatial networks, variety of types of spatial data, a fast update rate of spatial and temporal elements. It is challenging to learn, manage and process the collected data and produce meaningful information in a limited time. Produced information must be concise and easy to understand. At the same time, the information must be very descriptive and useful. My research aims to address these challenges through the development of fundamental data processing components for advanced spatial network queries that clearly and briefly deliver critical information. This thesis proposal studied two challenging Spatial Network Big Database problems: (1) Multiple Resource Network Voronoi Diagram and (2) Node-attributed Spatial Graph Partitioning.
To address the challenge of query processing for multiple resource allocation in preparing for or after a disaster, we investigated the problem of the Multiple Resource Network Voronoi Diagram (MRNVD). Given a spatial network and a set of service centers from k different resource types, a Multiple Resource Network Voronoi Diagram (MRNVD) partitions the spatial network into a set of Service Areas that can minimize the total cycle-distances of graph-nodes to allotted k service centers with different resource types. The MRNVD problem is important for critical societal applications such as assigning essential survival supplies (e.g., food, water, gas, and medical assistance) to residents impacted by man-made or natural disasters. The MRNVD problem is NP-hard; it is computationally challenging due to the large size of the transportation network. Previous work proposed the Distance bounded Pruning (DP) approach to produce an optimal solution for MRNVD. However, we found that DP can be generalized to reduce the computational cost for the minimum cycle-distance. We extend our prior work and propose a novel approach that reduces the computational cost. Experiments using real-world datasets from five different regions demonstrate that the proposed approach creates MRNVD and significantly reduces the computational cost.

This thesis presents a model designed to optimize the allocation of corporate resources required for the success of a product in the marketplace. The product development resources used in the model are: market research, applied research, product design, cost reduction and advertising. The key goals of this thesis are to provide industry with a usable tool: (1) Implement strategic plans through effective budgeting; (2) Optimize both short and long term profits; (3) Evaluate the impact of resource inter-dependencies; (4) Enable accountability that leads to goal achievement and checks unnecessary growth; (5) Remove much of the negative political and emotional variability; (6) Easily adapt to internal and external changes; (7) Output a specific allocation for each resource as a percentage of sales; (8) Output an estimate of future profitability. Genetic Algorithms are particularly well suited for this application because an exact optima is not required and the search space can be extremely large, complex, and non-linear.