Research Area: Metaheuristic Computing
The objective of this thesis is to implement metaheuristic approaches to solve different types of combinatorial problems. The thesis is focused on neighborhood heuristic optimization techniques such as Variable Neighborhood Search (VNS) and Ant Colony Optimisation (ACO) algorithms. The thesis will focus on two diverse combinatorial problems. A job shop scheduling problem, and a nancial derivative matching problem. The rst is a NP-hard 2-stage assembly problem, where we will be focussing on the rst stage. It consists of sequencing a set of jobs having multiple components to be processed. Each job has to be worked on independently on a speci c machine. We consider these jobs to form a vector of tasks.
Our objective is to schedule jobs on the particular machines in order to minimise the completion time before the second stage starts. In this thesis, we have constructed a new hybrid metaheuristic approach to solve this unique job shop scheduling problem. The second problem has arisen in the nancial sector, where the nancial regulators collects transaction data across regulated assets classes. Our focus is to identify any unhedged derivative, Contract for Difference (CFD), with its corresponding underlying asset that has been reported to the corresponding component authorities.
Our goal is to identify the mismatched CFD trades while optimizing the search process. We have developed two new local search techniques and we have implemented a VNS algorithm with the newly developed local search techniques to attain better solutions.
Name of the Researcher: Nareyus I Lawrance Amaldass
Name of the Supervisor(s): Cormac A Lucas
Year of Completion: 2019
University: Brunel University London
Thesis Link: Home Page Url
