BURA Collection:http://bura.brunel.ac.uk/handle/2438/2352020-10-21T09:53:12Z2020-10-21T09:53:12ZSpatially Continuous and Discontinuous Galerkin Finite Element Approximations for Dynamic Viscoelastic ProblemsJang, Yongseokhttp://bura.brunel.ac.uk/handle/2438/210842020-06-25T02:01:56Z2020-01-01T00:00:00ZTitle: Spatially Continuous and Discontinuous Galerkin Finite Element Approximations for Dynamic Viscoelastic Problems
Authors: Jang, Yongseok
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London2020-01-01T00:00:00ZMetaheuristic approach for solving scheduling and financial derivative problemsLawrance Amaldass, Nareyus Ihttp://bura.brunel.ac.uk/handle/2438/192132019-10-02T02:01:37Z2019-01-01T00:00:00ZTitle: Metaheuristic approach for solving scheduling and financial derivative problems
Authors: Lawrance Amaldass, Nareyus I
Abstract: The objective of this thesis is to implement metaheuristic approaches to solve di erent
types of combinatorial problems. The thesis is focused on neighborhood heuristic optimisation
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 Di erence (CFD), with its corresponding underlying asset that
has been reported to the corresponding component authorities. The underlying asset
and CFD transaction contain di erent variables, like volume and price. Therefore, we are
looking for a combination of underlying asset variables that may hedge the equivalent CFD
variables. Our aim is to identify unhedged or unmatched CFD's with their corresponding
underlying asset. This problem closely relates to the goal programming problem with
variable parameters. We have developed two new local search methods and embedded the
newly constructed local search methods with basic VNS, to attain a new modi ed variant
of the VNS algorithm. We then used these newly constructed VNS variants to solve this
nancial matching problem.
In tackling the Vector Job Scheduling problem, we developed a new hybrid optimisation
heuristic algorithm by combining VNS and ACO. We then compared the results of this hybrid algorithm with VNS and ACO on their own. We found that the hybrid algorithm
performance is better than the other two independent heuristic algorithms. In tackling
the nancial derivative problem, our objective is to match the CFD trades with their
corresponding underlying equity trades. Our goal is to identify the mismatched CFD
trades while optimising 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.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London2019-01-01T00:00:00ZA hybrid approach for solving mixed binary integer programming problemsMat Shariff, Mastura Bintihttp://bura.brunel.ac.uk/handle/2438/189112019-08-02T02:01:26Z2018-01-01T00:00:00ZTitle: A hybrid approach for solving mixed binary integer programming problems
Authors: Mat Shariff, Mastura Binti
Abstract: Optimisation appears in many aspects of day to day life and more often, involves integer
optimisation of very large scales. Although technology advancements have enabled many
combinatorial optimisation problems to be solved exactly, this is only true for small and some of
the medium instances. For large instances, they require high computational times and worse, fail
to be solved due to the massive usage of the machine’s memory.
In this research, we aim to develop a hybrid technique, focusing on solving the MBIP problem
rather than finding the best solution for individual problem’s application. Therefore, we proposed
a general framework of a hybrid technique that may need minor adjustment when applied to
various optimisation problems, in particular to the mixed binary integer programming (MBIP)
problems.
The hybrid approach proposed in this research is the collaborative combination of the linear
programming (LP) relaxation with variable neighbourhood search (VNS). We use LP relaxation
solutions to generate initial solutions and use VNS to improve the solutions obtained. To illustrate
the flexibility of the proposed method, we implement the proposed method on two similar MBIP
problems; the constrained index tracking problem (CITP) and the gas supply chain problem.
The proposed hybrid technique generates satisfactory solutions within significantly shorter
amount of computational time. For the CITP problem, we compare the obtained solutions with
the solutions provided by the CPLEX solver (with time and solution limit imposed) and a genetic
algorithm (GA) approach. For most of the instances, our proposed hybrid technique gives better
solutions with significant reduction of the computational time compared to the time taken by the
CPLEX solver and the GA approach.
For the gas supply chain problem, the proposed hybrid technique manage to replicate the solutions
generated by the CPLEX solver (with time and solution limit imposed) within a shorter
computational time. When we decrease the number of locations that were allowed to supply gas to a specific location, the proposed hybrid technique generated better solutions with lower total
costs than the solutions given by the CPLEX solver.
The proposed hybrid technique was successfully implemented for both problems by adjusting the
optimal LP solutions of the decision variables that are used to guide the search process.
Satisfactory solutions were obtained for both problems within a relatively shorter computational
time.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London2018-01-01T00:00:00ZParisian Option Pricing: A Recursive Solution for the Density of the Parisian Stopping TimeDassios, ALim, JWhttp://bura.brunel.ac.uk/handle/2438/187602019-07-19T02:01:28Z2013-08-15T00:00:00ZTitle: Parisian Option Pricing: A Recursive Solution for the Density of the Parisian Stopping Time
Authors: Dassios, A; Lim, JW
Abstract: In this paper, we obtain the density function of the single barrier one-sided Parisian stopping time. The problem reduces to that of solving a Volterra integral equation of the ﬁrst kind, where a recursive solution is consequently obtained. The advantage of this new method as compared to that in previous literature is that the recursions are easy to program as the resulting formula involves only a ﬁnite sum and does not require a numerical inversion of the Laplace transform. For long window periods, an explicit formula for the density of the stopping time can be obtained. For shorter window lengths, we derive a recursive equation from which numerical results are computed. From these results, we compute the prices of one-sided Parisian options.2013-08-15T00:00:00Z