GENET is a connectionist approach to constraint satisfaction.
It is a stochastic search method. It has been shown to be both
efficient and effective in binary constraint satisfaction problems,
graph colouring and car sequencing (which is a non-binary problem).
Guided Genetic Algorithm (GLS) is an extension of GLS. It is a
hybrid between GLS and GA.
The aim is to improve the robustness of GLS and to extend its
domain of application.
It has been shown efficient and effective in a number of problems,
including the Royal Road Function, the Processors configuration problem,
the frequency assignment problem and the general assignment problem.
A model of a constraint satisfaction problem is defined by the
variables, domains and contraints selected. Given a model specificaiton,
the choice of model is crucial to the solving of a constraint satisfaction problem.
This project looks at heuristics to evaluate models and modify it in order to help
solving constraint satisfaction problem.
This project is built upon the philosophy of finding
appropriate algorithms for a given constraint satisfaction (as opposed
to applying a "champion algorithm" to all problems).
Mechanisms are being developed to monitor the performance of algorithms
and adaptive strategies are being developed to dynamically switches
algorithms when the current algorithm is concluded failing.
This is a completed project which aims to bring constraint technology to
non-expert users. The software engineering and human-computer
interaction aspects of constraint satisfaction and constraint
optimization will be investigated. Practical aspects of the constraint
technology will be advanced. A computer-aided constraint-programming
system will be designed and implemented.
This research concerns itself with the scheduling of Autonomous Guided Vehicles
(AGVs) in a port. Port components that are relevant to our problem include
berths, quay cranes, container storage areas, and a road network. Given a number of AGVs and their
availability, the task is to schedule the AGVs to meet the transportation
requirements. We have extended classical Network Simplex Method for (a) efficiency, and
(b) dynamic problems. We have also developed a heuristic search algorithm for this problem.
Population-based algorithms using estimation of distribution,
often called estimation of distribution algorithms (EDAs),
have been recognized as a major paradigm in evolutionary computation.
This project will work towards establishing a sound theory for
characterizing and explaining EDA-like algorithms.
We shall use continuous optimization problems and quadratic
assignment problems as our test problems.
This project is in collaboration with BT.
Although it is motivated by BT's workforce scheduling problem, the ideas developed in this project are general.
It involved scheduling engineers to jobs, satisfying a wide range of constraints.
This is a multi-objective optimization problem.
Some of the objectives are to minimize travelling distance and to maximize service quality as defined by the company.
Staff empowerment is also a major theme in this project.