The traditional methods such as critical path method (CPM) and linear programming (LP) have difficulty solving more general scheduling problems such as resource constrained scheduling problems. Emerging techniques such as particle swarm optimisation (PSO) have shown advantages in addressing this problem. However, the performance of simple PSO is greatly dependent on its parameters, and bad sele...

The problem of analysis operator learning can be formulated as a constrained optimisation problem. This problem has been approximately solved using projected gradient or geometric gradient descent methods. We will propose a relaxation for the constrained analysis operator learning in this paper. The relaxation has been suggested here to, a) reduce the computational complexity of the optimisatio...

Modern vehicles possess an increasing number of software and hardware components that are integrated in electronic control units (ECUs). Finding an optimal allocation for all components is a multi-objective optimisation problem, since every valid allocation can be rated according to multiple objectives like costs, busload, weight, etc. Additionally, several constraints mainly regarding the avai...

We introduce and describe the Multiple Gravity Assist problem, a global optimisation problem that is of great interest in the design of spacecraft and their trajectories.We discuss its formalization andwe show, in one particular problem instance, the performance of selected state of the art heuristic global optimisation algorithms. A deterministic search space pruning algorithm is then develope...

This paper surveys complexity and approximability results for the Maximum Solution (Max Sol) problem. Max Sol is an optimisation variant of the constraint satisfaction problem. Many important and well-known combinatorial optimisation problems are instances of Max Sol: for example, Max Sol restricted to the domain {0, 1} is exactly the Max Ones problem (which, in turn, captures problems such as ...

The process of design search and optimisation using Computational Fluid Dynamics (CFD) is computationally and data intensive, a problem well-suited to Grid computing. The Geodise toolkit is a suite of Grid-enabled design optimisation and search tools within the Matlab environment. The use of these tools by the engineer is facilitated by intelligent design advisers targeted initially at CFD. The...

In this paper, the transportation problem under the carbon emission trading program ismodelled by mathematical programming and genetic algorithm. Since green supply chain issuesbecome important and new legislations are taken into account, carbon emissions costs are included inthe total costs of the supply chain. The optimisation model has the ability to minimise the total costsand provides the ...

The Radiotherapy Optimisation Test Set (TROTS) is an extensive set of problems originating from radiotherapy (radiation therapy) treatment planning. This dataset is created for 2 purposes: (1) to supply a large-scale dense dataset to measure performance and quality of mathematical solvers, and (2) to supply a dataset to investigate the multi-criteria optimisation and decision-making nature of t...

The problem of analysing the worst-case performance of a nonlinear aeroelastic system is formulated as an optimisation problem and solved using gradient-based local optimisation methods. Two different approaches are considered. The first formulates the optimisation problem in the classical Euler-Lagrange setting and computes the gradient by backward integration of the resulting adjoint system. ...

dynamic combinatorial problems poses a particular challenge to optimisation algorithms. Optimising a dynamic problem that does not notify the solver when a change has been made is very difficult for most well-known algorithms. Extremal Optimisation is a recent addition to the group of biologically inspired optimisation algorithms, while Ant Colony System has been used to solve a large variety o...