A survey of metaheuristic-based techniques for University Timetabling problems

As well as nearly always belonging to the class of NP-complete problems, university timetabling problems can be further complicated by the often idiosyncratic requirements imposed by the particular institution being considered. It is perhaps due to this characteristic that in the past decade-or-so, metaheuristics have become increasingly popular in the field of automated timetabling. In this paper we carry out an overview of such applications, paying particular attention to the various methods that have been proposed for dealing and differentiating between constraints of varying importance. Our review allows us to classify these algorithms into three general classes, and we make some instructive comments on each of these. The author would like to give thanks to Ben Paechter at Napier University, Edinburgh, and Barry McCollum of Queens University, Belfast for providing the initial motivation for the production of this work. The author is also grateful to Peter Morgan, Bruce Curry, and Jonathan Thompson at Cardiff University and also an anonymous referee for their helpful comments and insights.     

G1 Hierarchical Bézier Surface over Arbitrary Meshes

This paper presents a method for constructing composite surfaces based on a collection of quadrilateral patches. A global parameterization using bilinear quadrilateral meshes and FEM like minimization procedure are introduced. Smoothing conditions such as C¹ and G¹ are handled by constraint equation and a related duality argument is implemented. The surfaces that can be constructed in this way include conforming and non-conforming connections (3 n 6, T-nodes) between elementary patches. Non-manifold surfaces are automatically treated also by this data structure. The underlying quadratic programming with linear constraints is solved by duality methods. Hierarchical data structure with bordering matrices methods are implemented to deal with local refinement (subdivision). The present work details the actual implementation for the case of Bézier patches.     

Anticipation based on constraint processing in a multi-agent context

Anticipation is a general concept used and applied in various domains. Many studies in the field of artificial intelligence have investigated the capacity for anticipation. In this article, we focus on the use of anticipation in multi-agent coordination, particularly preventive anticipation which consists of anticipating undesirable future situations in order to avoid them. We propose to use constraint processing to formalize preventive anticipation in the context of multi-agent coordination. The resulting algorithm allows any action that may induce an undesirable future state to be detected upstream of any multi-agent coordination process. Our proposed method is instantiated in a road traffic simulation tool. For the specific question of simulating traffic at road junctions, our results show that taking anticipation into account allows globally realistic behaviors to be reproduced without provoking gridlock between the simulated vehicles.     

Comparison of the numerical efficiency of different sequential linear programming based algorithms for structural optimisation problems

Amongst the different optimisation methods, the Sequential Linear Programming (S.L.P.) is very popular because of its conceptual simplicity and of the large availability of LP commercial packages (i.e. Simplex algorithm). Unfortunately, the numerical efficiency of the S.L.P. method depends meaningfully on a proper choice of the move limits that are adopted for the optimisation variables.

In this paper the effect on the numerical solution of different move limits definition criteria has been investigated. Two different approaches (CGML and LEAML) for the definition of the move limits in Sequential Linear Programming are described and compared in terms of numerical efficiency in the solution of six problems of weight minimisation of bar trusses structures.     

Simplifying data fitting using branching ratios as constraints in alpha spectrometry

Interference between different emissions and the asymmetric peak-shape in alpha spectrometry with semiconductor detectors force one to use spectral deconvolution in order to evaluate contributions from individual peaks. Previously, only sets of unrelated peaks have generally been considered in fitting procedures, with the restriction of the same peak-shape being applied over the whole spectrum. However, in complex alpha spectra, several emissions commonly originate from the same emitting nuclide. The use of branching ratios as constraints simplifies the fitting process and more accurate results are achieved. Thus, we here describe fitting blocks of peaks as an alternative method for the deconvolution of alpha spectra.     

Correspondence matching using kernel principal components analysis and label consistency constraints

This paper investigates spectral approaches to the problem of point pattern matching. We make two contributions. First, we consider rigid point-set alignment. Here we show how kernel principal components analysis (kernel PCA) can be effectively used for solving the rigid point correspondence matching problem when the point-sets are subject to outliers and random position jitter. Specifically, we show how the point- proximity matrix can be kernelised, and spectral correspondence matching transformed into one of kernel PCA. Second, we turn our attention to the matching of articulated point-sets. Here we show label consistency constraints can be incorporated into definition of the point proximity matrix. The new methods are compared to those of Shapiro and Brady and Scott and Longuet-Higgins, together with multidimensional scaling. We provide experiments on both synthetic data and real world data.     

A Constraint-Based Approach for Visualization and Animation

TRIP systems are tools for visualization and animation. They are based on a constraint-based model of bi-directional translation between abstract data and pictorial data. Using these systems, programmers can visualize abstract data, and animate various algorithms and processes, simply by providing a declarative mapping rule. This paper presents this model for visualization and animation, focusing on the use of constraints, and also presents the TRIP systems with examples of visualization and animation.     

Supply chain design: A holistic approach

Local considerations are one of the major pitfalls in designing, operating and managing systems. As such are the “Supply Chain design decisions”, hence we have tried to incorporate systems thinking approach, and in particular the “Theory of Constraints”, into the problem of “Locating a new Distribution Center” among a producer and a set of existing retailers. While customers face known random demand, we look for the least cost location candidate in a plane with direct shipping. Usefulness of an equity function to balance the inventory levels at retail shops is also discussed. The model is finally reduced to a single nonlinear objective and for a hypothetical numerical example it is solved by Particle Swarm Optimization method.     

Towards the optimal control of Markov chains with constraints

An optimal control problem with constraints is considered on a finite interval for a non-stationary Markov chain with a finite state space. The constraints are given as a set of inequalities. The optimal solution existence is proved under a natural assumption that the set of admissible controls is non-empty. The stochastic control problem is reduced to a deterministic one and it is shown that the optimal solution satisfies the maximum principle, moreover it can be chosen within a class of Markov controls. On the basis of this result an approach to the numerical solution is proposed and its implementation is illustrated by examples.