Eric Soubeiga

A Tabu-Search Hyperheuristic for Timetabling and Rostering

Hyperheuristics can be defined to be heuristics which choose between heuristics in order to solve a given optimisation problem. The main motivation behind the development of such approaches is the goal of developing automated scheduling methods which are not restricted to one problem. In this paper we report the investigation of a hyperheuristic approach and evaluate it on various instances of two distinct timetabling and rostering problems. In the framework of our hyperheuristic approach, heuristics compete using rules based on the principles of reinforcement learning. A tabu list of heuristics is also maintained which prevents certain heuristics from being chosen at certain times during the search. We demonstrate that this tabu-search hyperheuristic is an easily re-usable method which can produce solutions of at least acceptable quality across a variety of problems and instances. In effect the proposed method is capable of producing solutions that are competitive with those obtained using state-of-the-art problem-specific techniques for the problems studied here, but is fundamentally more general than those techniques.

A Hyperheuristic Approach to Scheduling a Sales Summit

The concept of a hyperheuristic is introduced as an approach that operates at a higher lever of abstraction than current metaheuristic approaches. The hyperheuristic manages the choice of which lower-level heuristic method should be applied at any given time, depending upon the characteristics of the region of the solution space currently under exploration. We analyse the behaviour of several different hyperheuristic approaches for a real-world personnel scheduling problem. Results obtained show the effectiveness of our approach for this problem and suggest wider applicability of hyperheuristic approaches to other problems of scheduling and combinatorial optimisation.

A simulated annealing based hyperheuristic for determining shipper sizes for storage and transportation

The current drive to reduce packaging waste has led many companies to consider the use of multi-trip containers or shippers in which to transport their products in order to reduce packaging waste. The efficiency of such systems obviously depends on selecting shipper dimensions in such a way as to ensure high volumetric utilisation. As is the case with many practical problems the efficiency/solution quality can be improved if problem specific information is used to enhance the operation of a meta-heuristic solution approach. The problem can be modelled as a p-median problem but is too large to be solved in reasonable time without further modification. Four such modifications, all based on properties of the physical problem, are introduced and incorporated into a hyperheuristic driven simulated annealing solution approach.

Hyperheuristics: A Tool for Rapid Prototyping in Scheduling and Optimisation

The term hyperheuristic was introduced by the authors as a high-level heuristic that adaptively controls several low-level knowledgepoor heuristics so that while using only cheap, easy-to-implement low-level heuristics, we may achieve solution quality approaching that of an expensive knowledge-rich approach. For certain classes of problems, this allows us to rapidly produce effective solutions, in a fraction of the time needed for other approaches, and using a level of expertise common among non-academic IT professionals. Hyperheuristics have been successfully applied by the authors to a real-world problem of personnel scheduling. In this paper, the authors report another successful application of hyperheuristics to a rather different real-world problem of personnel scheduling occuring at a UK academic institution. Not only did the hyperheuristics produce results of a quality much superior to that of a manual solution but also these results were produced within a period of only three weeks due to the savings resulting from using the existing hyperheuristic software framework.

Multi-Objective Hyper-Heuristic Approaches for Space Allocation and Timetabling

An important issue in multi-objective optimisation is how to ensure that the obtained non-dominated set covers the Pareto front as widely as possible. A number of techniques (e.g. weight vectors, niching, clustering, cellular structures, etc.) have been proposed in the literature for this purpose. In this paper we propose a new approach to address this issue in multi-objective combinatorial optimisation. We explore hyper-heuristics, a research area which has gained increasing interest in recent years. A hyper-heuristic can be thought of as a heuristic method which iteratively attempts to select a good heuristic amongst many. The aim of using a hyper-heuristic is to raise the level of generality so as to be able to apply the same solution method to several problems, perhaps at the expense of reduced but still acceptable solution quality when compared to a tailor-made approach. The key is not to solve the problem directly but rather to (iteratively) recommend a suitable heuristic chosen because of its performance. In this paper we investigate a tabu search hyper-heuristic technique. The idea of our multi-objective hyper-heuristic approach is to choose, at each iteration during the search, the heuristic that is suitable for the optimisation of a given individual objective. We test the resulting approach on two very different real-world combinatorial optimisation problems: space allocation and timetabling. The results obtained show that the multi-objective hyper-heuristic approach can be successfully developed for these two problems producing solutions of acceptable quality.

Hyperheuristics: A Robust Optimisation Method Applied to Nurse Scheduling

A hyperheuristic is a high-level heuristic which adaptively chooses between several low-level knowledge-poor heuristics so that while using only cheap, easy-to-implement low-level heuristics, we may achieve solution quality approaching that of an expensive knowledge-rich approach, in a reasonable amount of CPU time. For certain classes of problems, this generic method has been shown to yield high-quality practical solutions in a much shorter development time than that of other approaches such as tabu search and genetic algorithms, and using relatively little domain-knowledge. Hyperheuristics have previously been successfully applied by the authors to two real-world problems of personnel scheduling. In this paper, a hyperheuristic approach is used to solve 52 instances of an NP-hard nurse scheduling problem occuring at a major UK hospital. Compared with tabu-search and genetic algorithms, which have previously been used to solve the same problem, the hyper-heuristic proves to be as robust as the former and more reliable than the latter in terms of solution feasibility. The hyperheuristic also compares favourably with both methods in terms of ease-of-implementation of both the approach and the low-level heuristics used.