Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem

Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. A critical aspect of the benchmarks addressed in this paper is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of good solutions and discuss the effects this has on the algorithm design. This paper presents a carefully-designed hybrid of Monte-Carlo tree search, novel neighbourhood moves, memetic algorithms, and hyper-heuristic methods. The implementation is also engineered to increase the speed with which iterations are performed, and to exploit the computing power of multicore machines. Empirical evaluation shows that the resulting information-sharing multi-component algorithm significantly outperforms other solvers on a set of “hidden” instances, i.e. instances not available at the algorithm design phase

Lokale ouderenadviesraden anno 2010: de visie van 173 voorzitters

Dit artikel schetst de resultaten van een onderzoek naar de werking van de lokale ouderenadviesraden in Vlaanderen. D.m.v. een quota steekproef werden 173 voorzitters bevraagd.status: publishe

Algorithm performance prediction for practical combinatorial optimisation problems

This dissertation investigates algorithm performance predictions in the context of combinatorial optimisation problems. The project is inspired by existing studies on running time predictions for complete search methods solving classical decision and optimisation problems. It considers more general performance criteria in the context of incomplete methods. One of the core concepts in this thesis is the notion of empirical hardness, denoting the apparent complexity of an instance as it is observed by a particular solver. We start by proposing a general strategy allowing for the construction of prediction models for the empirical hardness of practical problem instances. This strategy is formulated at a high level, allowing it to be applied in a broad variety of settings. We apply the strategy in two case studies, each focusing on a prototypical hard combinatorial optimisation problem with practical relevance. We consider the nurse rostering problem and a generalisation of the project scheduling problem. We investigate state-of-the-art metaheuristic algorithms for both problems and demonstrate the power of the methodology in this more complicated practical context. In particular, we present practical instantiations for all ingredients of the strategy. By extensive experimentation, we succeed in realising accurate performance prediction models. We furthermore build successful applications in the form of automatic algorithm selection tools, outperforming all of their state-of-the-art components individually. Based on the experience gained through the doctoral project, we present a discussion on the application of the proposed strategy in practical settings. We focus on the important ingredients and discuss how they can be instantiated.nrpages: 230status: publishe

Towards prediction of algorithm performance in real world optimisation problems

We investigate the applicability of an existing framework for algorithm runtime prediction to the field of metaheuristics, in particular applied to the real world problem of nurse rostering. Apart from predicting the runtime, we look at other performance criteria as well. These so called empirical hardness models are based on readily computable features of the problem instances. These problem features are basic properties or characteristics that are thought to be influencing the complexity of the problem instances. We follow two approaches, one in a domain specific setting, and later in a more general setting where problems are represented using a Propositional Satisfiability (SAT) formulation. Both approaches lead to accurate prediction models in a small proof-of-concept problem distribution. The framework can be used to help understanding the complexity, build algorithm portfolios and allow for quick quality approximation when the resources for computing a solution are not available.status: publishe

Distributed optimisation techniques - DICOMAS presentation

status: publishe

Distributed optimisation techniques - IT workshop presentation

status: publishe

Automatic algorithm selection for multi-mode resource-constrained project scheduling problems

In this talk, we will present the results of an experimental study towards building an automatic algorithm selection tool for the combinatorial optimisation problem of project scheduling. At the basis of this tool lies the concept of empirical hardness models. These models are mappings from problem instance features onto performance criteria of certain algorithms. Using such models, the performance of a set of algorithms can be predicted. Based on these predictions, the tool can automatically select the algorithm with best predicted performance. For such a tool to work properly, the predictions must be sufficiently accurate. Many state-of-the-art algorithms perform very well on a majority of bench- mark instances, while performing worse on a smaller set of instances. The perfor- mance of one algorithm can be very different on a set of instances while another algorithm sees no difference in performance at all. Knowing in advance, with- out using scarce computational resources, which algorithm to run on a certain problem instance, can significantly improve the total overall performance. We have applied this strategy to the classic problem of project scheduling with multiple execution modes. We selected two state-of-the-art algorithms that both perform relatively good on average. Combining these two algorithms in a portfolio with an automatic algorithm selection tool, we get a super-algorithm that outperforms any of it’s components individually.status: publishe

Towards an algorithm portfolio for nurse rostering

Based on the success of algorithm portfolio's in other domains, we have applied these ideas in a real-world setting. Last year, our research group co-organised the First International Nurse Rostering Competition 2010. This is an ideal testbed for the creation and validation of an algorithm portfolio for this real-world prob- lem. The goal of this research is building a super-algorithm composed of a set of competitive algorithms. The performance of each algorithm is learned based on characteristics of the instances. When presented with a new, unseen instance, the performance of each algorithm is predicted and the `best' algorithm is selected. Doing so, the portfolio is expected to outperform any of its individual components.status: publishe

Hardness analysis - DICOMAS presentation

status: publishe