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This is a summary of the most important results presented in the authors PhD thesis. This thesis, written in English, was defended on 13 June 2003 and supervised by Johan Springael and Gerrit K. Janssens. A copy is available from the author upon request. This PhD thesis focuses on stochastic problems and develops a framework to find robust and flexible solutions of such problems. The framework is...
. In the Frequency Assignment Problem with Polarization (FAPP), a given set of links must each be assigned a frequency and a polarization, while respecting given radio-electric compatibility constraints defined on pairs of links. In this paper, we propose a tabu search algorithm for the FAPP. A specialized neighborhood is proposed for the problem. Other key features of the algorithm are an adaptive...
. We present the main results in the author’s Ph.D. thesis (Iori 2004), defended at the University of Bologna in April 2004 and supervised by S. Martello. The thesis is written in English and is available from the author upon request. It proposes exact and metaheuristic algorithms for solving some relevant combinatorial optimization problems, with particular emphasis on scheduling, two-dimensional...
The design of effective neighborhood structures is fundamental to the performance of local search and metaheuristic algorithms for combinatorial optimization. Significant efforts have been made in the creation of larger and more powerful neighborhoods that are able to explore the solution space more extensively and effectively while keeping computation complexity within acceptable levels. The most...
This is a summary of the author’s Ph.D. thesis supervised by Dirk Van Oudheusden and Willy Herroelen and defended in May 2008 at the Katholieke Universiteit Leuven. The thesis is written in English and is available from the author upon request. In this work different operations research techniques are applied to solve several scheduling problems in two different domains. The first domain deals with...
Empirical evidence demonstrates that when the same local search operator is used, variable neighborhood search consistently outperforms random multistart local search on all types of combinatorial and global optimization problems tested. In this paper we suggest that this superiority in performance may be explained by the distribution of the attraction basins around a current solution as a function...
The m-Peripatetic Vehicle Routing Problem (m-PVRP) consists in finding a set of routes of minimum total cost over m periods so that two customers are never sequenced consecutively during two different periods. It models for example money transports or cash machines supply, and the aim is to minimize the total cost of the routes chosen. The m-PVRP can be considered as a generalization of two well-known...
A travelling deliveryman needs to find a tour such that the total waiting time of all the customers he has to visit is minimum. The deliveryman starts his tour at a depot, travelling at constant velocity. In this paper we suggest a general variable neighborhood search based heuristic to solve this NP-hard combinatorial optimization problem. We combine several classical neighborhood structures and...
The travelling salesperson problem with hotel selection (TSPHS) is a recently proposed variant of the travelling salesperson problem. Currently, the approach that finds the best solutions is a memetic algorithm. However, this approach is unsuitable for applications that require very short computation times. In this paper, a new set-partitioning formulation is presented along with a simple but powerful...
The paper addresses the problem of strategic base stations placement in cognitive radio networks. We consider a primary user, operating on the frequency channels of a primary network, and an operator (a leader) facing the competition of a second operator (a follower). These operators are willing to exploit the unused capacity of the primary network and maximize their profits derived from operating...
In the context of big data, many scientific communities aim to provide efficient approaches to accommodate large-scale datasets. This is the case of the machine-learning community, and more generally, the artificial intelligence community. The aim of this article is to explain how data mining problems can be considered as combinatorial optimization problems, and how metaheuristics can be used to address...
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