A container ship traffic model for simulation studies

The aim of this paper is to develop a container ship traffic model for port simulation studies. Such a model is essential for terminal design analyses and testing the performance of optimization algorithms. This kind of studies requires accurate information about the ship stream to build test scenarios and benchmark instances. A statistical model of ship traffic is developed on the basis of container ship arrivals in eight world ports. The model provides three parameters of the arriving ships: ship size, arrival time and service time. The stream of ships is divided into classes according to vessel sizes. For each class, service time distributions and mixes of return time distributions are provided. A model of aperiodic arrivals is also proposed. Moreover, the results achieved are used to compare port specific features

Framework of algorithm portfolios for strip packing problem

International audienceIn this paper selection of fast algorithm portfolios for 2SP packing problem is considered. The 2SP problem consists in placing rectangles on a strip of the given width for minimum strip length. The 2SP packing has application in many industries, but suitability of the related algorithms is limited by their runtimes. While solving combinatorial optimization problems, longer runtimes increase chances of obtaining higher quality solutions. This means that runtime vs solution quality trade-off is important in solving problems such as strip packing. Given some limited runtime, a method is needed to provide the best solution possible. However, a single algorithm outperforming all other methods under all possible conditions usually does not exist. Therefore, algorithm portfolios can reliably provide high quality solutions in the limited runtime. We propose a method choosing algorithm portfolios on the basis of the algorithm performance on a set of training instances. A portfolio covers the instances with the best solutions which could be obtained in the given runtime, subject to the minimum computational cost of the selected algorithms. The portfolios are evaluated in extensive experiments carried out on designed and literature datasets. We demonstrate that our method is capable of carrying over solution quality from the training datasets to the testing datasets. In other words, our algorithm selection method can learn from the training instances. We also compare performance of our portfolio selection method with some other more straightforward approaches to the portfolio selection

Solution initiale d'un système de transport à la demande intégrant les transports en commun

International audienceLe Dial-A-Ride Problem (DARP) est un problème de transport de passagers de porte à porte à l'aide de véhicules à la demande. Integrated Dial-A-Ride Problem (IDARP) est une version du DARP où les transports en commun comme les trams ou les bus ont été integrés . Pour cela nous étudions les modèles de programmation par contraintes développés pour le DARP afin d'en adapter certains au IDARP