An efficient memetic, permutation-based evolutionary algorithm for real-world train timetabling

Train timetabling is a difficult and very tightly constrained combinatorial problem that deals with the construction of train schedules. We focus on the particular problem of local reconstruction of the schedule following a small perturbation, seeking minimisation of the total accumulated delay by adapting times of departure and arrival for each train and allocation of resources (tracks, routing nodes, etc.). We describe a permutation-based evolutionary algorithm that relies on a semi-greedy heuristic to gradually reconstruct the schedule by inserting trains one after the other following the permutation. This algorithm can be hybridised with ILOG commercial MIP programming tool CPLEX in a coarse-grained manner: the evolutionary part is used to quickly obtain a good but suboptimal solution and this intermediate solution is refined using CPLEX. Experimental results are presented on a large real-world case involving more than one million variables and 2 million constraints. Results are surprisingly good as the evolutionary algorithm, alone or hybridised, produces excellent solutions much faster than CPLEX alone

On the Benefits of Inoculation, an Example in Train Scheduling

The local reconstruction of a railway schedule following a small perturbation of the traffic, seeking minimization of the total accumulated delay, is a very difficult and tightly constrained combinatorial problem. Notoriously enough, the railway company's public image degrades proportionally to the amount of daily delays, and the same goes for its profit! This paper describes an inoculation procedure which greatly enhances an evolutionary algorithm for train re-scheduling. The procedure consists in building the initial population around a pre-computed solution based on problem-related information available beforehand. The optimization is performed by adapting times of departure and arrival, as well as allocation of tracks, for each train at each station. This is achieved by a permutation-based evolutionary algorithm that relies on a semi-greedy heuristic scheduler to gradually reconstruct the schedule by inserting trains one after another. Experimental results are presented on various instances of a large real-world case involving around 500 trains and more than 1 million constraints. In terms of competition with commercial math ematical programming tool ILOG CPLEX, it appears that within a large class of instances, excluding trivial instances as well as too difficult ones, and with very few exceptions, a clever initialization turns an encouraging failure into a clear-cut success auguring of substantial financial savings

Artificial Agents and Speculative Bubbles

Pertaining to Agent-based Computational Economics (ACE), this work presents two models for the rise and downfall of speculative bubbles through an exchange price fixing based on double auction mechanisms. The first model is based on a finite time horizon context, where the expected dividends decrease along time. The second model follows the {\em greater fool} hypothesis; the agent behaviour depends on the comparison of the estimated risk with the greater fool's. Simulations shed some light on the influent parameters and the necessary conditions for the apparition of speculative bubbles in an asset market within the considered framework

SECTOR: Secure Common Information Space for the Interoperability of First Responders

AbstractThe ever-growing human, economic and environmental losses due to natural and/or man-made disasters demand a systematic, holistic, inter-governmental and multi-disciplinary approach to the management of large-scale crisis. However, crisis management is usually coordinated by local authorities, supported by a variety of different national and international crisis management organizations, all acting relatively autonomously. Coordination actions usually adopt non-interoperable information management tools, due to the heterogeneity of the involved organizations, limiting or even hindering the coordination efforts. This paper introduces the efforts conducted in the context of the EU-funded project called SECTOR, which aims at establishing the foundations of future Collaborative Crisis Management (CCM) Information Spaces by expanding the European scientific knowledge base on (cross-border) multi-agency processes and their complications when setting-up and designing the enabling information systems

Visible imaging system optical design by continuous optimization of glasses

Choice of lenses materials in optical design is crucial to reduce aberrations down to an acceptable level. Commercial glasses do not cover a continuous range of refractive indices and must be selected in a discrete library making them discrete variables in any optimization design process to achieve the final optical design to be manufactured. This paper proposes an alternative method to avoid the complicated discrete variables optimization process thanks to a two-steps continuous optimization methodology starting with fictitious glasses models before jumping to the real glasses optimization design. The illustration of this process and achieved results are presented on an example of optical system which validates our proposed method

Ant Colony Optimisation for E-Learning: Observing the Emergence of Pedagogic Suggestions

An attempt is made to apply Ant Colony Optimization (ACO) heuristics to an E-learning problem: the pedagogic material of an online teaching website for high school students is modelled as a navigation graph where nodes are exercises or lessons and arcs are hypertext links. The arcs' valuation, representing the pedagogic structure and conditioning the website's presentation, is gradually modified through the release and evaporation of virtual pheromones that reflect the successes and failures of students roaming around the graph. A compromis

Coupling the design of benchmark with algorithm in landscape-aware solver design

International audienceFollowing a general trend in artificial intelligence, Evolutionary Computation has, in recent years, witnessed substantial performance gains from landscape-aware selection and parameter tuning of algorithmic modules. Such approaches, however, are critically relying on suitable benchmarks, or training sets, that provide the appropriate blend of performance and generality. With this position paper we argue that, on a landscape analysis basis, the benchmark design problem will form a substantial part of the next-generation of automated, on-demand algorithm design principles