

Doctoral presentations
Registration for poster presentation for the Doctoral presentation on Monday 12th June should be made through https://goo.gl/forms/Vx57jjmsCK4PC6Rq1 before 6th June 2017. All presentations related to the summer school are accepted, if you have any doubt do not hesitate to contact us by email: The presentation will be a short slide + a poster during the session. More information on the slide will be given to the selected presentations. Accepted poster presentationsBrethomé Lucile Modelling and optimization of a passenger railway transportation plan with regards to mobility "For historical reasons, the design of the SNCF railway services are, as elsewhere, mainly driven by the incremental optimization of the use of production resources (and primarily the available trainpaths and material resources): timetable, infrastructure maintenance and rolling stock. Moreover, each year, marginal modifications are executed on the previous timetable, but a totally new timetable is rarely designed. Laura CalzadaInfante Dominance Network Analysis of Multiobjective Evolutionary Algorithms "My proposal consists in applying a methodology developed in my ongoing Ph.D, called Dominance Network Analysis (DNA) in the realm of Multiobjective Optimization. Originally, DNA was developed to assess the efficiency of inputoutput systems commonly studied using Data Envelopment Analysis (DEA). DNA consists in building a weighted direct acyclic graph that captures the dominance relationships between a sample of multidimensional points. This allows the visualisation of multidimensional data and the application of Complex Network Analysis (CNA) tools. Two specific applications of DNA to MOEAs are the following:
Cindy CalderónArce Adapted Dictionary Learning for online estimation of surrogate models "Expensive multiobjective optimization usually uses surrogate models to reduce the amount of objective function evaluations. This is particularly necessary when the objective function has a high evaluation cost. Several methods have been proposed for such estimation based on artificial neural networks or gaussian processes. In this work we propose a novel approach which explores key modifications to the dictionary learning methods in order to estimate surrogate models. These models are based on sparse linear combinations of \emph{words}, composed of smooth parametric functions. Hence, the surrogate models guide which locations of the objective function should be evaluated next and avoid unnecessary evaluations."
Simon Anderer Evolutionary Algorithms for the Optimization of Mobility Systems in RealTime (Working Title) To tackle realworld optimization problems in transport logistics, I examine the introduction of a socalled Decision Maker as an interface between realtime dynamic optimization problems and evolutionary optimization algorithms. One the one hand, this decision maker is responsible for eventhandling and information updates. On the other hand, it decides upon the realization of new (better) solutions suggested by the optimization algorithms. At the current stage of my PhD, a first version of this concept is implemented by considering a dynamic VRP in realtime, which is generated from the Solomon/Gehring & Homberger benchmark files.
Hani Zbib A multimove decent algorithm for the NoSplit MultiCompartment Capacitated Arc Routing Problem The NoSplit MultiCompartment Capacitated Arc Routing Problem (MCCARP) is an extension of the CARP arising in different applications such as waste collection where different waste types exist at each edge and the fleet of vehicle is multicompartmented. The aim of the MCCARP is to find a set of least cost routes that service the demand for each waste type at all required edges without exceeding compartment capacities, with the condition that when a vehicle visits an edge it either picks the totality or none of its demands. We propose a heuristic approach to solve the MCCARP, consisting of a multimove descent algorithm that combines traditional CARP local search moves with optimal edge orientation choice and ejection chains. The algorithm iteratively chooses at random a set of moves and computes all the savings obtained from the search of the whole move neighborhood. The savings are then plotted on an alternate moves graph where each MCCARP edge is transformed into a set of nodes and an edge is added between every two nodes presenting a positive saving. A shortest path algorithm is subsequently run on that graph to determine the set of multimoves that bring the largest saving to the current solution. The algorithm has been tested on a large set of instances obtained from reallife waste collection companies across Denmark, varying in sizes between 25 to 11656 nodes and 18 to 8584 required edges, and in the number of waste types on each edge (2 to 4).
Glize Estèle Branchandprice algorithm for MultiObjective Vehicle Routing Problems "We propose an extension of the single objective exact algorithm of Baldacci et al. (2008) to generate the complete Pareto front of a biobjective VRP. The resulting algorithm combines the column generation and the branchandbound methods. The algorithm requires the set of nondominated points of the LPrelaxation to define a lower bound of the problem. Each point is combined with a direction in which the solution optimizes the problem. The algorithm also needs an upper bound representing feasible solutions of the initial problem. For each direction, the associated gap between the corresponding lower bound point and the closest upper bound point is computed. Then, all routes having their parametrized cost within this gap are generated and solving the resulting VRP provides a Pareto optimal solution. Afterwards, every pair of consecutive Pareto optimal solutions previously found defines a reduced area where other optimal solutions could be. Those are generated successively by calculating their most efficient direction, lower bound and gap. The algorithm is applied to a biobjective VRP with timewindows to minimize two different costs on each route. "
Aymeric Blot Reacting and Adapting to the Environment  Designing autonomous methods for multiobjective combinatorial optimisation
Multiobjective combinatorial problems are usually tackled by metaheuristics. These metaheuristics expose from few to large numbers of parameters in order to be efficient on many types of problems and instances. However, their performance on a given problem instance are closely related to their parameters values that are generally not optimal, either kept to their default value or laboriously set by hand. Optimised values of these parameters can be inferred from a set of training instances (Algorithm Configuration / Parameter Tuning) and kept fixed during the algorithm execution. However, in order to better adapt to the instance to solve and its particular structure, these values can be modified during the algorithm execution (Parameter Control). In this thesis, we study both aspects of automatic algorithm design on MultiObjective Local Search (MOLS) algorithms, efficient multiobjective metaheuristics for which many parameterisable strategies and components have been proposed.

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