L'écart salarial entre les secteurs public et privé au Québec

Rapport de recherche présenté à la Faculté des arts et des sciences en vue de l'obtention du grade de Maîtrise en sciences économiques

Hybrid meta-heuristics for combinatorial optimization

Combinatorial optimization problems arise, in many forms, in vari- ous aspects of everyday life. Nowadays, a lot of services are driven by optimization algorithms, enabling us to make the best use of the available resources while guaranteeing a level of service. Ex- amples of such services are public transportation, goods delivery, university time-tabling, and patient scheduling. Thanks also to the open data movement, a lot of usage data about public and private services is accessible today, sometimes in aggregate form, to everyone. Examples of such data are traffic information (Google), bike sharing systems usage (CitiBike NYC), location services, etc. The availability of all this body of data allows us to better understand how people interacts with these services. However, in order for this information to be useful, it is necessary to develop tools to extract knowledge from it and to drive better decisions. In this context, optimization is a powerful tool, which can be used to improve the way the available resources are used, avoid squandering, and improve the sustainability of services. The fields of meta-heuristics, artificial intelligence, and oper- ations research, have been tackling many of these problems for years, without much interaction. However, in the last few years, such communities have started looking at each other’s advance- ments, in order to develop optimization techniques that are faster, more robust, and easier to maintain. This effort gave birth to the fertile field of hybrid meta-heuristics.openDottorato di ricerca in Ingegneria industriale e dell'informazioneopenUrli, Tommas

Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem

We consider the university course timetabling problem, which is one of the most studied problems in educational timetabling. In particular, we focus our attention on the formulation known as the curriculum-based course timetabling problem, which has been tackled by many researchers and for which there are many available benchmarks. The contribution of this paper is twofold. First, we propose an effective and robust single-stage simulated annealing method for solving the problem. Secondly, we design and apply an extensive and statistically-principled methodology for the parameter tuning procedure. The outcome of this analysis is a methodology for modeling the relationship between search method parameters and instance features that allows us to set the parameters for unseen instances on the basis of a simple inspection of the instance itself. Using this methodology, our algorithm, despite its apparent simplicity, has been able to achieve high quality results on a set of popular benchmarks. A final contribution of the paper is a novel set of real-world instances, which could be used as a benchmark for future comparison

Accurately Measuring the Satisfaction of Visual Properties in Virtual Camera Control

International audienceAbstract. Declarative approaches to camera control model inputs as properties on the camera and then rely on constraint-based and/or optimization techniques to compute the camera parameters or paths that best satisfy those properties. To reach acceptable performances, such approaches often (if not always) compute properties satisfaction in an approximate way. Therefore, it is difficult to measure results in terms of accuracy, and also compare approaches that use different approxima- tions. In this paper, we propose a simple language which can be used to express most of the properties proposed in the literature and whose semantics provide a way to accurately measure their satisfaction. The language can be used for several purposes, for example to measure how accurate a specific approach is and to compare two distinct approaches in terms of accuracy

Single- and multi-objective genetic programming: new bounds for weighted order and majority

We consolidate the existing computational complexity analysis of genetic programming (GP) by bringing together sound theoretical proofs and empirical analysis. In particular, we address computational complexity issues arising when coupling algorithms using variable length representation, such as GP itself, with different bloat-control techniques. In order to accomplish this, we first introduce several novel upper bounds for two single- and multi-objective GP algorithms on the generalised Weighted ORDER and MAJORITY problems. To obtain these, we employ well-established computational complexity analysis techniques such as fitness-based partitions, and for the first time, additive and multiplicative drift. The bounds we identify depend on two measures, the maximum tree size and the maximum population size, that arise during the optimization run and that have a key relevance in determining the runtime of the studied GP algorithms. In order to understand the impact of these measures on a typical run, we study their magnitude experimentally, and we discuss the obtained findings.Anh Nguyen, Tommaso Urli, Markus Wagnerhttp://www.sigevo.org/foga-2013

Temporal MCDA Methods for Decision-Making in Sustainable Development Context

Public decision-making problems are more and more complex in a context where decisions have to be made based concurrently on economic, social, and environmental considerations. In this context, decisions need to be evaluated in the short, medium, and long term because their planning horizons are usually of several years or even decades. A literature review on MCDA methods used in the sustainable development (SD) context shows that most MCDA methods used are static and existing research does not propose any aggregation framework for temporal assessment of actions. In the last 5 years, development of temporal MCDA has witnessed the interest of some researchers. However, the latest developments remain limited, and only a few research studies offer aggregation frameworks for multi-period settings. This paper presents two recent temporal MCDA methods that were applied in SD context. The first is MUPOM method which demonstrates how outranking methods, based on concordance-discordance principles, can be generalized to processing temporal impacts of decisions. The second, named PROMETHEE-MP, consists of a multi-period generalization of PROMETHEE under random uncertainty

Zamke za nositelje naboja u stupnjevanim InGaAS fotodiodama s velikim sadržajem indija

Carrier traps in In0.82Ga0.18As, introduced during manufacturing of photodiodes by vapour phase epitaxy (VPE), have been studied by electrical measurements. Two groups of localized energy levels associated with traps were found in photodiodes annealed at higher temperature after fabrication: the first, at Ec- 0.14 eV, and the second located deeper, close to the middle of the energy gap. Electrically activated dislocations by association with some impurities are responsible for the occurrence of the deeper levels.Električnim mjerenjima istražena su svojstva zamki za nositelje naboja koje nastaju pri izradi fotodioda iz In0.82Ga0.18As metodom epitaksijalnog rasta iz parne faze (VPE). U fotodiodama, koje su naknadno napuštane nakon izrade na povišenoj temperaturi, zapažene su dvije skupine lokaliziranih nivoa zamki: jedan plići na Ec − 0, 14 eV i druge dublje nešto ispod sredine zabranjenog energijskog pojasa. Ustanovljeno je da su električki aktivirane dislokacije primjesama odgovorne za pojavu dubljih nivoa