The objective of this research is to devise a procedure to generate random Set Covering Problem (SCP) instances with known optimal solutions and correlated coefficients. The procedure presented in this work can generate a virtually unlimited number of SCP instances with known optimal solutions and realistic characteristics, thereby facilitating testing of the performance of SCP heuristics and algorithms. A four-phase procedure based on the Karush-Kuhn-Tucker (KKT) conditions is proposed to generate SCP instances with known optimal solutions and correlated coefficients. Given randomly generated values for the objective function coefficients and the sum of the binary constraint coefficients for each variable and a randomly selected optimal solution, the procedure: (1) calculates the range for the number of possible constraints, (2) generates constraint coefficients for the variables with value one in the optimal solution, (3) assigns values to the dual variables, and (4) generates constraint coefficients for variables with value 0 in the optimal solution so that the KKT conditions are satisfied. A computational demonstration of the procedure is provided. A total of 525 SCP instances are simulated under seven correlation levels and three levels for the number of constraints. Each of these instances is solved using three simple heuristic procedures. The performance of the heuristics on the SCP instances generated is summarized and analyzed. The performance of the heuristics generally worsens as the expected correlation between the coefficients increases and as the number of constraints increases. The results provide strong evidence of the benefits of the procedure for generating SCP instances with correlated coefficients, and in particular SCP instances with known optimal solutions.

The aim of this research is to investigate parallel problem generation for structured optimization problems. The result of this research has produced a novel parallel model generator tool, namely the Parallel Structured Model Generator (PSMG). PSMG adopts the model syntax from SML to attain backward compatibility for the models already written in SML [1]. Unlike the proof-of-concept implementation for SML in [2], PSMG does not depend on AMPL [3]. In this thesis, we firstly explain what a structured problem is using concrete real-world problems modelled in SML. Presenting those example models allows us to exhibit PSMG’s modelling syntax and techniques in detail. PSMG provides an easy to use framework for modelling large scale nested structured problems including multi-stage stochastic problems. PSMG can be used for modelling linear programming (LP), quadratic programming (QP), and nonlinear programming (NLP) problems. The second part of this thesis describes considerable thoughts on logical calling sequence and dependencies in parallel operation and algorithms in PSMG. We explain the design concept for PSMG’s solver interface. The interface follows a solver driven work assignment approach that allows the solver to decide how to distribute problem parts to processors in order to obtain better data locality and load balancing for solving problems in parallel. PSMG adopts a delayed constraint expansion design. This allows the memory allocation for computed entities to only happen on a process when it is necessary. The computed entities can be the set expansions of the indexing expressions associated with the variable, parameter and constraint declarations, or temporary values used for set and parameter constructions. We also illustrate algorithms that are important for delivering efficient implementation of PSMG, such as routines for partitioning constraints according to blocks and automatic differentiation algorithms for evaluating Jacobian and Hessian matrices and their corresponding sparsity partterns. Furthermore, PSMG implements a generic solver interface which can be linked with different structure exploiting optimization solvers such as decomposition or interior point based solvers. The work required for linking with PSMG’s solver interface is also discussed. Finally, we evaluate PSMG’s run-time performance and memory usage by generating structured problems with various sizes. The results from both serial and parallel executions are discussed. The benchmark results show that PSMG achieve good parallel efficiency on up to 96 processes. PSMG distributes memory usage among parallel processors which enables the generation of problems that are too large to be processed on a single node due to memory restriction.

Le bâtiment devient de plus en plus un système complexe où les flux énergétiques doivent être gérés en fonction des usages : on parle de bâtiments intelligents. Il s'ensuit une complexité croissante pour les concepteurs, qui doivent s'intéresser autant au bâtiment lui-même (plusieurs sources électriques et multiplication des charges) qu'à ses équipements, sa gestion énergétique mais aussi aux interactions avec l'environnement extérieur (flux d'informations exogènes sur le marché d'énergie, prix d'achat et de revente, subventions à l'auto-consommation, etc...). Il est désormais nécessaire de coupler la phase de conception avec celle de gestion énergétique du bâtiment. Les travaux de cette thèse visent à proposer une démarche méthodologique permettant de formuler automatiquement les problèmes d'optimisation exploitables autant en conception qu'en exploitation du système bâtiment. La démarche est basée sur les concepts issus de l'ingénierie dirigée par les modèles (IDM). / The building is becoming increasingly a complex system where energy flows must be managed according to consumption: we talk about intelligent buildings. This means increasing complexity for designers who need to focus as much on building itself (several power sources and multiplication of charges) in its equipment, and in its energy management but also to interactions with the external environment (exogenous flow of information on the energy market, the purchase price and resale, subsidies for self-consumption, etc. ...). It is now necessary to tie the design phase with that of building energy management. The work of this thesis aims at proposing a methodological approach to automatically formulate optimization problems at design stage and under operation of building. The approach is based on concepts from the model-driven engineering (MDE).

Cette thèse aborde le défi de la conception des systèmes de mobilité urbaine. Elle vise à développer un modèle d’expérience-voyageur pour faciliter, dans une démarche de conception, le diagnostic des problèmes de voyage et améliorer la pertinence des modèles de transport pour les voyageurs. En combinant les points de vue de la conception de l’expérience-utilisateur et du transport, elle contribue à approfondir la compréhension de comment les voyageurs vivent leur voyage et particulièrement des problèmes qu’ils rencontrent. Le premier axe d’investigation est lié à la modélisation de l’expérience-voyageur pour alimenter un diagnostic pertinent et riche des problèmes de voyage. Dans un deuxième axe, les voyageurs sont impliqués, par une démarche de théorie ancrée, pour identifier les problèmes qu’ils rencontrent lors de l’utilisation de systèmes de mobilité urbaine au moyen de stimuli appropriés.Un troisième axe introduit des attributs subjectifs de voyage dans des modèles de transport afin d’améliorer leur précisionCette recherche utilise la recherche-action comme méthodologie. Elle combine revue de littérature dans les disciplines de conception et de transport, quatre observations terrain, quinze interviews en profondeurs aves des voyageurs et experts en transport, cinq ateliers de problématisation, et deux expérimentations, dans une amélioration cyclique des résultats. Les différentes utilisations du modèle ont permis un diagnostic approfondi de trois systèmes de mobilité urbaine (train de banlieue, bus à la demande, navette sur voie dédiée) et la mise au point d'attributs centrés sur le voyageur pour un modèle d’optimisation et une simulation multi-agents qui ont été testé par une enquête de plus de 450 participants. / This thesis addresses the challenge of designing urban mobility systems. It aims at developing a traveler experience model to help diagnose travel problems in a design approach and improve the relevance of transportation models for travelers. By combining the views of user-experience design and transportation, it helps to deepen the understanding of how travelers experience their journey and especially the problems they face. The first axis of investigation is related to the modeling of the traveler experience to feed a relevant and rich diagnosis of travel problems. In the second axis, travelers are involved, through a grounded theory approach, to identify the problems they encounter when using urban mobility systems, using appropriate stimuli.The third axis introduces travel subjective attributes into transport models to improve their accuracy.This research used action research as a methodology. It combines literature review in design and transportation disciplines, four field observations, fifteen in-depth interviews with transport travelers and experts, five problem-solving workshops, and two experiments, in a cyclical improvement of results. The various uses of the model have led to an in-depth diagnosis of three urban mobility systems (suburban train, on-demand bus, dedicated shuttle) and the development of traveler-centric attributes for an optimization model and a multi-agent simulation that was tested by a survey of over 450 participants.