The percentage of shop fabrication, including pipe spool fabrication, has been increasing on industrial construction projects during the past years. Industrial fabrication has a great impact on construction projects due to the fact that the productivity is higher in a controlled environment than in the field, and therefore time and cost of construction projects are reduced by making use of industrial fabrication. Effective planning and scheduling of the industrial fabrication processes is important for the success of construction projects. This thesis focuses on developing a new framework for optimizing shop scheduling, particularly pipe spool fabrication shop scheduling. The proposed framework makes it possible to capture uncertainty of the pipe spool fabrication shop while accounting for linguistic vagueness of the decision makers preferences using simulation modeling and fuzzy set theory. The implementation of the proposed framework is discussed using a real case study of a pipe spool fabrication shop. In this thesis, first, a simulation based scheduling framework is presented based on the integration of relational database management system, product modeling, process modeling, and heuristic approaches. Next, a framework for optimization of the industrial shop scheduling with respect to multiple criteria is proposed. Fuzzy set theory is used to linguistically assess different levels of satisfaction for the selected criteria. Additionally, an executable scheduling toolkit is introduced as a decision support system for pipe spool fabrication shop. / Construction Engineering and Management

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Dans les économies modernes, le vieillissement démographique, l’arrivée à la retraite des générationsdu Baby-Boom et les difficultés financières actuelles et à venir des régimes de retraite par répartition qui enrésultent laissent envisager que les ménages seront davantage amenés à épargner pour la retraite dans lecadre professionnel et non professionnel.Cette thèse propose une réflexion théorique et empirique sur la contractualisation de l’épargne retraite àtravers les plans d’épargne retraite en dehors des systèmes publics de protection sociale. Cette réflexion estmenée sur deux fronts. Dans un premier temps, nous faisons la lumière sur l’importance des plans d’épargneretraite tels qu’ils existent dans les systèmes de retraites en France et aux Etats-Unis, démontrons leur rôledans la gestion des ressources humaines des entreprises et leur intérêt dans les stratégies d’accumulationd’épargne en vue de la retraite des ménages. L’existence de l’épargne retraite par capitalisation est biensouvent admise comme une situation de fait, cette première partie de la thèse permet de fournir lesarguments micro-économiques qui justifient le recours aux plans d’épargne retraite dans la planification de laretraite. Toutefois, le développement de la contractualisation de l’épargne retraite fait apparaître de nombreuxrisques pour les épargnants, et nous proposons dans un second temps de les identifier. Parmi ces risques,le sous provisionnement en présence d’un objectif de rentes pour les épargnants et la faillite des entreprisesou des institutions financières qui offrent des plans d’épargne retraite sont parmi les plus importants. Malgréla création des agences de garantie des pensions de retraite, l’occurrence de ces risques se traduit pardes pertes substantielles de ressources pour les épargnants. Dans une seconde partie de la thèse, notreréflexion nous conduit à proposer des solutions permettant de sécuriser l’épargne retraite. / Modern economies are increasingly facing a population ageing, the massive retirement of post WorldWar II generations and resulting financial distress of their Pay-As-You-Go pension schemes. These challengesraise the need to rely more on pension saving either on an occupational or an individual basis.In this study, we conduct several theoretical and empirical investigations on pension plans as a contractualsaving vehicle for retirement, and follow two main directions. On the one hand, we shed light on theimportance of pension plans in the French and American retirement systems, demonstrate the role ofpension plans in the firms’ management of human resources and the use of pension plans in households’wealth accumulation strategies. This first part of our studies offers microeconomic insights in theusefulness of pension plans in retirement planning. However, the expansion of pension plans raises newchallenges in terms of retirement income security. Among risks that may result in income shortfalls forretirees, underfunding (whenever a certain level of income is guaranteed to retirees) and sponsoring ormanagement firms’ bankruptcy are the most important one. In spite of the creation of public entities thatprovide income insurance, plan participants incur a substantial amount of losses in the event of underfundingand bankruptcy. The second part of our contributions offers ways to secure pension benefits with priority rules.

In this thesis, the Job Shop Scheduling Problem (JSSP) is the problem of interest. The classical JSSP is well-known as an NP-hard problem. Although with current computational capabilities, the small problems are solvable using deterministic methods, it is out of reach when they are larger in size. The complexity of JSSP is further increased when process interruptions, such as machine breakdown and/or machine unavailability, are introduced. Over the last few decades, several stochastic algorithms have been proposed to solve JSSPs. However, none of them are suitable for all kinds of problems. Genetic and Memetic algorithms have proved their effectiveness in these regards, because of their diverse searching behavior. In this thesis, we have developed one genetic algorithm and three different Memetic Algorithms (MAs) for solving JSSPs. Three priority rules are designed, namely partial re-ordering, gap reduction and restricted swapping, and these have been used as local search techniques in designing our MAs. We have solved 40 well-known benchmark problems and compared the results obtained with some of the established algorithms available in the literature. Our algorithm clearly outperforms those established algorithms. For better justification of the superiority of MAs over GA, we have performed statistical significance testing (Student's t-test). The experimental results show that MA, as compared to GA, not only significantly improves the quality of solutions, but also reduces the overall computation. We have extended our work by proposing an improved local search technique, shifted gap-reduction (SGR), which improves the performance of MAs when tested with the relatively difficult test problems. We have also modified the new algorithm to accommodate JSSPs with machine unavailability and also developed a new reactive scheduling technique to re-optimize the schedule after machine breakdowns. We have considered two scenarios of machine unavailability. Firstly, where the unavailability information is available in advance (predictive), and secondly, where the information is known after a real breakdown (reactive). We show that the revised schedule is mostly able to recover if the interruptions occur during the early stages of the schedules. We also confirm that the effect of a single continuous breakdown has more impact compared to short multiple breakdowns, even if the total durations of the breakdowns are the same. Finally, for convenience of implementation, we have developed a decision support system (DSS). In the DSS, we have built a graphical user interface (GUI) for user friendly data inputs, model choices, and output generation. This DSS tool will help users in solving JSSPs without understanding the complexity of the problem and solution approaches, as well as will contribute in reducing the computational and operational costs.

The Master’s thesis deals with production scheduling in an industrial company. It uses the means of artificial intelligence to develop an appropriate production schedule in a generalized Flow-shop Programming problem. This problem can be solved by application which is a result of this thesis and was prepaired with use of the software Matlab 7.1 and its Genetic Algorithm and Direct Search toolbox. There is a part devoted to the use of advanced production systems (APS) and the concept of the operative production planning in praxis as well. The thesis pays attention to various optimization models in production scheduling and supply chain management too.