The demand for aviation has been steadily growing over the past few decades and will keep increasing in the future. The anticipated growth of traffic demand will cause the current airspace system, one that is already burdened by heavy operations and inefficient usage, to become even more congested than its current state. Because busy airports in the United States (U.S.) are becoming "bottlenecks" of the National Airspace System (NAS), it is of great importance to discover the most efficient means of using existing facilities to improve airport operations. This dissertation aims at designing an efficient airport surface operations management system that substantially contributes to the modernized NAS. First, a global comparison is conducted in the major airports within the U.S. and Europe in order to understand, compare, and explore the differences of surface operational efficiency in two systems. The comparison results are then presented for each airport pair with respect to various operational performance metrics, as well as airport capacity and different demand patterns. A detailed summary of the associated Air Traffic Management (ATM) strategies that are implemented in the U.S. and Europe can be found towards the end of this work. These strategies include: a single Air Navigation Service Provider (ANSP) in the U.S. and multiple ANSPs in Europe, airline scheduling and demand management differences, mixed usage of Instrument Flight Rule (IFR) and Visual Flight Rules (VFR) operations in the U.S., and varying gate management policies in two regions. For global comparison, unimpeded taxi time is the reference time used for measuring taxi performance. It has been noted that different methodologies are currently used to benchmark taxi times by the performance analysis groups in the U.S. and Europe, namely the Federal Aviation Authority (FAA) and EUROCONTROL. The consistent methodology to measure taxi efficiency is needed for the facilitation of global benchmarking. Therefore, after an in-depth factual comparison conducted for two varying methodologies, new methods to measure unimpeded taxi times are explored through various tools, including simulation software and projection of historical surveillance data. Moreover, a sophisticated statistical model is proposed as a state-of-the-art method to measure taxi efficiency while quantifying the impact of various factors to taxi inefficiency and supporting decision-makers with reliable measurements to improve the operational performance. Lastly, a real-time integrated airport surface operations management (RTI-ASOM) is presented to fulfil the third objective of this dissertation. It provides optimal trajectories for each aircraft between gates and runways with the objective of minimizing taxi delay and maximizing runway throughput. The use of Mixed Integer Linear Programming (MIP) formulation, Dynamic Programming for decomposition, and CPLEX optimization can permit the use of an efficient solution algorithm that can instantly solve the large-scale optimization problem. Examples are shown based on one-day track data at LaGuardia Airport (LGA) in New York City. In additional to base scenarios with historical data, simulation through MATLAB is constructed to provide further comparable scenarios, which can demonstrate a significant reduction of taxi times and improvement of runway utilization in RTI-ASOM. By strategically holding departures at gates, the application of RTI-ASOM also reduces excess delay on the airport surface, decreases fuel consumption at airports, and mitigates the consequential environmental impacts.

During the past decades, there have been an acceleration of customers’ needs of customized products. This have pressured many companies in offering customized products in order to reach customer satisfaction and keep growing and expanding their market share. Nowadays the competitive pressure between companies with the same product segment is increasing and the market requirements are changing rapidly. To handle this competition, many manufacturing companies are focusing on mass customized production. However, mass customization has been a topic of interest for many researchers. The focus has been in studying and understanding the complexity and the constrains that a company encounter when dealing with mass customized production in mostly the automotive industry. One of the constrains that have been less highlighted by researchers were the effects that mass customized production has on the output variation of production lines. Therefore, the purpose of this thesis is to investigate how to minimize output variations in mass customized production within assembly lines. To do that the researchers focused on studying the output variation in whitegoods industry instead of the automotive industry that captures most of the existing research. The study includes two main research questions. The first question was to identify the factors that affect the output variation in mass customized production. While the second question was to identify applicable solutions that can be used to stabilize the output. To ensure the reliability and validity of the research findings, the researchers used multiple case study combined with literature reviews. To answer the research questions several data collection techniques were used in the multiple case study conducted on two lines. Those data collection techniques were participating observations, document reviews, a semi-structured interview and many conversations with the affected persons. The findings from the case study for research question one showed consistency with the theories described in the theoretical background. This was evident because similar problems when it comes to output variations were defined in the theories. One problem was about the simplification of assembly line balancing problem that was evident in both case studies. This was an effect of the missing product family classification which created a high output variation because of the use of only one line balancing for all product variants. Another problem identified included functionality problems of machines because of performing wrong assembly or the use of the wrong component. There was as well the problem of missing or delayed components which was an effect of the lack of structure in the material picking and the deliveries performed for both lines. A combination of those factors created the experienced output variation on both lines. To deal with those problems and minimize the output variation, a solution approach was defined in the second research question. This approach was created together with the case company with the help of the theoretical background and it presents several steps to follow when attacking and resolving similar problems as presented above. The most important conclusion of this thesis is that, when dealing with output variation in mass customized production where people are performing the assembly, it is essential to provide the right conditions for them to ensure that they have the right knowledge base to perform the requested assembly.

Les problèmes de séquencement et d'ordonnancement forment une famille de problèmes combinatoires qui implique la programmation dans le temps d'un ensemble d'opérations soumises à des contraintes de capacités et de ressources. Nous contribuons dans cette thèse à la résolution de ces problèmes dans un contexte de satisfaction de contraintes et d'optimisation combinatoire. Nos propositions concernent trois aspects différents: comment choisir le prochain nœud à explorer (recherche)? comment réduire efficacement l'espace de recherche (propagation)? et que peut-on apprendre des échecs rencontrés lors de la recherche (apprentissage)? Nos contributions commencent par une étude approfondie des heuristiques de branchement pour le problème de séquencement de chaîne d'assemblage de voitures. Cette évaluation montre d'abord les paramètres clés de ce qui constitue une bonne heuristique pour ce problème. De plus, elle montre que la stratégie de branchement est aussi importante que la méthode de propagation. Deuxièmement, nous étudions les mécanismes de propagation pour une classe de contraintes de séquencement à travers la conception de plusieurs algorithmes de filtrage. En particulier, nous proposons un algorithme de filtrage complet pour un type de contrainte de séquence avec une complexité temporelle optimale dans le pire cas. Troisièmement, nous investiguons l'impact de l'apprentissage de clauses pour résoudre le problème de séquencement de véhicules à travers une nouvelle stratégie d'explication réduite pour le nouveau filtrage. L'évaluation expérimentale montre l'importance de l'apprentissage de clauses pour ce problème. Ensuite, nous proposons une alternative pour la génération retardée de variables booléennes pour encoder les domaines. Finalement, nous revisitons les algorithmes d'analyse de conflits pour résoudre les problèmes d'ordonnancement disjonctifs. En particulier, nous introduisons une nouvelle procédure d'analyse de conflits dédiée pour cette famille de problèmes. La nouvelle méthode diffère des algorithmes traditionnels par l'apprentissage de clauses portant uniquement sur les variables booléennes de disjonctions. Enfin, nous présentons les résultats d'une large étude expérimentale qui démontre l'impact de ces nouveaux mécanismes d'apprentissage. En particulier, la nouvelle méthode d'analyse de conflits a permis de découvrir de nouvelle bornes inférieures pour des instances largement étudiées de la littérature / Sequencing and scheduling involve the organization in time of operations subject to capacity and resource constraints. We propose in this dissertation several improvements to the constraint satisfaction and combinatorial optimization methods for solving these problems. These contributions concern three different aspects: how to choose the next node to explore (search)? how much, and how efficiently, one can reduce the search space (propagation)? and what can be learnt from previous failures (learning)? Our contributions start with an empirical study of search heuristics for the well known car-sequencing problem. This evaluation characterizes the key aspects of a good heuristic and shows that the search strategy is as important as the propagation aspect in this problem. Second, we carefully investigate the propagation aspect in a class of sequencing problems. In particular, we propose an algorithm for filtering a type of sequence constraints which worst case time complexity is lower than the best known upper bound, and indeed optimal. Third, we investigate the impact of clause learning for solving the car-sequencing problem. In particular, we propose reduced explanations for the new filtering. The experimental evaluation shows compelling evidence supporting the importance of clause learning for solving efficiently this problem. Next, we revisit the general approach of lazy generation for the Boolean variables encoding the domains. Our proposition avoids a classical redundancy issue without computational overhead. Finally, we investigate conflict analysis algorithms for solving disjunctive scheduling problems. In particular, we introduce a novel learning procedure tailored to this family of problems. The new conflict analysis differs from conventional methods by learning clauses whose size are not function of the scheduling horizon. Our comprehensive experimental study with traditional academic benchmarks demonstrates the impact of the novel learning scheme that we propose. In particular, we find new lower bounds for a well known scheduling benchmark

A CONWIP (Constant Work-In-Progress) system is basically a hybrid system with a PUSH-PULL interface at the first machine in the line. This research addresses the most general case of a cyclic CONWIP system by incorporating two additional constraints over earlier studies namely; stochastic processing times and limited intermediate storage. One of the main issues in the design of a CONWIP system is the WIP level 'M', to be maintained. This research proposes an iterative procedure to determine this optimal level. The second main issue is the optimization of the line by determining an appropriate job sequence. This research assumes a 'permutational' scheduling policy and proposes an iterative approach to find the best sequence. The approach utilizes a controlled enumerative approach called the Fast Insertion Heuristic (FIH) coupled with a method to appraise the quality of every enumeration at each iteration. This is done by using a modified version of the Floyd's algorithm, to determine the cycle time (or Flow time) of a partial/full solution. The performance measures considered are the Flow time and the Interdeparture time (inverse of throughput). Finally, both the methods suggested for the two subproblems, are tested through computer implementations to reveal their proficiency. / Master of Science