Within the wide scope of logistics management,transportation plays a central role and is a crucialactivity in both production and service industry.Among others, it allows for the timely distributionof goods and services between suppliers, productionunits, warehouses, retailers, and final customers.More specifically, Vehicle Routing Problems(VRPs) deal with the design of a set of minimal costroutes that serve the demand for goods orservices of a set of geographically spread customers,satisfying a group of operational constraints.While it was traditionally a static problem, recenttechnological advances provide organizations withthe right tools to manage their vehicle fleet in realtime. Nonetheless, these new technologies alsointroduce more complexity in fleet managementtasks, unveiling the need for decision support systemsdedicated to dynamic vehicle routing. In thiscontext, the contributions of this Ph.D. thesis arethreefold : (i) it presents a comprehensive reviewof the literature on dynamic vehicle routing ; (ii)it introduces flexible optimization frameworks thatcan cope with a wide variety of dynamic vehiclerouting problems ; (iii) it defines a new vehicle routingproblem with numerous applications.

We address the task of mapping a given domain model (e.g., an industry-standard reference model) for a given domain (e.g., ERP), with the source code of an independently developed application in the same domain. This has applications in improving the understandability of an existing application, migrating it to a more flexible architecture, or integrating it with other related applications. We build on a previous approach, which uses relationships among source code elements for improving the precision of the mapping process. We extend this approach by considering relationships among domain model elements in addition to relationships among source code elements, and also by stating the mapping process as an optimization problem. We have implemented our approach, and compared it with the previous approach. We show that our approach gives significantly better precision as well as recall than the previous approach when applied on a real industry-standard domain model and an open-source application.

The aim of this diploma thesis is to design and efficiently implement a framework to support optimization modelling. The emphasis is placed on two-stage stochastic optimization problems and performing calculations on large data. The computing core uses the GAMS system and with using its application interface and Python programming language, the user will be able to efficiently acquire and process input and output data. The separation of the data logic and the application logic then offers a wide range of options for testing and experimenting with a general model on dynamically changing input data. The thesis is also focused on an evaluation of the framework complexity. The framework performance was evaluated by measuring the time required to complete the required task for various use cases, on the increasing sample size of input data.

Dans les systèmes manufacturiers de plus en plus complexes, les variations du processus de fabrication et de ses paramètres opératoires ainsi que leurs effets sur l’ensemble du système doivent être maîtrisés, mesurés et contrôlés. Cette thèse propose un cadre d’optimisation pour l’élaboration d’un plan d’inspection optimal qui permet une prise de décision opérationnelle afin d’assurer la satisfaction des objectifs stratégiques (réduction des coûts, amélioration de la qualité, augmentation de la productivité, …). La prise de décision se divise en trois questions : Quoi contrôler ? Comment contrôler ? Quand contrôler ? Le manque d'informations fiables sur les processus de production et plusieurs facteurs environnementaux est devenu un problème important qui impose la prise en compte de certaines incertitudes lors de la planification des inspections. Cette thèse propose plusieurs formulations du problème d’optimisation de la planification du processus d'inspection, dans lesquelles, les paramètres sont incertains et les machines de production sont sujettes aux défaillances. Ce problème est formulé par des modèles de programmation mathématique avec les objectifs : minimiser le coût total de fabrication, maximiser la satisfaction du client, et minimiser le temps de la production totale. En outre, les méthodes Taguchi et Monte Carlo sont appliquées pour faire face aux incertitudes. En raison de la complexité des modèles proposés, les algorithmes de méta-heuristiques sont utilisés pour trouver les solutions optimales. / Quality inspection in multistage production systems (MPSs) has become an issue and this is because the MPS presents various possibilities for inspection. The problem of finding the best inspection plan is an “inspection planning problem”. The main simultaneous decisions in an inspection planning problem in a MPS are: 1) which quality characteristics need to be inspected, 2) what type of inspection should be performed for the selected quality characteristics, 3) where these inspections should be performed, and 4) how the inspections should be performed. In addition, lack of information about production processes and several environmental factors has become an important issue that imposes a degree of uncertainty to the inspection planning problem. This research provides an optimization framework to plan an inspection process in a MPS, wherein, input parameters are uncertain and inspection tools and production machines are subject to breakdown. This problem is formulated through several mixed-integer mathematical programming models with the objectives of minimizing total manufacturing cost, maximizing customer satisfaction, and minimizing total production time. Furthermore, Taguchi and Monte Carlo methods are applied to cope with the uncertainties. Due to the complexity of the proposed models, meta-heuristic algorithms are employed to find optimal or near-optimal solutions. Finally, this research implements the findings and methods of the inspection planning problem in another application as hub location problem. General and detail concluding remarks are provided for both inspection and hub location problems.

<p>Powder bed fusion (PBF) process is a metal additive manufacturing process, which can build parts with any complexity from a wide range of metallic materials. Research in the PBF process predominantly focuses on the impact of a few parameters on the ultimate properties of the printed part. The lack of a systematic approach to optimizing the process parameters for a better performance of given material results in a sub-optimal process limiting the potentialof the application. This process needs a comprehensive study of all the influential parameters and their impact on the mechanical and microstructural properties of a fabricated part. Furthermore, there is a need to develop a quantitative system for mapping the material properties and process parameters with the ultimate quality of the fabricated part to achieve improvement in the manufacturing cycle as well as the quality of the final part produced by the PBF process. To address the aforementioned challenges, this research proposes a framework to optimize the process for 316L stainless steel material. This framework characterizes the influence of process parameters on the microstructure and mechanical properties of the fabricated part using a series of experiments. These experiments study the significance of process parameters and their variance as well as study the microstructure and mechanical properties of fabricated parts by conducting tensile, impact, hardness, surface roughness, and densification tests, and ultimately obtain the optimum range of parameters. This would result in a more complete understanding of the correlation between process parameters and part quality. Furthermore, the data acquired from the experimentsare employed to develop an intelligent parameter suggestion multi-layer feedforward (FF) backpropagation (BP) artificial neural network (ANN). This network estimates the fabrication time and suggests the parameter setting accordingly to the user/manufacturers desired characteristics of the end-product. Further, research is in progress to evaluate the framework for assemblies and complex part designs and incorporate the results in the network for achieving process repeatability and consistency.</p><br>

No description available.

No description available.