Variation is inevitable in any process, so it has to be dealt with effectively and economically. Reducing variation can be achieved in assembly lines strategically and dynamically. Implementing both the strategic and dynamic variation reduction techniques is expected to lead to further reduction in the number of failed final assemblies. The dissertation is divided into three major parts. In the first part, we propose to reduce variation for assemblies by developing efficient inspection plans based on (1) historical data for existing products, or simulated data for newly developed products; (2) Monte Carlo simulation; and (3) optimization search techniques. The cost function to be minimized is the total of inspection, rework, scrap and failure costs. The novelty of the proposed approach is three-fold. First, the use of CAD data to develop inspection plans for newly launched products is new, and has not been introduced in the literature before. Second, frequency of inspection is considered as the main decision variable, instead of considering whether or not to inspect a quality characteristic of a subassembly. Third, we use a realistic reaction plan (rework-scrap-keep) that mimics reality in the sense that not all out-of-tolerance items should be scrapped or reworked. At a certain stage, real-time inspection data for a batch of subassemblies could be available. In the second part of this dissertation, we propose utilizing this data in near real-time to dynamically reduce variation by assigning the inspected subassembly parts together. In proposing mathematical models, we found that they are hard to solve using traditional optimization techniques. Therefore, we propose using heuristics.Finally, we propose exploring opportunities to reduce the aforementioned cost function by integrating the inspection planning model with the Dynamic Throughput Maximization (DTM) model. This hybrid model adds one decision variable in the inspection planning; which is whether to implement DTM (assemble the inspected subassemblies selectively) or to assemble the inspected items arbitrarily. We expect this hybrid implementation to substantially reduce the failure cost when assembling the final assemblies for some cases. To demonstrate this, we solve a numerical example that supports our findings. / Ph. D.

<p dir="ltr">In recent years (circa 2024), the purpose of pedestrian bridges has extended beyond simply providing a safe route for pedestrians to cross an obstacle. Nowadays, pedestrian bridges are becoming works of art integrated into the design plan for the whole city. The pleasant appearance of these bridges, however, comes at the cost of requiring complex structural analysis and design, unique fabrication requirements, and construction challenges. Therefore, inspecting different types of pedestrian bridges efficiently and adequately is crucial to avoid unexpected failure during their service life. While National Bridge Inspection Standers (NBIS) regulations are only applicable to all publicly owned <i>highway</i> bridges with spans longer than twenty feet, there is no standard inspection criteria applicable across the board for any type of pedestrian bridge (FHWA 2022a). The current criteria, implemented ad-hoc by many owners, is to inspect pedestrian bridges using the traditional calendar-based inspection approach. This method is based on assigning an inspection interval not to exceed some time frame (typically 24-months) for all bridges with exceptions for some specific bridges receiving special inspections. Although this method may provide an adequate level of safety for some bridges, it doesn’t explicitly account for the current condition, variation in operational environment, and the design characteristics of the bridge. In addition, the current inspection practice of pedestrian bridges considers only inspecting bridge's <i>structural conditions</i> while some unique safety and serviceability criteria should be considered to attain an optimum level of safety and serviceability for pedestrians and cyclists on the bridge such as railing, lighting, walking surface, etc.</p><p dir="ltr">The main objective of this research is to develop an inspection criterion specifically applicable to pedestrian bridges to ensure the optimal allocation of inspection resources while maintaining an optimum safety and serviceability. In its final form, the Risk Based Inspection (RBI) methodology is applied in conjunction with reliability concepts and expert inputs from the Risk Assessment Panel (RAP) of the Indiana Department of Transportation (INDOT) to systematically evaluate the key components of the proposed approach. The proposed methodology is based on the Reliability Based Inspection procedures presented in NCHRP 782 report (Washer et al. 2014a). In this method, the inspection interval is determined based on the risk assessment, which is the product of a combination of occurrence and consequence factors. The occurrence factor is calculated based on design, loading (mechanical and environmental), and condition attributes for each type of damage. The consequence factor measures the outcomes of the occurrence of the damage under consideration. This factor is evaluated at two stages, an immediate consequence in which outcomes impact the safety of the service on and under the bridge, and a short-term consequence, in which effects influence the serviceability of the service under the bridge. Furthermore, a new factor is also introduced to the RBI approach. Specifically, what will be referred to as the inspection effectiveness factor which attempts to accounts for the reliability of the inspection technique to identify and quantify a specific defect for a given components of the bridge. The proposed approach is then applied and validated on a set of real in-service pedestrian bridges with varying materials and structural systems. The results demonstrate that the approach improves the safety and serviceability of pedestrian bridge inspections. Furthermore, it ensures a better allocation of the limited inspection resources and proves to be more cost-effective compared to current inspection practices.</p>

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.