Molecular models allow computer simulations to predict the microscopic properties of macroscopic systems. Molecular modeling can also provide a fully understood test system for the application of theoretical methods. The power of a model lies in the accuracy of the parameter values which govern its mathematical behavior. In this work, a new software, called ParOpt, for general high dimensional non-linear optimization will be presented. The software provides a very general framework for the optimization of a wide variety of parameter sets. The software is especially powerful when applied to the difficult task of molecular model parameter optimization. Three applications of the ParOpt software, and the Nelder-Mead algorithm implemented within it, are presented: a coarse-grained (CG) water--ion model, a model for the determination of lipid bilayer structure via the interpretation of scattering data, and a reactive molecular dynamics (ReaxFF) model for oxygen and hydrogen. Each problem presents specific difficulties. The power and generality of the ParOpt software is illustrated by the successful optimization of such a diverse set of problems.

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2018 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 67-68). / The traditional supply chain performance depends on the efficiency of mass production, the availability of productive low cost labor and the geometry and materials of the products. Additive manufacturing, on the other hand, bypasses all these constraints and reduces the number of stages in the supply chain by allowing local production of low volume parts of greater complexity. We develop an approach for assessing the total cost when additive manufacturing is integrated into the service-parts supply chain given a set of inputs that characterize the supply chain. Specifically, we present several simulation and optimization models to help companies decide the end-of-life strategy of low volume service parts. Through sensitivity analysis, we identify regions of parameters where additive manufacturing is preferred. Moreover, we find that service parts with high lost sales unit cost and low fixed and variable additive manufacturing costs are the most suitable for additive manufacturing. / by Yijin Wei. / S.M. / S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2018 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 73-77). / A modern city generates a large volume of digital information, especially in the form of unstructured image and video data. Recent advancements in deep learning techniques have enabled effective learning and estimation of high-level attributes and meaningful features from large digital datasets of images and videos. In my thesis, I explore the potential of applying deep learning to image and video data to quantify urban tree cover and street parking utilization. Large-scale and accurate quantification of urban tree cover is important towards informing government agencies in their public greenery efforts, and useful for modelling and analyzing city ecology and urban heat island effects. We apply state-of-the-art deep learning models, and compare their performance to a previously established benchmark of an unsupervised method. / Our training procedure for deep learning models is novel; we utilize the abundance of openly available and similarly labelled street-level image datasets to pre-train our model. We then perform additional training on a small training dataset consisting of GSV images. We also employ a recently developed method called gradient-weighted class activation map (Grad-CAM) to interpret the features learned by the end-to-end model. The results demonstrate that deep learning models are highly accurate, can be interpretable, and can also be efficient in terms of data-labelling effort and computational resources. Accurate parking quantification would inform developers and municipalities in space allocation and design, while real-time measurements would provide drivers and parking enforcement with information that saves time and resources. We propose an accurate and real-time video system for future Internet of Things (IoT) and smart cities applications. / Using recent developments in deep convolutional neural networks (DCNNs) and a novel intelligent vehicle tracking filter, the proposed system combines information across multiple image frames in a video sequence to remove noise introduced by occlusions and detection failures. We demonstrate that the proposed system achieves higher accuracy than pure image-based instance segmentation, and is comparable in performance to industry benchmark systems that utilize more expensive sensors such as radar. Furthermore, the proposed system can be easily configured for deployment in different parking scenarios, and can provide spatial information beyond traditional binary occupancy statistics. / by Bill Yang Cai. / S.M. / S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2018 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 89-92). / Cities are home to more than half of the world population today and urbanization is one of this century's biggest drivers of global economic growth. The dynamics of the urban environment is thus an important question to investigate. In this thesis, techniques from statistical modeling, machine learning, data mining and econometrics are utilized to study digital traces of people's everyday lives. In particular, we investigated how people influence the economic growth of cities, as well as how the urban environment affect the decisions made by people. Focusing on the role of cities as centers of consumption, we found that a gravity model based on the availability of a large and diverse pool of amenities accurately explained human flows observed from credit card records. Investigation of the consumption patterns of individuals in Istanbul, Beijing and various metropolitan areas in the United States revealed a positive relationship between the diversity of urban amenities consumed and the city's economic growth. Taking the perspective of cities as hubs for information exchange, we modeled the interactions between individuals in the cities of Beijing and Istanbul using records of their home and work locations and demonstrated how cities which facilitate the mixing of diverse human capital are crucial to the flow of new ideas across communities and their productivity. This contributes to the body of evidence which supports the notion that efficient information exchange is the key factor that drives innovation. To investigate how urban environments shape people's decisions, we study the social influence city dwellers have on each other and showed how face-to-face interaction and information exchange across different residential communities can shape their behavior and increase the similarity of their financial habits and political views in Istanbul. / by Shi Kai Chong. / S.M. / S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program

Abstract not available

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 101-110).

<p>There exists many applications with so-called costly problems, which means that the objective function you want to maximize or minimize cannot be described using standard functions and expressions. Instead one considers these objective functions as ``black box'' where the parameter values are sent in and a function value is returned. This implies in particular that no derivative information is available.The reason for describing these problems as expensive is that it may take a long time to calculate a single function value. The black box could, for example, solve a large system of differential equations or carrying out a heavy simulation, which can take anywhere from several minutes to several hours!These very special conditions therefore requires customized algorithms. Common optimization algorithms are based on calculating function values every now and then, which usually can be done instantly. But with an expensive problem, it may take several hours to compute a single function value. Our main objective is therefore to create algorithms that exploit all available information to the limit before a new function value is calculated. Or in other words, we want to find the optimal solution using as few function evaluations as possible.A good example of real life applications comes from the automotive industry, where on the development of new engines utilize advanced models that are governed by a dozen key parameters. The goal is to optimize the model by changing the parameters in such a way that the engine becomes as energy efficient as possible, but still meets all sorts of demands on strength and external constraints.</p>

Globalization and containerization have changed the shipping industry and carriers are challenged to reshape their operational planning in order to maintain their market share. The objective of this paper is to formulate a model to determine the optimal fleet size and sailing frequency that minimizes total shipping and inventory (wait) costs for a container shipping company. The proposed model assumes an arrival process that follows a Poisson rate. We first consider unlimited ship capacity and propose a solution to determine the required fleet size and the optimal sailing frequency. We then extend the work to consider limited ship capacity. Furthermore, we introduce a cost component associated with outsourcing shipments due to insufficient capacity. The outsourced shipment is utilized when the number of containers at a port exceeds the available capacity. In the general case, a closed form solution could not be derived. Therefore, a simulation study is undertaken to analyze optimal fleet sizing, scheduling, and outsourcing policies under varying paramaters. Our study investigates the trade-off between building capacity and outsourcing in the context of cargo shipment. The model proves to be a reliable tool to determine optimal delay time at ports and optimal fleet size.

Drilling operations management will face hurdles to reduce costs and increase performance, and to do this with less experience and organizational drilling capacity. A technology called Drilling Simulators Software has shown an extraordinary potential to improve the drilling performance and reduce risk and cost. Different approaches have been made to develop drilling-simulator software. The Virtual Experience Simulator, geological drilling logs, and reconstructed lithology are some of the most successful. The drilling simulations can run multiple scenarios quickly and then update plans with new data to improve the results. Its storage capacity for retaining field drilling experience and knowledge add value to the program. This research shows the results of using drilling simulator software called Drilling Optimization Simulator (DROPS®) in the evaluation of the Aloctono block, in the Pirital field, eastern Venezuela. This formation is characterized by very complex geology, containing faulted restructures, large dips, and hard and abrasive rocks. The drilling performance in this section has a strong impact in the profitability of the field. A number of simulations using geological drilling logs and the concept of the learning curve defined the optimum drilling parameters for the block. The result shows that DROPS® has the capability to simulate the drilling performance of the area with reasonable accuracy. Thus, it is possible to predict the drilling performance using different bits and the learning-curve concept to obtain optimum drilling parameters. All of these allow a comprehensive and effective cost and drilling optimization.

Natural gas plants can have multiple owners for raw natural gas streams and processing facilities as well as for multiple products. Therefore, a proper cost allocation method is necessary for taxation of the profits from natural gas and crude oil as well as for cost sharing among gas producers. However, cost allocation methods most often used in accounting, such as the sales value method and the physical units method, may produce unacceptable or even illogical results when applied to natural gas processes. Wright and Hall (1998) proposed a new approach called the design benefit method (DBM), based upon engineering principles, and Wright et al. (2001) illustrated the potential of the DBM for reliable cost allocation for natural gas processes by applying it to a natural gas process. In the present research, a rigorous modeling technique for the DBM has been developed based upon a Taylor series approximation. Also, we have investigated a cost allocation framework that determines the virtual flows, models the equipment, and evaluates cost allocation for applying the design benefit method to other scenarios, particularly those found in the petroleum and gas industries. By implementing these individual procedures on a computer, the proposed framework easily can be developed as a software package, and its application can be extended to large-scale processes. To implement the proposed cost allocation framework, we have investigated an optimization methodology specifically geared toward economic optimization problems encountered in natural gas plants. Optimization framework can provide co-producers who share raw natural gas streams and processing plants not only with optimal operating conditions but also with valuable information that can help evaluate their contracts. This information can be a reasonable source for deciding new contracts for co-producers. For the optimization framework, we have developed a genetic-quadratic search algorithm (GQSA) consisting of a general genetic algorithm and a quadratic search that is a suitable technique for solving optimization problems including process flowsheet optimization. The GQSA inherits the advantages of both genetic algorithms and quadratic search techniques, and it can find the global optimum with high probability for discontinuous as well as non-convex optimization problems much faster than general genetic algorithms.