We propose a general methodology based on robust optimization to address the problem of optimally controlling a supply chain subject to stochastic demand in discrete time. The attractive features of the proposed approach are: (a) It incorporates a wide variety of phenomena, including demands that are not identically distributed over time and capacity on the echelons and links; (b) it uses very little information on the demand distributions; (c) it leads to qualititatively similar optimal policies (basestock policies) as in dynamic programming; (d) it is numerically tractable for large scale supply chain problems even in networks, where dynamic programming methods face serious dimensionality problems; (e) in preliminary computation experiments, it often outperforms dynamic programming based solutions for a wide range of parameters. / Singapore-MIT Alliance (SMA)

Given the increasing demand for energy, climate change, and environmental concern of fossil fuels, it is becoming increasingly significant to find alternative renewable energy sources. Bioethanol as one sort of cellulosic biofuel produced from lignocellulosic biomass feedstocks has shown great potential as a renewable resource. Delivering a competitive, sustainable biofuel product requires comprehensive supply chain planning and design. Developing economically and environmentally optimal supply chain models is necessary in this context. Also, designing biomass bioethanol supply chain (BBSC) models addressing social issues requires using second-generation biomass which is not a source of food for humans. Currently, corn as a first-generation feedstock is the primary source of bioethanol in the United States which has given growth to new social issues such as the food versus fuel debate. Considering incentives for first-generation bioethanol producers to switch to second-generation biomass and associated production technologies will help to address such social issues. The scope of this study focuses on analyzing economic and environmental market incentives for second-generation bioethanol producers while considering different carbon policies as penalties and restrictions for emissions coming from BBSC activities. First, we develop an integrated life cycle emission and energy optimization model for analyzing an entire second-generation bioethanol supply chain using switchgrass as the source of biomass while finding the most appropriate potential locations for building new cellulosic biorefineries in North Dakota. Second, we propose a supply chain model by comparing a first-generation (corn) and a second-generation (corn stover) bioethanol supply chain to analyze how policymakers can incentivize first-generation bioethanol producers to switch their technology and biomass supply from first-generation to second-generation biomass. Third, we develop the model further by investigating the impact of four different carbon policies including the carbon tax, carbon cap, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. This research will help to design robust BBSCs focused on sustainability in order to optimally utilize second-generation biomass resources in the future. The findings can be utilized by renewable energy policy decision makers, bioethanol producers, and investors to operate in a competitive market while protecting the environment.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第17873号 / 工博第3782号 / 新制||工||1578(附属図書館) / 30693 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 谷口 栄一, 教授 藤井 聡, 准教授 宇野 伸宏 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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<p>Autoinjectors are pen-like devices that typically deliver drug products of 2 mL or less. They shield the needle before and after use, reducing patient anxiety from needle phobia and mitigating the risk of needlestick injuries and accidental contamination. Additionally, automatic delivery ensures more consistent needle penetration depth and injection force than manual injection methods. </p> <p><br></p> <p>To optimize autoinjector design, this thesis presents experimentally validated computational models that describe the processes of needle insertion, drug delivery, and transport of subcutaneously administered therapeutic proteins in the body. A multi-objective optimization framework is also proposed to guide the design of autoinjectors.</p> <p><br></p> <p>This thesis focuses on spring-driven autoinjectors, the most common type of autoinjector. It begins with an overview of the interactions between the spring-driven autoinjector, tissue, and therapeutic proteins. Moving on to Chapter 2, a computational model is presented to accurately predict the kinematics of the syringe barrel and plunger during the needle insertion process.</p> <p><br></p> <p>In Chapter 3, we present a quasi-steady model for the drug delivery process, which considers the rheology of therapeutic proteins. The Carreau model is adopted to describe protein viscosity, and explicit relationships between flow rate and pressure drop in the needle are derived. Furthermore, the applicable regime for the power-law model for protein viscosity is identified.</p> <p><br></p> <p>Chapter 4 quantifies the impact of sloshing and cavitation on therapeutic proteins in the syringe. Additionally, a workflow is presented to integrate available simulation tools to predict the performance of spring-driven autoinjectors. The influence of each design parameter of spring-driven autoinjectors on their performance is also discussed. </p> <p><br></p> <p>The spring-driven autoinjector delivers therapeutic proteins through subcutaneous administration. To gain insights into the transport process of therapeutic proteins, Chapter 5 presents a physiologically-based pharmacokinetic model that has been validated against experimental data for humans and rats. The lymph flow rate significantly affects the bioavailability of therapeutic proteins. This finding highlights the importance of studying the transport of therapeutic proteins in the lymphatic system in future research.</p> <p><br></p> <p>Chapter 6 provides a multi-objective design optimization framework for the spring-driven autoinjector. The computational model is replaced with an accurate deep neural network surrogate to improve the computational efficiency.  Using this surrogate model, we conduct a sensitivity analysis to identify essential design parameters. After that, we perform multi-objective optimization to find promising design candidates.</p> <p><br></p> <p>Chapter 7 presents a model for bubble dynamics in a protein solution. An explicit expression for the bubble dissolution rate is derived, enabling extraction of the interfacial properties of the protein-coated interface from the measured bubble radii. Moreover, analytical solutions for the response of a protein-coated bubble to an imposed acoustic pressure are derived. This work provides insight into protein-coated bubbles, which are used as vehicles to deliver drugs, as active miniature tracers to probe the rheology of soft and biological materials, or as contrast agents to enhance the ultrasound backscatter in ultrasonic imaging.</p> <p><br></p> <p>At last, in Chapter 8, we introduce a model for laser-induced cavitation that considers several key factors, such as liquid compressibility, heat transfer, and non-equilibrium evaporation and condensation. Our model's predictions for the time-course of bubble radii have been validated with experimental data. Moreover, our model reveals that the reduction of the bubble's oscillation amplitude is primarily due to a decrease in the number of vapor molecules inside the bubble, highlighting the crucial role of phase change in laser-induced cavitation bubbles.</p> <p><br></p> <p>The developed computational models and framework provide crucial insights into the development of spring-driven autoinjectors and cavitation bubbles. These studies can also enhance the efficacy and safety of the delivery of therapeutic proteins, ultimately improving patient outcomes.</p>

This dissertation focuses on the introduction and development of an integrated model-based design and optimization platform to solve the optimal design and optimal control, or hardware and software co-design, problem for hybrid electric propulsion systems. Specifically, the hybrid and plug-in hybrid electric powertrain systems with diesel and natural gas (NG) fueled compression ignition (CI) engines and large Li-ion battery energy storage system (ESS) for propelling a hybrid electric marine vessel are investigated. The combined design and control optimization of the hybrid propulsion system is formulated as a bi-level, nested optimization problem. The lower-level optimization applies dynamic programming (DP) to ensure optimal energy management for each feasible powertrain system design, and the upper-level global optimization aims at identifying the optimal sizes of key powertrain components for the powertrain system with optimized control. Recently, Li-ion batteries became a promising ESS technology for electrified transportation applications. However, these costly Li-ion battery ESSs contribute to a large portion of the powertrain electrification and hybridization costs and suffer a much shorter lifetime compared to other key powertrain components. Different battery performance modelling methods are reviewed to identify the appropriate degradation prediction approach. Using this approach and a large set of experimental data, the performance degradation and life prediction model of LiFePO4 type battery has been developed and validated. This model serves as the foundation for determining the optimal size of battery ESS and for optimal energy management in powertrain system control to achieve balanced reduction of fuel consumption and the extension of battery lifetime. In modelling and design of different hybrid electric marine propulsion systems, the life cycle cost (LCC) model of the cleaner, hybrid propulsion systems is introduced, considering the investment, replacement and operational costs of their major contributors. The costs of liquefied NG (LNG), diesel and electricity in the LCC model are collected from various sources, with a focus on present industrial price in British Columbia, Canada. The greenhouse gas (GHG) and criteria air pollutant (CAP) emissions from traditional diesel and cleaner NG-fueled engines with conventional and optimized hybrid electric powertrains are also evaluated. To solve the computational expensive nested optimization problem, a surrogate model-based (or metamodel-based) global optimization method is used. This advanced global optimization search algorithm uses the optimized Latin hypercube sampling (OLHS) to form the Kriging model and uses expected improvement (EI) online sampling criterion to refine the model to guide the search of global optimum through a much-reduced number of sample data points from the computationally intensive objective function. Solutions from the combined hybrid propulsion system design and control optimization are presented and discussed. This research has further improved the methodology of model-based design and optimization of hybrid electric marine propulsion systems to solve complicated co-design problems through more efficient approaches, and demonstrated the feasibility and benefits of the new methods through their applications to tugboat propulsion system design and control developments. The resulting hybrid propulsion system with NG engine and Li-ion battery ESS presents a more economical and environmentally friendly propulsion system design of the tugboat. This research has further improved the methodology of model-based design and optimization of hybrid electric marine propulsion systems to solve complicated co-design problems through more efficient approaches, and demonstrated the feasibility and benefits of the new methods through their applications to tugboat propulsion system design and control developments. Other main contributions include incorporating the battery performance degradation model to the powertrain size optimization and optimal energy management; performing a systematic design and optimization considering LCC of diesel and NG engines in the hybrid electric powertrains; and developing an effective method for the computational intensive powertrain co-design problem. / Graduate