<p>  </p> <p>Sea level rise is a growing threat to coastal communities across the United States. Uncertainty about the extent of sea level rise poses a challenge for creating infrastructure to address this threat. Due to disagreement among decision makers on the severity of climate change, it can be challenging to determine the appropriate level of preparation for the future, which can lead to potential under or over-preparedness. To combat this uncertainty, the US Department of Defense has embraced a robust decision-making model. Decision makers should incorporate multiple future models of the world into their decision-making process. This thesis describes an effort to address these challenges at the United States Naval Academy using a decision support approach called many-objective robust decision-making. Considering decisions to upgrade their seawall at varying heights and at twenty-year intervals, a genetic algorithm was employed to identify a frontier of non-dominated upgrade strategies. Three strategies from the frontier were evaluated in hundreds of possible scenarios with varied discount rates, sea level rise projections, changes to future storminess, and building replacement costs. An analysis was performed to determine in which future conditions those three strategies were vulnerable to failing to meet objectives of having a benefit cost ratio of over 1 and limiting damage to less than $100 million over an 80-year planning horizon. This analysis will enable the US Naval Academy to determine the effectiveness of their seawall upgrade plans for preventing storm surge damage over a range of future scenarios and stakeholder preferences. Ultimately, this research found that upgrading the Naval Academy’s seawall in the near future is critical to avoiding costly damage from flooding. This research also emphasizes how variations to the assumed discount rate can reshape a cost benefit analysis.</p>

<p>  </p> <p>Identification of the phases of a large-scale natural disaster is often clouded by classes and sources of deep uncertainty, further proliferating as disaster events unfold. Focusing on three distinct phases of natural disaster relief operations, it is not necessary nor viable to eliminate all uncertainty from a natural disaster system. Instead, reducing the amount of time taken to minimize particular uncertainties may be sufficient to execute the preparation phase to carry out a response. The goal of this research is to understand the intricacies associated with forecastable and rapid-onset natural disaster events and restructure already-established tools to assist first responders and relevant decision-makers in the planning and response phases. Understanding specific foraging actions will support the considerations that must be made during the preparation phase while tying in other notable concepts, including use of a problem-structuring technique from the decision-making under deep uncertainty literature to contextualize the system of interest. The restructuring of a planning-based to a response-based problem-structuring tool will also highlight the added value in shifting from a static to a dynamic perspective. Following contextualization, utilizing an adaptive pathway approach will serve as a practical decision-support tool, allowing for open and flexible progression through the response phase of a natural disaster as events unfold, inclusive of specific triggers indicating a new event occurrence and thus, a new decision point. This paper addresses conditional criterion-based decision-making, focusing on an adaptive pathways approach in response to flooding incidents.</p>

<p> Direct policy search (DPS) has been shown to be an efficient method for identifying optimal rules (i.e., policies) for adapting a system in response to changing conditions. This dissertation describes three major advances in the usage of DPS for long-range infrastructure planning, using a specific application domain of flood risk management. We first introduce a new adaptive way to incorporate learning into DPS. The standard approach identifies policies by optimizing their average performance over a large ensemble of future states of the world (SOW). Our approach exploits information gained over time, regarding what kind of SOW is being experienced, to further improve performance via adaptive meta-policies defining how control of the system should switch between policies identified by a standard DPS approach (but trained on different SOWs). We outline the general method and illustrate it using a case study of optimal dike heightening extending the work of Garner and Keller (2018). The meta-policies identified by the adaptive algorithm show Pareto-dominance in two objectives over the standard DPS, with an overall 68% improvement in hypervolume. We also see the improved performance over three grouped SOWs based on future extreme water levels, with the hypervolume improvements of 90%, 46%, and 35% for low, medium, and high water level SOWs respectively. Additionally, we evaluate the degree of improvement achieved by different ways of implementing the algorithm (i.e., different hyperparameter values). This provides guidance for decision makers with different degrees of risk aversion, and computational budgets. </p> <p>Due to simplifying assumptions and limitations of the adaptive DPS model used in the chapter, such as uniform levee design heights, the Surge and Waves Model for Protection Systems (SWaMPS) is presented as a more realistic application of the DPS framework. SWaMPS is a process-based model of surge-based flood risk. This chapter marks the first implementation of DPS using a realistic process-based risk model. The physical process of storm surge and rainfall is simulated independently over multiple reaches, and different frequencies are explored to manage the production system in SWaMPS. The performance of the DPS algorithm is evaluated versus a static intertemporal optimization.</p> <p>The computational burden of evaluating the large ensemble of SOWs to include possible future events in DPS motivates us to apply scenario reduction methods to select representative scenarios that more efficiently span an uncertain parameter space. This allows us to reduce the runtime of the optimization process. We explore a range of data-mining tools, including principal component analysis (PCA) and clustering to reduce the scenarios. We compare the computational efficiency and quality of policies to this optimization problem with reduced ensembles of SOWs.</p>

Substantial anthropogenic change of the Earth’s climate is modifying patterns of rainfall, river flow, glacial melt and groundwater recharge rates across the planet, undermining many of the stationarity assumptions upon which water resources infrastructure has been historically managed. This hydrological uncertainty is creating a potentially vast range of possible futures that could threaten the dependability of vital regional water supplies. This, combined with increased urbanisation and rapidly growing regional populations, is putting pressures on finite water resources. One of the greatest international challenges facing decision makers in the water industry is the increasing influences of these “deep” climate change and population growth uncertainties affecting the long-term balance of supply and demand and necessitating the need for adaptive action. Water companies and utilities worldwide are now under pressure to modernise their management frameworks and approaches to decision making in order to identify more sustainable and cost-effective water management adaptations that are reliable in the face of uncertainty. The aim of this thesis is to compare and contrast a range of existing Decision Making Methods (DMMs) for possible application to Water Resources Management (WRM) problems, critically analyse on real-life case studies their suitability for handling uncertainties relating to climate change and population growth and then use the knowledge generated this way to develop a new, resilience-based WRM planning methodology. This involves a critical evaluation of the advantages and disadvantages of a range of methods and metrics developed to improve on current engineering practice, to ultimately compile a list of suitable recommendations for a future framework for WRM adaptation planning under deep uncertainty. This thesis contributes to the growing vital research and literature in this area in several distinct ways. Firstly, it qualitatively reviews a range of DMMs for potential application to WRM adaptation problems using a set of developed criteria. Secondly, it quantitatively assesses two promising and contrasting DMMs on two suitable real-world case studies to compare highlighted aspects derived from the qualitative review and evaluate the adaptation outputs on a practical engineering level. Thirdly, it develops and reviews a range of new potential performance metrics that could be used to quantitatively define system resilience to help answer the water industries question of how best to build in more resilience in future water resource adaptation planning. This leads to the creation and testing of a novel resilience driven methodology for optimal water resource planning, combining optimal aspects derived from the quantitative case study work with the optimal metric derived from the resilience metric investigation. Ultimately, based on the results obtained, a list of suitable recommendations is compiled on how to improve the existing methodologies for future WRM planning under deep uncertainty. These recommendations include the incorporation of more complex simulation models into the planning process, utilisation of multi-objective optimisation algorithms, improved uncertainty characterisation and assessments, an explicit robustness examination and the incorporation of additional performance metrics to increase the clarity of the strategy assessment process.

No description available.

Battery electric trucks (BETs) offer environmental benefits in terms of reduced carbon emissions and enhanced energy efficiency but have been challenged with economic viability compared to conventional internal combustion engine trucks (ICETs) caused by substantial acquisition costs, limited charging infrastructure, and concerns regarding range and payload capacity.  Previous studies focus on TCO at the vehicle or policy level but overlook the system and firm-level impacts. Operational aspects like vehicle utilization, battery utilization, charging planning, and route optimization are often ignored, potentially underestimating electric freight cost-competitiveness.The research gap does not address the practical needs of fleet operators, especially in scenarios where charging infrastructure is lacking. There is therefore a need to consider the complex system level interactions, market dynamics, technology developments, and operational processes involved in freight shipping. By applying a decision-making under deep uncertainty (DMDU) framework, this study enables informed decisions in unpredictable scenarios, bridging the gap between strategic choices like battery capacity and operational optimization like route planning. This study identifies the most significant factors that affect the TCO of BET fleets and cost-competitiveness relative to ICET fleets, taking into account market-operational interfaces between unpredictable market dynamics and operational processes such as stochastic demand and feature selection from a strategic and operational perspective. 40 tonne truck-trailers for freight distribution networks with distances up to 250 km are considered in the study.  A TCO model of BET and ICET fleets was developed taking into account vehicle route optimization, vehicle selection, and vehicle utilization which was then programmatically iterated by sampling and simulating optimized vehicle routes for a total of 220 224 iterations. The parameter space was screened and reduced with Feature Scoring using Extra Trees approximation of 1st order Sobol Indices. The reduced parameter space was then sampled using Sobol sampling to conduct a Sobol Global Variance decomposition Analysis of TCO, TCO delta, and service level in order to identify the most significant factors affecting BET fleet TCO and cost-competitiveness.To identify cost-competitive scenarios, the Patient Rule Induction Method (PRIM) was used to identify parameter sub spaces to determine scenarios where BET fleets have a lower TCO than ICET fleets. Further visual analysis was done using linear and polynomial regression and kernel density estimation. The analysis shows that both TCO and cost-competitiveness of BETs are primarily affected by shipment demand, distance between distribution center and delivery sites, and battery size, and that a trade-off is made between cost-competitiveness and service level. The results show that cost-competitiveness of electric freight scales with demand, with larger fleets being better able to optimize routing and shipment allocation; balancing the shipment demand to minimize charging times that otherwise would make the fleet less competitive than their fossil-fuel counterparts. This, paired together with higher degrees of vehicle utilization and appropriate battery sizing, allow for electric freight to be cost-competitive even for long-haul distances up to 250 km.  Furthermore, optimization of the Electric Vehicle Routing Problem (E-VRP) with shifts and time windows is shown to have a highly significant effect when minimizing TCO on a fleet level, with the vast majority of optimal ICET routes not being optimal for BETs.The benefits of E-VRP optimization scales with demand and fleet size, indicating that large-scale electrification is required to make BETs cost-competitive.Electrification of road freight is therefore highly contingent on effective route planning and charging scheduling with E-VRP optimization in order to be cost-competitive, which has not been considered in previous literature. Thus previous literature have therefore likely underestimated the cost-competitiveness of electric freight, particularly at medium-long haul distances. / Battery electric trucks (BETs), även kända som batterielektriska lastbilar, erbjuder miljömässiga fördelar genom minskade koldioxidutsläpp och förbättrad energieffektivitet. Men de har utmanats när det kommer till ekonomisk konkurrenskraft jämfört med konventionella lastbilar med förbränningsmotor (ICETs) på grund av höga inköpskostnader, begränsad laddinfrastruktur och oro över räckvidd och lastkapacitet. Tidigare studier har fokuserat på TCO (totala ägandekostnader) på fordon- eller policynivå men har inte betraktat TCO på nätverksnivå och från det enskilda företagets perspektiv. Operativa aspekter som fordonssutnyttjande, batteriutnyttjande, laddningsplanering och ruttoptimisering ignoreras ofta, vilket potentiellt leder till en underskattning av elektrisk frakts kostnadskonkurrenskraft. Forskningsluckan tar inte upp de praktiska behoven hos fordonsflottoperatörer, särskilt i scenarier där laddinfrastrukturen är bristfällig. Det finns därför ett behov av att granska komplexa systemnivåinteraktioner, marknadens dynamik, teknikutveckling och operativa processer som är involverade i godstransport. Genom att tillämpa \textit{decision-making under deep uncertainty} (DMDU) möjliggör denna studie informerade beslut i scenarier präglade av osäkerhet och studerar interaktionseffecter mellan strategiska val som batterikapacitet och operativ optimering som t.ex.\ ruttplanering. Denna studie identifierar de mest betydande faktorer som påverkar TCO för BET-flottor och deras kostnadskonkurrenskraft jämfört med ICET-flottor, med beaktande av gränssnitten mellan marknadsdynamik och operativa processer såsom stokastisk efterfrågan och urval av funktioner ur såväl strategisk som operativ synvinkel. 40-ton lastbilssläp för nätverk med avstånd upp till 250 km beaktas inom omfånget för studien. En TCO-modell för BET- och ICET-flottor utvecklades med hänsyn till ruttoptimering, fordonsval och fordonsutnyttjande, vilket sedan programmässigt itererades genom provtagning och simulering av optimerade fordonsrutter för sammanlagt 220 224 iterationer. Parameterrummet granskades och minskades med hjälp av funktionsskattning med hjälp av Extra Trees-approximation av Sobol-indices av första ordningen. Det reducerade parameterrummet provtogs sedan med Sobol-provtagningsmetod för att genomföra en global variansdekomponering av TCO, TCO-delta och servicenivå för att identifiera de mest betydande faktorerna som påverkar BET-flottans TCO och kostnadskonkurrenskraft. För att identifiera kostnadskonkurrenskraftiga scenarier användes Patient Rule Induction Method (PRIM) för att identifiera parametrarum som visar scenarier där BET-flottor har lägre TCO än ICET-flottor. Vidare utfördes visuell analys med linjär och polynomisk regression samt kärnskattning. Analysen visar at kostnadskonkurrenskraft för tunga elektriska fordon primärt påverkas av efterfrågan, köravstånder och batteristorlek, och att det görs en avvägning mellan kostnadskonkurrenskraft och servicenivå. Resultaten visar at kostnadskonkurrenskraft ökar i takt med efterfrågan, då större flottor kan mer fördelaktigt optimera rutter och allokering av leveranser till varje fordon genom att transportefterfrågan balanseras sådan att tiden för laddning minimeras, vilket hade annars gjort de elektriska flottorna mindre konkurrenskraftiga gentemot fossildrivna flottor av tunga fordon. Detta i samband med högre utnyttjandegrad av fordonen och val av rätt batteristorlek gjör att elektrisk godstransport kan vara kostnadskonkurrenskraftig även vid längre körsträckor upp till 250 km. Vidare visar ruttoptimering för BETs (E-VRP) sig vara av stor betydelse när det gäller att minimera TCO på flottnivå, medan majoriteten av optimala ICET-rutter inte är optimala för BETs.Fördelarna med E-VRP optimering skalar med ökande efterfrågan och flottstorlek, vilket tyder på att storskalig elektrifiering behövs för att göra BETs kostnadskonkurrenskraftigaElektrifiering av godstransport är därför starkt beroende av effektiv rutt- och laddningsplanering med E-VRP-optimering. Tidigare litteratur har sannolikt underskattat kostnadskonkurrenskraften för elektrisk godstransport, särskilt vid medellånga och långa transportavstånd.