Although there have been oscillations in airship interest since their use in the early 1900s, technological advancements and the need for more flexible and environmentally friendly transportation modes have caused a stream of study and surge in airship development in recent years. For companies and governments to understand how airships can be incorporated into their fleets to fulfil new or existing mission types, system design space exploration is an important step in understanding airships, their uses, and their design parameters. A decision support system (DSS), Design Exploration of Lighter-Than-Air Systems (DELTAS), was developed to help stakeholders with this task. DELTAS allows users to design airships and missions to determine how a design will perform in the scenario. Simulations can also be run for a given mission to find the Pareto-optimal designs for user-defined ranges of high-level airship design parameters. A case study is provided that demonstrates how DELTAS can be used to explore the airship design space for three specified missions. These three mission case studies show how design of experiments is important to more thoroughly cover the design space and to find and understand the relationships between airship design variables that lead to optimal mission times and costs. This research also explores the impacts of introducing an airship into operation. Engineered products have economic, environmental, and social impacts, which comprise the major dimensions of sustainability. This paper seeks to determine the interaction between design parameters when social impacts are incorporated into the concept development phase of the systems design process. Social impact evaluation is increasing in importance similar to what has happened in recent years with environmental impact consideration in the design of engineered products. Concurrently, research into new airship design has increased. Airships have yet to be reintroduced at a large scale or for a range of applications in society. Although airships have the potential for positive environmental and economic impacts, the social impacts are still rarely considered. This paper presents a case study of the hypothetical introduction of airships in the Amazon region of Brazil to help local farmers transport their produce to market. It explores the design space in terms of both engineering parameters and social impacts using a discrete-event simulation to model the system. The social impacts are found to be dependent not only on the social factors and airship design parameters, but also on the farmer-airship system, suggesting that socio-technical systems design will benefit from integrated social impact metric analysis. This thesis seeks to demonstrate how computer-aided engineering tools can be used to predict social impacts, to more effectively explore a system's design space, and to optimize the system design for maximum positive impact, using the modern airship as a case study.

Decision-making by one or more individuals to select a course of action is predicated on the values and preferences to identify, choose options, and finally select the option that is evaluated to be the “best option.” Decision theory provides the means to model and analyze both the processes and options available to the decision-makers. This dissertation assembled in three phases: 1) An effort to collect and review existing literature relating to the concept of expanding decision analysis options to provide a model of decision-making made with time-dependent factors along with uncertainty and risk. Further, adding the concept of a decision to update time-dependent decision data in a Bayesian fashion aids in modeling decision-making thought processes. This review included a total of 395 research artifacts. 2) Development of a technical approach using the information gathered in the literature review to guide planning for a decision-making simulation of individuals and organizations. The approach emphasizes creating a decision-making simulation framework with capabilities to model time-dependent factors, information processing and communication, and fuzzy-stochastic data. 3) Use of the technical approach to develop a simulation framework to simulate complex decision-making and work packages at multiple levels in an organization using time-dependent factors, information processing and communication, and fuzzy-stochastic data. Using a Discrete Event Simulation (DES) and Agent-Based Models (ABM) to simulate people and their interactions, this framework was then be used to simulate decision-making and work processes within an organization. Ultimately, the Agile Enterprise Simulation (AES) capability was created and demonstrated.

No description available.

No description available.

Forest fuel is an important source of energy with potential for increased use in the Nordic countries. Profitable forest operations are dependent on logistics as the forest is a natural resource with varying conditions over widespread areas. In comparison to saw log and pulp wood supply, forest fuel supply has to face challenges like low product value, seasonality and irregularity of customer demand, low bulk density and the need to process the biomass prior to delivery. In order to utilize both the economic potential and societal benefits from replacing fossil-based fuels with renewable forest fuels these challenges must be managed. This requires efficient long distance transports, with changes of transport mode typically required. This thesis examines preconditions necessary for logistic planning to achieve cost and energy efficient long distance transports within supply chains for forest fuel. The thesis has an explorative approach, including both qualitative and quantitative techniques. Paper I provides a review of the development within forestry logistics in Sweden and Finland since the beginning of the century, with focus on transports, terminal usage and storage related aspects. Paper II uses qualitative interviews to deepen the understanding of a specific forest fuel supply chain by focusing on communications and relations between the actors. Paper III is a study of  the influence of a terminal before maritime transport of wood chips using simulation.  Paper I shows that forestry logistics faces challenges, primarily related to increased variations in field conditions caused by climate change. The importance of terminal storage for the supply of forest products will likely increase as an effect. Also the entrepreneurs interviewed in Paper II stressed the benefits of terminals in the supply chain, e.g. enhanced planning opportunities and a more even workflow over the year. Paper III puts cost for extra terminal handling in relation to storage costs in a port and demonstrates the importance of including variation in logistic planning. The terminal flows were not profitable in the case of a small port. In order to make a well-founded decision gains in other parts of the supply chain must also be considered, even when they are harder to quantify. This deserves attention in future research. / Skogsbränsle är en viktig energikälla i de nordiska länderna och det finns potential att öka användningen. Ett lönsamt skogsbruk är beroende av god logistik då skogen är en naturresurs som är spridd över stora arealer och där förutsättningarna varierar i landet. Utöver de utmaningar som påverkar försörjningskedjorna för timmer och massaved måste skogsbränslesektorn förhålla sig till ett lågt produktvärde, stor säsongsvariation på efterfrågesidan, låg bulkdensitet och behov av sönderdelning inför transport. För att realisera de samhällsnyttor och den ekonomiska potential som finns i att ersätta fossila bränslen med förnybara måste dessa utmaningar hanteras. Det förutsätter effektiva transporter på längre avstånd, vilket normalt kräver byte av transportslag.  Avhandlingen studerar vilka förutsättningar som är viktiga för att skapa kostnads- och energieffektiva transporter över långa avstånd, avseende skogsbränsle. Den har ett explorativt angreppssätt och inkluderar både kvalitativa och kvantitativa metoder. Artikel I beskriver utvecklingen inom skoglig logistik i Sverige och Finland sedan år 2000. Bland fokusområdena finns transporter, terminalanvändning och aspekter kring lagring. Artikel II syftar till att fördjupa förståelsen kring en specifik försörjningskedja för skogsbränslen genom en intervjustudie. I fokus är kommunikation och relationer mellan aktörerna i kedjan. I artikel III används en simuleringsmodell för att undersöka hur införandet av en terminal påverkar logistiken vid fartygsleveranser av flisat skogsbränsle. Artikel I visar att den skogliga logistiken står inför utmaningar relaterade till ökande variationer i väderförhållanden som en följd av klimatförändringar. Som en konsekvens kommer möjligheten att lagra material på terminaler sannolikt öka i betydelse. Också i Artikel II lyfts vikten av terminallagring av de intervjuade entreprenörerna, som nämner ökade planeringsmöjligheter och ett jämnare arbetsflöde över året som fördelar med terminaler. Artikel III jämför kostnader för terminalhantering med kostnaderna för lagring i hamn och demonstrerar vikten av att inkludera variation i logistikplaneringen. Terminalflödena var inte lönsamma i fallet med en mindre hamn, men övriga studier tydliggör vikten av att inkludera effekter för hela försörjningskedjan, också när dessa är svåra att kvantifiera. Detta område förtjänar uppmärksamhet i framtida forskning.

Data-intensive scientific applications often process an enormous amount of data. The scalability of such applications depends critically on how to manage the locality of data. Our study explores two common types of applications that are vastly different in terms of memory access pattern and workload variation. One includes those with multi-stride accesses in regular nested parallel loops. The other is for processing large-scale irregular social network graphs. In the former case, the memory location or the data item accessed in a loop is predictable and the load on processing a unit work (an array element) is relatively uniform with no significant variation. On the other hand, in the latter case, the data access per unit work (a vertex) is highly irregular in terms of the number of accesses and the locations being accessed. This property is further tied to the load and presents significant challenges in the scalability of the application performance. Designing platforms to support extreme performance scaling requires understanding of how application specific information can be used to control the locality and improve the performance. Such insights are necessary to determine which control and which abstraction to provide for interfacing an underlying system and an application as well as for designing a new system. Our goal is to expose common requirements of data-intensive scientific applications for scalability. For the former type of applications, those with regular accesses and uniform workload, we contribute new methods to improve the temporal locality of software-managed local memories, and optimize the critical path of scheduling data transfers for multi-dimensional arrays in nested loops. In particular, we provide a runtime framework allowing transparent optimization by source-to-source compilers or automatic fine tuning by programmers. Finally, we demonstrate the effectiveness of the approach by comparing against a state-of-the-art language-based framework. For the latter type, those with irregular accesses and non-uniform workload, we analyze how the heavy-tailed property of input graphs limits the scalability of the application. Then, we introduce an application-specific workload model as well as a decomposition method that allows us to optimize locality with the custom load balancing constraints of the application. Finally, we demonstrate unprecedented strong scaling of a contagion simulation on two state-of-the-art high performance computing platforms. / Ph. D.

The emission-heavy open-pit mining industry is exploring a new emission-free era with electrified autonomous haulers. The new technology has challenges, such as designing charging infrastructure for complex environments. This thesis addresses these challenges with a simulation model. This thesis aims to create a foundation and enhance decision-making for designing and evaluating charging infrastructure for Volvo Autonomous Solutions. The paper investigates various charging infrastructure designs using a simulation model verified by Volvo Autonomous Solutions employees, prioritizing minimizing the total cost of ownership and simultaneously meeting the production goal. The project consists of phases such as Needfinding and Research, Modeling, Simulation, and Validation, each aligned with specific activities inspired by Design Thinking principles. The simulation model is developed iterative with a specific site as the case. Several meetings were conducted with the co-advisor and supervisor to validate the simulation model. The thesis has developed a simulation model for charging infrastructures specifically designed for open-pit quarries. The simulation model was developed to make changes to the system easily. Adaptiveness is needed for the constantly changing environment or testing a new open-pit quarry. How to design charging infrastructure for open-pit quarries was found to be a research gap, and there is also an industrial need for guidance on how it should be designed. The scientific contribution concludes that important factors are important in designing charging infrastructure. Whereas the industrial contribution concludes, the simulation model is configured. / Den utsläppstunga dagbrottsindustrin utforskar en ny utsläppsfri era med elektrifierade autonoma dumprar. Den nya tekniken har utmaningar, som att designa laddinfrastruktur för komplexa miljöer. Denna avhandling tar uppdessa utmaningar med en simuleringsmodell. Examensarbetet syftar till att skapa en grund och förbättra beslutsfattandet för design och utvärdering av laddinfrastruktur för Volvo Autonomous Solutions. Uppsatsen undersöker olika designer av laddinfrastruktur med hjälp av en simuleringsmodell som verifierats av anställda på Volvo Autonomous Solutions, med prioritet att minimera den totala ägandekostnaden och samtidigt uppfylla produktionsmålet. Projektet bestod av faser som Needfinding och Research, Modellering, Simulering och Validering, var och en anpassad till specifika aktiviteter inspirerade av Design Thinking principer. Simuleringsmodellen utvecklades iterativt med ett specifik dagbrott som fall. Flera möten genomfördes med företagshandledare och handledare för att validera simuleringsmodellen. Avhandlingen har utvecklat en simuleringsmodell för laddningsinfrastruktur speciellt utformad för dagbrott. Simuleringsmodellen utvecklades för att enkelt kunna göra ändringar i systemet. Anpassningsförmåga behövs för den ständigt föränderliga miljön eller för att testa ett nytt dagbrott. Hur man utformar laddinfrastruktur för dagbrott visade sig vara ett forskningsgap och det finns också ett industriellt behov av vägledning om hur den ska utformas. Det vetenskapliga bidraget drar slutsatser om viktiga faktorer vid utformningen av laddinfrastruktur. Medan det industriella bidraget drar slutsatser om konfigurationen av simuleringsmodellen.

This research investigates and develops a new approach to the management of service quality with the emphasis on patient and staff satisfaction in the healthcare sector. The challenge of measuring the quality of service in healthcare requires us to view the problem from multiple perspectives. At the philosophical level, the true nature of quality is still debated; at the psychological level, an accurate conceptual representation is problematic; whilst at the physical level, an accurate measurement of the concept still remains elusive to practitioners and academics. This research focuses on the problem of quality measurement in the healthcare sector. The contributions of this research are fourfold: Firstly, it argues that from the technological point of view the research to date into quality of service in healthcare has not considered methods of real-time measurement and monitoring. This research identifies the key elements that are necessary for developing a real-time quality monitoring system for the healthcare environment.Secondly, a unique index is proposed for the monitoring and improvement of healthcare performance using information-theoretic entropy formalism. The index is formulated based on five key performance indicators and was tested as a Healthcare Quality Index (HQI) based on three key quality indicators of dignity, confidence and communication in an Accident and Emergency department. Thirdly, using an M/G/1 queuing model and its underlying Little’s Law, the concept of Effective Satisfaction in healthcare has been proposed. The concept is based on a Staff-Patient Satisfaction Relation Model (S-PSRM) developed using a patient satisfaction model and an empirically tested model developed for measuring staff satisfaction with workload (service time). The argument is presented that a synergy between patient satisfaction and staff satisfaction is the key to sustainable improvement in healthcare quality. The final contribution is the proposal of a Discrete Event Simulation (DES) modelling platform as a descriptive model that captures the random and stochastic nature of healthcare service provision process to prove the applicability of the proposed quality measurement models.

Approved for public release, distribution is unlimited / This thesis is concerned with the operational benefit of the Integrated Mechanical Diagnostics Health and Usage Management Systems (IMD HUMS) rotor track and balance (RTB) functionality. The questions addressed are whether there is a savings in flight hours expended on functional check flights (FCF's) when compared to present practices, if there will there be a reduction in directed maintenance man-hours (DMMH) spent on maintenance related to the rotor system, and the impact on Operational Availability. Experiments were conducted using a discrete event simulation model of squadron flight operations and organizational level maintenance. The simulation is generic and can be used in the analysis of other helicopters. Input parameters governing the distributions of maintenance action inter-arrival times were estimated from Naval Aviation Logistics Data Analysis (NALDA) databases and squadron experiences on such systems. The analysis suggests that flight hours spent in FCF are dependent upon vibration growth rate, an unknown quantity, and the maintenance policy for rotor smoothing. Directed maintenance man-hours decrease with increasing numbers of IMD HUMS configured aircraft and further gains are achieved with a maintenance policy suited to a continuous monitoring system. / Captain, United States Marine Corps

Le développement de nouveaux produits constitue une priorité stratégique de l'industrie pharmaceutique, en raison de la présence d'incertitudes, de la lourdeur des investissements mis en jeu, de l'interdépendance entre projets, de la disponibilité limitée des ressources, du nombre très élevé de décisions impliquées dû à la longueur des processus (de l'ordre d'une dizaine d'années) et de la nature combinatoire du problème. Formellement, le problème se pose ainsi : sélectionner des projets de Ret D parmi des projets candidats pour satisfaire plusieurs critères (rentabilité économique, temps de mise sur le marché) tout en considérant leur nature incertaine. Plus précisément, les points clés récurrents sont relatifs à la détermination des projets à développer une fois que les molécules cibles sont identifiées, leur ordre de traitement et le niveau de ressources à affecter. Dans ce contexte, une approche basée sur le couplage entre un simulateur à événements discrets stochastique (approche Monte Carlo) pour représenter la dynamique du système et un algorithme d'optimisation multicritère (de type NSGA II) pour choisir les produits est proposée. Un modèle par objets développé précédemment pour la conception et l'ordonnancement d'ateliers discontinus, de réutilisation aisée tant par les aspects de structure que de logique de fonctionnement, a été étendu pour intégrer le cas de la gestion de nouveaux produits. Deux cas d'étude illustrent et valident l'approche. Les résultats de simulation ont mis en évidence l'intérêt de trois critères d'évaluation de performance pour l'aide à la décision : le bénéfice actualisé d'une séquence, le risque associé et le temps de mise sur le marché. Ils ont été utilisés dans la formulation multiobjectif du problème d'optimisation. Dans ce contexte, des algorithmes génétiques sont particulièrement intéressants en raison de leur capacité à conduire directement au front de Pareto et à traiter l'aspect combinatoire. La variante NSGA II a été adaptée au problème pour prendre en compte à la fois le nombre et l'ordre de lancement des produits dans une séquence. A partir d'une analyse bicritère réalisée pour un cas d'étude représentatif sur différentes paires de critères pour l'optimisation bi- et tri-critère, la stratégie d'optimisation s'avère efficace et particulièrement élitiste pour détecter les séquences à considérer par le décideur. Seules quelques séquences sont détectées. Parmi elles, les portefeuilles à nombre élevé de produits provoquent des attentes et des retards au lancement ; ils sont éliminés par la stratégie d'optimistaion bicritère. Les petits portefeuilles qui réduisent les files d'attente et le temps de lancement sont ainsi préférés. Le temps se révèle un critère important à optimiser simultanément, mettant en évidence tout l'intérêt d'une optimisation tricritère. Enfin, l'ordre de lancement des produits est une variable majeure comme pour les problèmes d'ordonnancement d'atelier. / New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline, namely, the presence of uncertainty, the high level of the involved capital costs, the interdependency between projects, the limited availability of resources, the overwhelming number of decisions due to the length of the time horizon (about 10 years) and the combinatorial nature of a portfolio. Formally, the NPD problem can be stated as follows: select a set of R and D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while copying with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGA II type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. An object-oriented model previously developed for batch plant scheduling and design is then extended to embed the case of new product management, which is particularly adequate for reuse of both structure and logic. Two case studies illustrate and validate the approach. From this simulation study, three performance evaluation criteria must be considered for decision making: the Net Present Value (NPV) of a sequence, its associated risk defined as the number of positive occurrences of NPV among the samples and the time to market. Theyv have been used in the multiobjective optimization formulation of the problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. NSGA II has been adapted to the treated case for taking into account both the number of products in a sequence and the drug release order. From an analysis performed for a representative case study on the different pairs of criteria both for the bi- and tricriteria optimization, the optimization strategy turns out to be efficient and particularly elitist to detect the sequences which can be considered by the decision makers. Only a few sequences are detected. Among theses sequences, large portfolios cause resource queues and delays time to launch and are eliminated by the bicriteria optimization strategy. Small portfolio reduces queuing and time to launch appear as good candidates. The optimization strategy is interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems.