Intelligent Operation System for the Autonomous Vehicle Fleet

Modular vehicles are vehicles with interchangeable substantial components also known as modules. Fleet modularity provides extra operational flexibility through on-field actions, in terms of vehicle assembly, disassembly, and reconfiguration (ADR). The ease of assembly and disassembly of modular vehicles enables them to achieve real-time fleet reconfiguration, which is proven as beneficial in promoting fleet adaptability and in saving ownership costs. The objective of military fleet operation is to satisfy uncertain demands on time while providing vehicle maintenance. To quantify the benefits and burdens from modularity in military operation, a decision support system is required to yield autonomously operation strategies for comparing the (near) optimal fleet performance for different vehicle architectures under diverse scenarios. The problem is challenging because: 1) fleet operation strategies are numerous, especially when modularity is considered; 2) operation actions are time-delayed and time-varying; 3) vehicle damages and demands are highly uncertain; 4) available capacity for ADR actions and vehicle repair is constrained. Finally, to explore advanced tactics enabled by fleet modularity, the competition between human-like and adversarial forces is required, where each force is capable to autonomously perceive and analyze field information, learn enemy's behavior, forecast enemy's actions, and prepare an operation plan accordingly. Currently, methodologies developed specifically for fleet competition are only valid for single type of resources and simple operation rules, which are impossible to implement in modular fleet operation. This dissertation focuses on a new general methodology to yield decisions in operating a fleet of autonomous military vehicles/robots in both conventional and modular architectures. First, a stochastic state space model is created to represent the changes in fleet dynamics caused by operation actions. Then, a stochastic model predictive control is customized to manage the system dynamics, which is capable of real-time decision making. Including modularity increases the complexity of fleet operation problem, a novel intelligent agent based model is proposed to ensure the computational efficiency and also imitate the collaborative decisions making process of human-like commanders. Operation decisions are distributed to several agents with distinct responsibility. Agents are designed in a specific way to collaboratively make and adjust decisions through selectively sharing information, reasoning the causality between events, and learning the other's behavior, which are achieved by real-time optimization and artificial intelligence techniques. To evaluate the impacts from fleet modularity, three operation problems are formulated: (i) simplified logistic mission scenario: operate a fleet to guarantee the readiness of vehicles at battlefields considering the stochasticity in inventory stocks and mission requirements; (ii) tactical mission scenario: deliver resources to battlefields with stochastic requirements of vehicle repairs and maintenance; (iii) attacker-defender game: satisfy the mission requirements with minimized losses caused by uncertain assaults from an enemy. The model is also implemented for a civilian application, namely the real-time management of reconfigurable manufacturing systems (RMSs). As the number of RMS configurations increases exponentially with the size of the line and demand changes frequently, two challenges emerge: how to efficiently select the optimal configuration given limited resources, and how to allocate resources among lines. According to the ideas in modular fleet operation, a new mathematical approach is presented for distributing the stochastic demands and exchanging machines or modules among lines (which are groups of machines) as a bidding process, and for adaptively configuring these lines and machines for the resulting shared demand under a limited inventory of configurable components.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147588/1/lixingyu_2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147588/2/lixingyu_1.pd

Integration of software reliability into systems reliability optimization

Reliability optimization originally developed for hardware systems is extended to incorporate software into an integrated system reliability optimization. This hardware-software reliability optimization problem is formulated into a mixed-integer programming problem. The integer variables are the number of redundancies, while the real variables are the components reliabilities;To search a common framework under which hardware systems and software systems can be combined, a review and classification of existing software reliability models is conducted. A software redundancy model with common-cause failure is developed to represent the objective function. This model includes hardware redundancy with independent failure as a special case. A software reliability-cost function is then derived based on a binomial-type software reliability model to represent the constraint function;Two techniques, the combination of heuristic redundancy method with sequential search method, and the Lagrange multiplier method with the branch-and-bound method, are proposed to solve this mixed-integer reliability optimization problem. The relative merits of four major heuristic redundancy methods and two sequential search methods are investigated through a simulation study. The results indicate that the sequential search method is a dominating factor of the combination method. Comparison of the two proposed mixed-integer programming techniques is also studied by solving two numerical problems, a series system with linear constraints and a bridge system with nonlinear constraints. The Lagrange multiplier method with the branch-and-bound method has been shown to be superior to all other existing methods in obtaining the optimal solution;Finally an illustration is performed for integrating software reliability model into systems reliability optimization

Integration of maintenance optimization in process design and operation under uncertainty.

Imperial Users onl

A review of asset management literature on multi-asset systems

This article gives an overview of the literature on asset management for multi-unit systems with an emphasis on two multi-asset categories: fleet (a system of homogeneous assets) and portfolio (a system of heterogeneous assets). As asset systems become more complicated, researchers have employed different terms to refer to their specific problems. With an objective to facilitate readers in searching conducive studies to their interests, this paper establishes a novel classification scheme for multi-unit systems in accordance with essential features such as diversity of assets and intervention options. Moreover, discerning differences in characteristics between cross-component and cross-asset interactions, we select three types of potential multi-component dependencies (performance, stochastic, and resource) and extend their notions to be applicable to multi-asset systems. The investigation into these dependencies enables the identification of problems that could exist in real industrial settings but are yet to be determined in academia. Ultimately, we delve into modelling approaches adopted by previous researchers. This comprehensive information allows us to offer the insights into the current trends in multi-asset maintenance. We expect that the output of this review paper will not only stress research gaps on multi-asset systems, but more importantly help systematise future studies on this aspect

Adaptation pathways to reconcile hydropower generation and aquatic ecosystems restoration

The growing demands for water, food and energy, in addition to the need to protect ecosystems, pose significant challenges to water management and the operation of water systems. In hydropower-dominated basins, where reservoirs capture flow variability for energy generation, the modification of the natural flow regime disrupts the natural equilibrium of aquatic ecosystems. Migratory fish species and the associated ecosystem services are particularly vulnerable as the migration and recruitment success relies on the synchronization between the hydrologic flow regime and the reproductive cycle. While there is a consensus on the importance of restoring impacted ecosystems in balance with multiple uses, the current water governance framework lacks a comprehensive understanding of the tradeoffs involved and mechanisms for ensuring the equitable distribution of the adaptation costs among users. The present study brings a contribution to the field by proposing solutions to improve the water governance of river basins, combining the (1) identification of flow-ecological relationships by measuring the response of multiple options of flow regime restoration with a clear ecosystem indicator, (2) incorporation of the flow-ecological relationships and hydroclimatic conditions into the operation decisions of hydropower systems to create dynamic environmental flow solutions (termed Dynamic Adaptive Environmental flows – DAE-flows) with better long-term performance, (3) calculation of the reoperation trade-offs between alternative levels of environmental flow regime restoration and (4) development of mechanisms to share the adaptation costs among stakeholders. The electricity market is proposed as an institutional arrangement and financing mechanism to support the restoration of flow regimes in environmentally sensitive areas. The Upper Paraná River Basin, in Brazil, where consecutive hydropower impoundments have reduced the original floodplain along the last decades, is a recurrent example where reservoirs’ operation need to be reconciled with ecosystem functionality, which makes the basin an important study area. The findings of this dissertation indicate that it is possible to enhance the capacity of water systems to incorporate historically suppressed environmental water demands without imposing a hard constraint to economic uses. The consideration of the long-term effects of operation when designing operating strategies for multiple users leads to improved performance in both hydropower generation and meeting ecosystem demands. So, during severe droughts the water can still be reallocated to hydropower (as it is currently done) but at a lesser cost to the environment.As demandas crescentes por água, alimentos e energia, além da necessidade de proteger os ecossistemas, tornam a gestão dos recursos hídricos, bem como a operação de sistemas hídricos, uma tarefa desafiadora. Em bacias com aproveitamento hidrelétrico, a modificação do regime de vazões decorrente da operação dos reservatórios altera o equilíbrio natural dos ecossistemas aquáticos. Espécies migratórias de peixes e serviços ecossistêmicos associados ficam particularmente vulneráveis, uma vez que o sucesso da migração e recrutamento depende da sincronização entre o regime de vazão e o ciclo reprodutivo. Embora haja consenso sobre a importância de restaurar as demandas ecossistêmicas suprimidas e alcançar um equilíbrio que permita múltiplos usos, o atual quadro de governança carece de uma compreensão abrangente dos trade-offs envolvidos e dos mecanismos para garantir a distribuição equitativa dos custos de adaptação entre os usuários. O presente estudo contribui para o campo, propondo soluções para aprimorar a governança de bacias antropizadas, combinando (1) a identificação das relações vazão-ecológicas por meio da quantificação da resposta de múltiplas opções de restauração do regime de vazão por meio de um indicador de desempenho do ecossistema, (2) a incorporação dessas relações vazão-ecológicas juntamente com condições hidroclimáticas nas decisões operacionais de sistemas hidrelétricos (denominadas Vazões Ambientais Dinâmicas e Adaptativas - DAE-flows) para criar soluções dinâmicas de operação de reservatórios, (3) o cálculo dos trade-offs de reoperação de múltiplos níveis de restauração de regime de vazão ambiental e (4) o desenvolvimento de mecanismos para compartilhar os custos relacionados entre as partes interessadas. Nesse sentido, o mercado de eletricidade é proposto como arranjo institucional e mecanismo de financiamento para apoiar a restauração de regimes de vazão em áreas ambientalmente sensíveis. A Bacia Hidrográfica do Alto Paraná, Brasil, caracterizada como uma das mais represadas da América do Sul, com 65 usinas hidrelétricas integradas ao Sistema Integrado Nacional, é um exemplo recorrente da necessidade de reconciliação entre a geração de energia e a conservação de serviços ecossistêmicos, sendo utilizada como área de estudo. Os resultados indicam que podemos aumentar a capacidade dos sistemas hídricos para incorporar demandas ambientais historicamente suprimidas sem impor uma restrição rígida aos usos econômicos. Ao considerar os efeitos de longo prazo da operação ao projetar estratégias de operação para múltiplos usuários, obtemos um desempenho aprimorado tanto na geração de energia hidrelétrica quanto no atendimento às demandas do ecossistema. Assim, durante períodos de seca severa, a água ainda pode ser realocada para a produção de energia hidrelétrica (como é feito atualmente), porém com menor impacto ambiental

Models, Theoretical Properties, and Solution Approaches for Stochastic Programming with Endogenous Uncertainty

In a typical optimization problem, uncertainty does not depend on the decisions being made in the optimization routine. But, in many application areas, decisions affect underlying uncertainty (endogenous uncertainty), either altering the probability distributions or the timing at which the uncertainty is resolved. Stochastic programming is a widely used method in optimization under uncertainty. Though plenty of research exists on stochastic programming where decisions affect the timing at which uncertainty is resolved, much less work has been done on stochastic programming where decisions alter probability distributions of uncertain parameters. Therefore, we propose methodologies for the latter category of optimization under endogenous uncertainty and demonstrate their benefits in some application areas. First, we develop a data-driven stochastic program (integrates a supervised machine learning algorithm to estimate probability distributions of uncertain parameters) for a wildfire risk reduction problem, where resource allocation decisions probabilistically affect uncertain human behavior. The nonconvex model is linearized using a reformulation approach. To solve a realistic-sized problem, we introduce a simulation program to efficiently compute the recourse objective value for a large number of scenarios. We present managerial insights derived from the results obtained based on Santa Fe National Forest data. Second, we develop a data-driven stochastic program with both endogenous and exogenous uncertainties with an application to combined infrastructure protection and network design problem. In the proposed model, some first-stage decision variables affect probability distributions, whereas others do not. We propose an exact reformulation for linearizing the nonconvex model and provide a theoretical justification of it. We designed an accelerated L-shaped decomposition algorithm to solve the linearized model. Results obtained using transportation networks created based on the southeastern U.S. provide several key insights for practitioners in using this proposed methodology. Finally, we study submodular optimization under endogenous uncertainty with an application to complex system reliability. Specifically, we prove that our stochastic program\u27s reliability maximization objective function is submodular under some probability distributions commonly used in reliability literature. Utilizing the submodularity, we implement a continuous approximation algorithm capable of solving large-scale problems. We conduct a case study demonstrating the computational efficiency of the algorithm and providing insights

Contributions au développement de politiques de remplacement préventif pour des sytèmes multi-composants

Dans cette thèse, nous proposons de développer des politiques de remplacement préventif pour des systèmes multi-composants. Ces systèmes sont composés de plusieurs composants selon une configuration bien déterminée et dont l’état se dégrade d’une manière aléatoire. Les politiques de remplacement définissent les actions à entreprendre en fonction de l'état du système ou de ses composants et ont pour objectif de retarder l'apparition des pannes et de prolonger la durée de vie du système. Sur le plan théorique, la généralisation des modèles de remplacement des systèmes mono-composants à des systèmes multi-composants n'est pas évidente. La difficulté réside essentiellement dans l’existence d’interaction ou de dépendance entre les différents composants du système. Nous nous sommes concentrés dans cette thèse sur les dépendances stochastique et économique entre les composants. Pour la dépendance stochastique, la propagation de la panne a été modélisée par l’effet domino pour un système parallèle à deux composants. Nous avons proposé deux politiques de remplacement de type Age. Dans la première politique, nous avons supposé que la structure des coûts est constante alors que dans la deuxième politique cette hypothèse a été modifiée en prenant une structure de coûts variable. Nous avons aussi proposé dans le cadre de la dépendance stochastique un modèle de remplacement bi-objectif qui optimise à la fois le coût espéré du remplacement et la disponibilité du système. Pour la dépendance économique, nous avons proposé une politique de remplacement basée sur le comptage des pannes pour un système parallèle et nous l’avons intégrée dans un modèle d’allocation de la redondance d’un système série-parallèle. Le modèle mathématique a été résolu par une approche heuristique basée sur l’algorithme du recuit simulé.The aim of this thesis is to develop preventive replacement policies for multi-component systems. Systems are composed of several components connected under a known configuration and subject to random failures. Each replacement policy defines the actions to be taken according to the state of the system or its components and it is intended to delay the occurrence of failures and extend the lifetime of the system. From the theoretical point of view, the extension of replacement models from single-component systems to multi-component systems is not obvious. The difficulty is due primarily to the interaction or dependence between the different components of the system. In this thesis the focus has been put on the stochastic and economic dependencies between components. For stochastic dependence the propagation of the failure is modeled by the domino effect for a two-component parallel system, and two age replacement policies are investigated. In the first policy, we assumed that the cost structure is constant whereas in the second policy a variable cost structure is assumed. We proposed also a bi-objective replacement model that optimizes both expected replacement cost rate and system availability. For economic dependence, we proposed a failure counting replacement policy for a parallel system and we integrated it in a redundancy allocation model for a serie-parallel system. The mathematical model has been built taking account of this policy and Simulated Annealing algorithm has been used as resolution approach