A multi-objective evolutionary approach to simulation-based optimisation of real-world problems.

This thesis presents a novel evolutionary optimisation algorithm that can improve the quality of solutions in simulation-based optimisation. Simulation-based optimisation is the process of finding optimal parameter settings without explicitly examining each possible configuration of settings. An optimisation algorithm generates potential configurations and sends these to the simulation, which acts as an evaluation function. The evaluation results are used to refine the optimisation such that it eventually returns a high-quality solution. The algorithm described in this thesis integrates multi-objective optimisation, parallelism, surrogate usage, and noise handling in a unique way for dealing with simulation-based optimisation problems incurred by these characteristics. In order to handle multiple, conflicting optimisation objectives, the algorithm uses a Pareto approach in which the set of best trade-off solutions is searched for and presented to the user. The algorithm supports a high degree of parallelism by adopting an asynchronous master-slave parallelisation model in combination with an incremental population refinement strategy. A surrogate evaluation function is adopted in the algorithm to quickly identify promising candidate solutions and filter out poor ones. A novel technique based on inheritance is used to compensate for the uncertainties associated with the approximative surrogate evaluations. Furthermore, a novel technique for multi-objective problems that effectively reduces noise by adopting a dynamic procedure in resampling solutions is used to tackle the problem of real-world unpredictability (noise). The proposed algorithm is evaluated on benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of a production cell at Volvo Aero, while the second one concerns the optimisation of a camshaft machining line at Volvo Cars Engine. The results from the optimisations show that the algorithm finds better solutions for all the problems considered than existing, similar algorithms. The new techniques for dealing with surrogate imprecision and noise used in the algorithm are identified as key reasons for the good performance.University of Skövde Knowledge Foundation Swede

Investigation of the Multiple Method Adaptive Control (MMAC) method for flight control systems

The stochastic adaptive control of the NASA F-8C digital-fly-by-wire aircraft using the multiple model adaptive control (MMAC) method is presented. The selection of the performance criteria for the lateral and the longitudinal dynamics, the design of the Kalman filters for different operating conditions, the identification algorithm associated with the MMAC method, the control system design, and simulation results obtained using the real time simulator of the F-8 aircraft at the NASA Langley Research Center are discussed

Advances and open problems on the control of large scale systems

Bibliography: leaves 10-12.ONR Contract N00014-76-C-0345 and ERDA Contract E-(49-18)-2087.by Michael Athans

A Robust Reactive Scheduling System with Application to Parallel Machine Scheduling

In this turbulent world, scheduling role has become crucial in most manufacturing production, and service systems. It allows the allocation of limited resources to activities with the objective of optimizing one performance measure or more. Resources may be machines in a factory, operating rooms in a hospital, or employees in a company, while activities can be jobs in a manufacturing plant, surgeries in a hospital, or paper work in a company. The goal of each schedule is to optimize some performance measures, which could be the minimization of the schedule makespan, the jobs\u27 completion times, jobs\u27 earliness and tardiness, among others. Until very recently, research has concentrated on scenarios that assume a predefined schedule that is failure free. Initial schedules produced in advance are being followed hoping no delays will occur, because once they do, the whole schedule may be compromised as it is not designed to adapt to change. Researchers focused on the generation of good schedules in the presence of complex constraints while assuming fixed processing times, known job arrival times, unbreakable machines, and immune employees. However, this is not the case in the real world, where processing times are stochastic, job arrival times could be unknown, machines do break down, and employees get sick. In fact, most environments including manufacturing are dynamic by nature and not static, vulnerable to many unpredictable events, which leads the initial schedule to become obsolete once it is executed. The reason these deterministic schedules fail is because they do not account for variability, scheduling the activities directly after each other, so when a certain activity is delayed, all its successors will be delayed too. In this dissertation, new repair and rescheduling algorithms, and robust systems equipped with learning capability are developed for the unrelated parallel machine environment, a known NP-hard problem. The introduced rules and algorithms were subjected to different stochastic rates of breakdowns and delays and were judged based on several performance measures to ensure the optimization of both the schedule quality and stability. Schedule quality is assessed based on the schedule Makespan (time to finish all jobs) and CPU, while schedule stability is based on the number of shifted jobs from one machine to another and the time to match up with the original schedule after the occurrence of a breakdown. The extensive computational tests and analyses show the superiority of the proposed algorithms and systems compared to existing methods in the literature, especially when implemented with the learning capability. Moreover, the rules were ranked based on their performance for different performance measure combinations, allowing the decision maker to easily determine the most appropriate repair/rescheduling rule depending on the performance measure(s) desired

A Smart Grid Approach to Sustainable Power System Integration

Many factors can be identified for faster incorporation of renewable energy resources to displace the traditional fossil fuel energy sources. These factors are divided into three different aspects. First is the rapid decline of the cost of renewable energy sources and their associated components. The second factor can be attributed to the increasing pressure to transition from fossil-fuel energy sources which have detrimental environmental effects towards more sustainable energy source. A third aspect can be introduced in countries which are blessed with an enormous amount of fossil fuel resources, where the preservation of these limited natural resources is of paramount importance to the country that holds it. The dissertation includes the Kingdom of Saudi Arabia as the primary case study. However, the algorithm developed is applicable for other geographical locations which share similarities to the kingdom. The kingdom is considered to be one of the countries with an abundance of fossil-fuel reserves. The unique features of Saudi Arabia are primarily the availability of solar radiation and wind speed as well as high percentage of electrical loads which can be controlled such as energy-intensive desalination plants. This feature, in particular, provides a significant driver for renewables to penetrate the electricity generation mixture. With loads that are deferrable, the issue of renewable sources variability can be mitigated and reduced with an optimized operation strategy. Therefore, the research tends to define and model electrical loads by how susceptible they are to the time of service. The types of loads considered are summarized as non-deferrable such as typical electrical loads in which the demand must be satisfied instantly, semi-deferrable loads which they share the same features as the non-deferrable, however, a storage medium is available to store energy products for later usage. This category of loads is represented by a water desalination plant with a water tank storage. The final load model is the fully deferrable load which is flexible in regarding time of service, and this type of load can be represented by an industrial production factory, such as a steel or aluminum plants. The concept of value storage is introduced, where energy can be stored in different forms which are quite different from a typical storage component (i.e., batteries). The justification to start increasing the penetration of renewable sources into the existing grid in countries which have abundant fossil fuel might not be evident. However, the dissertation provides both economical as well as environmental justifications and incentives to approach more sustainable energy sources. The economical and technical evaluation is referred to as the Generation Expansion Planning (GEP). This type of problem is associated with high complexity and non-linearity. Therefore, computational intelligence based optimization methods are used to resolve these issues. Heuristic optimization methodologies are utilized to solve the developed problem which provides a fixable approach to solve optimization problems

Agent-Driven Representations, Algorithms, and Metrics for Automated Organizational Design.

As cooperative multiagent systems (MASs) increase in interconnectivity, complexity, size, and longevity, coordinating the agents' reasoning and behaviors becomes increasingly difficult. One approach to address these issues is to use insights from human organizations to design structures within which the agents can more efficiently reason and interact. Generally speaking, an organization influences each agent such that, by following its respective influences, an agent can make globally-useful local decisions without having to explicitly reason about the complete joint coordination problem. For example, an organizational influence might constrain and/or inform which actions an agent performs. If these influences are well-constructed to be cohesive and correlated across the agents, then each agent is influenced into reasoning about and performing only the actions that are appropriate for its (organizationally-designated) portion of the joint coordination problem. In this dissertation, I develop an agent-driven approach to organizations, wherein the foundation for representing and reasoning about an organization stems from the needs of the agents in the MAS. I create an organizational specification language to express the possible ways in which an organization could influence the agents' decision making processes, and leverage details from those decision processes to establish quantitative, principled metrics for organizational performance based on the expected impact that an organization will have on the agents' reasoning and behaviors. Building upon my agent-driven organizational representations, I identify a strategy for automating the organizational design process~(ODP), wherein my ODP computes a quantitative description of organizational patterns and then searches through those possible patterns to identify an (approximately) optimal set of organizational influences for the MAS. Evaluating my ODP reveals that it can create organizations that both influence the MAS into effective patterns of joint policies and also streamline the agents' decision making in a coordinate manner. Finally, I use my agent-driven approach to identify characteristics of effective abstractions over organizational influences and a heuristic strategy for converging on a good abstraction.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113616/1/jsleight_1.pd

Advances in Spatial Theory and Dynamics

This book originates from two meetings, set apart in time but closely connected by continuing collaborative efforts between researchers in an international network. The first of these meetings took place at IIASA in October 1984, organized by IIASA's Regional Issues Project under the title "Dynamic Analysis of Spatial Development". About half of the papers in this volume were presented at that meeting. These contributions have been elaborated and revised considerably during the preparation of this volume, and can now be regarded as mature papers embracing the frontiers of spatial and economic dynamics. Another set of contributions was presented during the European Summer Institute in Regional Science held at the University of Umea in June 1986. The Summer Institute was organized by CERUM in collaboration with the Departments of Economics and Geography at the same university. The contributions have been drawn from the sessions on technological change, nonlinear dynamics in spatial networks and infrastructure development. This is reflected in the three parts of the volume (1) Competition, specialization and technological change, (2) Spatial interaction, (3) Urban and regional infrastructure

Periphery and Small Ones Matter

This open access book analyzes the dualism and inequality insofar as how it is manifested in interregional disparity and small enterprises. Using the case of Indonesia, the author considers how the general direction of policy should be to mitigate the effects of agglomeration forces leading towards concentration, and exploit the same forces by encouraging small businesses to operate in a cluster for collective action. The book addresses these issues by focusing on the role of interactions between policies and institutions, of which social capital is an important part. This is an open access book

Effects of circadian rhythm phase alteration on physiological and psychological variables: Implications to pilot performance (including a partially annotated bibliography)

The effects of environmental synchronizers upon circadian rhythmic stability in man and the deleterious alterations in performance and which result from changes in this stability are points of interest in a review of selected literature published between 1972 and 1980. A total of 2,084 references relevant to pilot performance and circadian phase alteration are cited and arranged in the following categories: (1) human performance, with focus on the effects of sleep loss or disturbance and fatigue; (2) phase shift in which ground based light/dark alteration and transmeridian flight studies are discussed; (3) shiftwork; (4)internal desynchronization which includes the effect of evironmental factors on rhythmic stability, and of rhythm disturbances on sleep and psychopathology; (5) chronotherapy, the application of methods to ameliorate desynchronization symptomatology; and (6) biorythm theory, in which the birthdate based biorythm method for predicting aircraft accident susceptability is critically analyzed. Annotations are provided for most citations