AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Aplication of fractional algoritms in the control of an helicopter system

This paper compares the application of fractional and integer order controllers for a laboratory helicopter twin rotor MIMO system using the MatLab package.N/

DEVELOPMENT OF THE CROSS-COUPLING PHENOMENA OF MIMO FLIGHT SYSTEM USING FUZZY LOGIC CONTROLLER

This paper describes the performance of a simplified dynamic controller with fuzzy logic controllers. The six degree-of-freedom simulation study focuses on the results with and without fuzzy logic controller. One area of interest is the performance of a simulated the cross coupling effect. The controller uses explicit models to produce the desired commands. In this paper the effect of the cross-coupling between channels on the overall performance of the flight system has been considered. Two fuzzy controllers have been added to the system to improve its performance. This paper presents the development and simulation of a modified system is presented using MatLab Simulink. Also it focuses on the use of fuzzy logic controller in model-based control of multiple-input, multiple-output systems. Here, we address the question of how the overall performance of the system is affected when both fuzzy logic controllers are applied at the same time. Simulation and experimental results of a flight system , as an illustrative example, are presented

Supervisory-plus-regulatory control design for efficient operation of industrial furnaces

A two-level system engineering design approach to integrated control and supervision of industrial multi-zone furnaces has been elaborated and tested. The application case study is the three-zone 25 MW RZS furnace plant at Skopje Steelworks. The integrated control and supervision design is based on combined use of general predictive control optimization of set-points and steady-state decoupling,at the upper level, and classical two-term laws with stady-state decouling, at the executive control level. This design technique exploits the intrinsic stability of thermal processes and makes use of constrained optimization, standard non-parametric time-domain process models, identified under operating conditions, using truncated k-time sequence matrices, controlled autoregressive moving average models. Digital implementations are sought within standard computer process control platform for practical engineering and maintenance reasons

Methods of system identification, parameter estimation and optimisation applied to problems of modelling and control in engineering and physiology

Mathematical and computer-based models provide the foundation of most methods of engineering design. They are recognised as being especially important in the development of integrated dynamic systems, such as “control-configured” aircraft or in complex robotics applications. These models usually involve combinations of linear or nonlinear ordinary differential equations or difference equations, partial differential equations and algebraic equations. In some cases models may be based on differential algebraic equations. Dynamic models are also important in many other fields of research, including physiology where the highly integrated nature of biological control systems is starting to be more fully understood. Although many models may be developed using physical, chemical, or biological principles in the initial stages, the use of experimentation is important for checking the significance of underlying assumptions or simplifications and also for estimating appropriate sets of parameters. This experimental approach to modelling is also of central importance in establishing the suitability, or otherwise, of a given model for an intended application – the so-called “model validation” problem. System identification, which is the broad term used to describe the processes of experimental modelling, is generally considered to be a mature field and classical methods of identification involve linear discrete-time models within a stochastic framework. The aspects of the research described in this thesis that relate to applications of identification, parameter estimation and optimisation techniques for model development and model validation mainly involve nonlinear continuous time models Experimentally-based models of this kind have been used very successfully in the course of the research described in this thesis very in two areas of physiological research and in a number of different engineering applications. In terms of optimisation problems, the design, experimental tuning and performance evaluation of nonlinear control systems has much in common with the use of optimisation techniques within the model development process and it is therefore helpful to consider these two areas together. The work described in the thesis is strongly applications oriented. Many similarities have been found in applying modelling and control techniques to problems arising in fields that appear very different. For example, the areas of neurophysiology, respiratory gas exchange processes, electro-optic sensor systems, helicopter flight-control, hydro-electric power generation and surface ship or underwater vehicles appear to have little in common. However, closer examination shows that they have many similarities in terms of the types of problem that are presented, both in modelling and in system design. In addition to nonlinear behaviour; most models of these systems involve significant uncertainties or require important simplifications if the model is to be used in a real-time application such as automatic control. One recurring theme, that is important both in the modelling work described and for control applications, is the additional insight that can be gained through the dual use of time-domain and frequency-domain information. One example of this is the importance of coherence information in establishing the existence of linear or nonlinear relationships between variables and this has proved to be valuable in the experimental investigation of neuromuscular systems and in the identification of helicopter models from flight test data. Frequency-domain techniques have also proved useful for the reduction of high-order multi-input multi-output models. Another important theme that has appeared both within the modelling applications and in research on nonlinear control system design methods, relates to the problems of optimisation in cases where the associated response surface has many local optima. Finding the global optimum in practical applications presents major difficulties and much emphasis has been placed on evolutionary methods of optimisation (both genetic algorithms and genetic programming) in providing usable methods for optimisation in design and in complex nonlinear modelling applications that do not involve real-time problems. Another topic, considered both in the context of system modelling and control, is parameter sensitivity analysis and it has been found that insight gained from sensitivity information can be of value not only in the development of system models (e.g. through investigation of model robustness and the design of appropriate test inputs), but also in feedback system design and in controller tuning. A technique has been developed based on sensitivity analysis for the semi-automatic tuning of cascade and feedback controllers for multi-input multi-output feedback control systems. This tuning technique has been applied successfully to several problems. Inverse systems also receive significant attention in the thesis. These systems have provided a basis for theoretical research in the control systems field over the past two decades and some significant applications have been reported, despite the inherent difficulties in the mathematical methods needed for the nonlinear case. Inverse simulation methods, developed initially by others for use in handling-qualities studies for fixed-wing aircraft and helicopters, are shown in the thesis to provide some important potential benefits in control applications compared with classical methods of inversion. New developments in terms of methodology are presented in terms of a novel sensitivity based approach to inverse simulation that has advantages in terms of numerical accuracy and a new search-based optimisation technique based on the Nelder-Mead algorithm that can handle inverse simulation problems involving hard nonlinearities. Engineering applications of inverse simulation are presented, some of which involve helicopter flight control applications while others are concerned with feed-forward controllers for ship steering systems. The methods of search-based optimisation show some important advantages over conventional gradient-based methods, especially in cases where saturation and other nonlinearities are significant. The final discussion section takes the form of a critical evaluation of results obtained using the chosen methods of system identification, parameter estimation and optimisation for the modelling and control applications considered. Areas of success are highlighted and situations are identified where currently available techniques have important limitations. The benefits of an inter-disciplinary and applications-oriented approach to problems of modelling and control are also discussed and the value in terms of cross-fertilisation of ideas resulting from involvement in a wide range of applications is emphasised. Areas for further research are discussed

Developing a fuzzy-based decision-making procedure for traffic control in expressway congestion management

This paper presents part of a multi-stage fuzzy logic controller (MS-FLC) that is developed for traffic control in congestion management on expressways. The decision-making process of traffic control for expressway congestion management using the MS-FLC consists of three tasks: (1) evaluation of current traffic congestion; (2) prediction of traffic congestion tendency; and (3) recommendation of control strategies and control actions to alleviate the congestion. This paper presents the 3rd stage of the MS-FLC that develops a fuzzy-based decision-making procedure (FDMP) for management of recurring and non-recurring congestion. Using fuzzy rules, the FDMP evaluates the current and anticipated traffic data and incident information to recommend control strategies at the strategic level, and control actions at the operational level. Results from this research show that: (i) the FDMP offers a comprehensive procedure in deriving control strategies and actions; (ii) FDMP control actions are derived from a systematic decision-making logic where the design of control rules is consistently oriented toward achieving desirable control objectives; (iii) the FDMP targets a proper balance in congestion management between the mainline and the ramp using compromise rule design; (iv) the FDMP facilitates using various forms of available traffic and incident data on an extended expressway segment to derive at control actions, making the system-wide gains possible; and (v) the FDMP could be applied for management of both recurring and non-recurring congestion.</p

Methodological guide to deploy Functional Analysis into CODAC Systems for the Tritium Processing in ITER

The present document is focused on the a nalysis of the ITER - TBM‘s Proto - CODAC system. ITER is considered to be the first nuclear fusion reactor to be energetically feasible for a sustained period of time with a rated fusion power of 500 MW. ITER Project involves 35 countries with a total est imated budget of some 15.000 M€; being the first of its kind from the point of view of international collaboration, engineering and supply sources; where every country participate with the best of its possibilities. The hearth of the fusion reactor is a giant Tokamak (6.2 m plasma major radius) with a se ries of ancillary buildings and facilities that might complete the whole p roject. The operation of ITER is scheduled to operate along the next 50 years , after completion of the facilities construction and commissioning of the plant, considering first to b e operated in D - D and further in a D - T modes. In this sense, the activity that supports the development of the present work was stated to be necessary to consider a tritium balance for the self - sufficient reaction and operation of the whole. Tritium is a v ery scarce element being its global sto cks to the present date of 2016 of some 20 kg, being produced mainly collected from the operation of Candu reactors in Canada [Raeder, 1986] . Also the operation of the ITER reactor might produce Tritium at a rate that might b e able to support the fusion reaction indefinitely on a time basis. Because of the tritium balance it is difficult to state due to its highly permeation throughout confinement of first walls and joint materials . Not to mention its high ly dangerous potential to human health, according to radiologic al properties . This is why it is necessary to establish predictive tools that might indicate the concentration and inventory across the facility, including emissions to the environment. In this sense, ITER Instrumentation and Control systems for Control and Data Acquisition (DACS) mainly constitute the layers between the users (Control Room) and the field Instrumentation (sensors and actuators). This is nam ed as ITER CODAC, which is the primary global system analyzed in the present document. The control philosophy it is stated to be predictive and from the author‘s point of view must include the comparison between field measurement and advanced modeling, including machine learning utility system that might be deployed in computational base

Model predictive satisficing fuzzy logic control,”

Abstract-Model-predictive control, which is an alternative to conventional optimal control, provides controller solutions to many constrained and nonlinear control problems. However, even when a good model is available, it may be necessary for an expert to specify the relationship between local model predictions and global system performance. We present a satisficing fuzzy logic controller that is based on a receding control horizon, but which employs a fuzzy description of system consequences via model predictions. This controller considers the gains and losses associated with each control action, is compatible with robust design objectives, and permits flexible defuzzifier design. We demonstrate the controller&apos;s application to representative problems from the control of uncertain nonlinear systems