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

A design of higher-level control based genetic algorithms for wastewater treatment plants

A wastewater treatment plant facilitates various processes (e.g., physical, chemical and biological) to treat industrial wastewater and remove pollutants. This topic recently encourages much attention in different fields to explore suitable methods to be able to remove chemical or biological elements from wastewater. This paper presents a novel genetic based control algorithm for biological wastewater treatment plants, intending to improve the quality of the effluent, and reduce the costs of operation. The proposed controller allows adjusting the dissolved oxygen in the last basin, , according to the operational conditions, instead of maintaining it at a constant value. genetic algorithm (GA) is used in the higher-level control design to verify the desired value of the lower level based on the Ammonium and ammonia nitrogen concentration in the fourth tank, , concentration values in the fourth tank. In order to modify the tuning parameters of the higher level, an adjustment region is determined. Consequently, the effluent quality is improved, help to decrease the total operational cost. Simulation results demonstrate the advantages of the proposed method

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Computation Approaches for Continuous Reinforcement Learning Problems

Optimisation theory is at the heart of any control process, where we seek to control the behaviour of a system through a set of actions. Linear control problems have been extensively studied, and optimal control laws have been identified. But the world around us is highly non-linear and unpredictable. For these dynamic systems, which don’t possess the nice mathematical properties of the linear counterpart, the classic control theory breaks and other methods have to be employed. But nature thrives by optimising non-linear and over-complicated systems. Evolutionary Computing (EC) methods exploit nature’s way by imitating the evolution process and avoid to solve the control problem analytically. Reinforcement Learning (RL) from the other side regards the optimal control problem as a sequential one. In every discrete time step an action is applied. The transition of the system to a new state is accompanied by a sole numerical value, the “reward” that designate the quality of the control action. Even though the amount of feedback information is limited into a sole real number, the introduction of the Temporal Difference method made possible to have accurate predictions of the value-functions. This paved the way to optimise complex structures, like the Neural Networks, which are used to approximate the value functions. In this thesis we investigate the solution of continuous Reinforcement Learning control problems by EC methodologies. The accumulated reward of such problems throughout an episode suffices as information to formulate the required measure, fitness, in order to optimise a population of candidate solutions. Especially, we explore the limits of applicability of a specific branch of EC, that of Genetic Programming (GP). The evolving population in the GP case is comprised from individuals, which are immediately translated to mathematical functions, which can serve as a control law. The major contribution of this thesis is the proposed unification of these disparate Artificial Intelligence paradigms. The provided information from the systems are exploited by a step by step basis from the RL part of the proposed scheme and by an episodic basis from GP. This makes possible to augment the function set of the GP scheme with adaptable Neural Networks. In the quest to achieve stable behaviour of the RL part of the system a modification of the Actor-Critic algorithm has been implemented. Finally we successfully apply the GP method in multi-action control problems extending the spectrum of the problems that this method has been proved to solve. Also we investigated the capability of GP in relation to problems from the food industry. These type of problems exhibit also non-linearity and there is no definite model describing its behaviour

Metal-Organic Framework Synthesis System Based on Fuzzy Predictive Control via Network Transmission

The purpose of this study is to construct metal-organic framework (MOF) synthesis heating systems based on fuzzy method for monitoring and automatic control. In this study, the temperature sensing module for measurements sensed values that it through a wireless ZigBee chips and wired DAQ device for real-time data transmission. Because MOF synthesis, often due to different modes of heating or heating instability caused by its nucleation and crystal growth rate, is an important influence, leading to different crystallinity, the use of fuzzy theory to predict the temperature parameter and instant heating MOF synthesis parameters can be adjusted to improve the accuracy of the system. The research system to RS-232 interface module for infrared emission control packets issued and automated control of the furnace through the infrared receiver module. This study is based on a terminal interface window of Visual Basic programming and LabView graphical diagram for control system design. Finally, this research, through a number of experiments to validate the use of fuzzy system development methods and networks, can improve the accuracy of the reaction efficiency MOF sensing and control the heating system

An overview of decision table literature.

The present report contains an overview of the literature on decision tables since its origin. The goal is to analyze the dissemination of decision tables in different areas of knowledge, countries and languages, especially showing these that present the most interest on decision table use. In the first part a description of the scope of the overview is given. Next, the classification results by topic are explained. An abstract and some keywords are included for each reference, normally provided by the authors. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. Other examined topics are the theoretical or practical feature of each document, as well as its origin country and language. Finally, the main body of the paper consists of the ordered list of publications with abstract, classification and comments.

Advances in PID Control

Since the foundation and up to the current state-of-the-art in control engineering, the problems of PID control steadily attract great attention of numerous researchers and remain inexhaustible source of new ideas for process of control system design and industrial applications. PID control effectiveness is usually caused by the nature of dynamical processes, conditioned that the majority of the industrial dynamical processes are well described by simple dynamic model of the first or second order. The efficacy of PID controllers vastly falls in case of complicated dynamics, nonlinearities, and varying parameters of the plant. This gives a pulse to further researches in the field of PID control. Consequently, the problems of advanced PID control system design methodologies, rules of adaptive PID control, self-tuning procedures, and particularly robustness and transient performance for nonlinear systems, still remain as the areas of the lively interests for many scientists and researchers at the present time. The recent research results presented in this book provide new ideas for improved performance of PID control applications

Damping interarea and torsional oscillations using FACTS devices

A problem of interest in the power industry is the mitigation of interarea and torsional oscillations. Interarea oscillations are due to the dynamics of interarea power transfer and often exhibit poor damping when the aggregate power transfer over a corridor is high relative to the transmission strength. These oscillations can severely restrict system operations and, in some cases, can lead to widespread system disturbances. Torsional oscillations are induced due to the interaction between transmission system disturbances and turbine-generator shaft systems. The high torsional stresses induced due to some of these disturbances reduce the life expectancy of the turbine-generators and, in severe cases, may cause shaft damage. This thesis reports the development of novel control techniques for Flexible AC Transmission System (FACTS) devices for the purpose of damping power system interarea and torsional oscillations. In this context, investigations are conducted on a typical three-area power system incorporating FACTS devices. The Genetic Algorithm (GA) and fuzzy logic techniques are used for designing the FACTS controllers. Although attention is focused in the investigations of this thesis on the Unified Power Flow Controller (UPFC), studies are also conducted on two other FACTS devices, a three voltage-source converter Generalized Unified Power Flow Controller (GUPFC) and a voltage-source converter back-to-back HVdc link. The results of the investigations conducted in this thesis show that the achieved control designs are effective in damping interarea oscillations as well as the high torsional torques induced in turbine-generator shafts due to clearing and high-speed reclosing of transmission system faults. The controller design procedures adopted in this thesis are general and can be applied to other FACTS devices incorporated in a power system. The results and discussion presented in this thesis should provide valuable information to electric power utilities engaged in planning and operating FACTS devices