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

The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

Failure detection and separation in SOM based decision support

Failure management in process industry has difficult tasks. Decision support in control rooms of nuclear power plants is needed. A prototype that uses Self-Organizing Map (SOM) method is under development in an industrial project. This paper has focus on failure detection and separation. A literature survey outlines the state-of-the-art and reflects our study to related works. Different SOM visualizations are used. Failure management scenarios are carried out to experiment the methodology and the Man-Machine Interface (MMI). U-matrix trajectory analysis and quantization error are discussed more in detail. The experiments show the usefulness of the chosen approach. Next step will be to add more practical views by analyzing real and simulated industrial data with the control room tool and by feedback from the end users

An abnormal situation modeling method to assist operators in safety-critical systems

© 2014 Elsevier Ltd. One of the main causes of accidents in safety-critical systems is human error. In order to reduce human errors in the process of handling abnormal situations that are highly complex and mentally taxing activities, operators need to be supported, from a cognitive perspective, in order to reduce their workload, stress, and the consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing errors. Despite the importance of SA in decision-making in time- and safety-critical situations, the difficulty of SA modeling and assessment means that very few methods have as yet been developed. This study confronts this challenge, and develops an innovative abnormal situation modeling (ASM) method that exploits the capabilities of risk indicators, Bayesian networks and fuzzy logic systems. The risk indicators are used to identify abnormal situations, Bayesian networks are utilized to model them and a fuzzy logic system is developed to assess them. The ASM method can be used in the development of situation assessment decision support systems that underlie the achievement of SA. The performance of the ASM method is tested through a real case study at a chemical plant

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.

Uncertainty quantification for safety verification applications in nuclear power plants

There is an increasing interest in computational reactor safety analysis to systematically replace the conservative calculations by best estimate calculations augmented by quantitative uncertainty analysis methods. This has been necessitated by recent regulatory requirements that have permitted the use of such methods in reactor safety analysis. Stochastic uncertainty quantification methods have shown great promise, as they are better suited to capture the complexities in real engineering problems. This study proposes a framework for performing uncertainty quantification based on the stochastic approach, which can be applied to enhance safety analysis. Additionally, risk level has increased with the degradation of Nuclear Power Plant (NPP) equipment and instrumentation. In order to achieve NPP safety, it is important to continuously evaluate risk for all potential hazards and fault propagation scenarios and map protection layers to fault / failure / hazard propagation scenarios to be able to evaluate and verify safety level during NPP operation. In this study, the Fault Semantic Network (FSN) methodology is proposed. This involved the development of static and dynamic fault semantic network (FSN) to model possible fault propagation scenarios and the interrelationships among associated process variables. The proposed method was demonstrated by its application to two selected case studies. The use of FSN is essential for fault detection, understanding fault propagation scenarios and to aid in the prevention of catastrophic events. Two transient scenarios were simulated with a best estimate thermal hydraulic code, CATHENA. Stochastic uncertainty quantification and sensitivity analyses were performed using the OPENCOSSAN software which is based on the Monte Carlo method. The effect of uncertainty in input parameters were investigated by analyzing the probability distribution of output parameters. The first four moments (mean, variance, skewness and kurtosis) of the output parameters were computed and analyzed. The uncertainty in output pressure was 0.61% and 0.57% was found for the mass flow rate in the Edward???s blowdown transient. An uncertainty of 0.087% was obtained for output pressure and 0.048% for fuel pin temperature in the RD-14 test case. These results are expected to be useful for providing insight into safety margins related to safety analysis and verification

A review of applications of fuzzy sets to safety and reliability engineering

Safety and reliability are rigorously assessed during the design of dependable systems. Probabilistic risk assessment (PRA) processes are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). In conventional PRA, failure data about components is required for the purposes of quantitative analysis. In practice, it is not always possible to fully obtain this data due to unavailability of primary observations and consequent scarcity of statistical data about the failure of components. To handle such situations, fuzzy set theory has been successfully used in novel PRA approaches for safety and reliability evaluation under conditions of uncertainty. This paper presents a review of fuzzy set theory based methodologies applied to safety and reliability engineering, which include fuzzy FTA, fuzzy FMEA, fuzzy ETA, fuzzy Bayesian networks, fuzzy Markov chains, and fuzzy Petri nets. Firstly, we describe relevant fundamentals of fuzzy set theory and then we review applications of fuzzy set theory to system safety and reliability analysis. The review shows the context in which each technique may be more appropriate and highlights the overall potential usefulness of fuzzy set theory in addressing uncertainty in safety and reliability engineering