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

Applying fuzzy theory concepts to the analysis of employment diversification of farm households: methodological considerations

The Deliverable 7.2 (D7.2) of the SCARLED project provides methodological considerations for applying fuzzy set theory to the analysis of employment diversification of farm households. It presents a Mamdani's type fuzzy inference model and describes its application within the project's framework. The model consists of ten variables that are grouped into the four factors: (i) necessity to diversify, (ii) internal preconditions, (iii) external preconditions, and (iv) attitudes. The coherence of these four factors with the integrated framework for the analysis of nonfarm rural employment is discussed. The model will be realised in the Fuzzy Logic Toolbox from MATLAB®. Forty four membership functions and 138 rules are going to be implemented, tested, and adapted with survey data from the five countries: Bulgaria, Hungary, Poland, Romania, and Slovenia. The final model will be used to assess the diversification potential of 15 regions in these countries. --

New perspectives on realism, tractability, and complexity in economics

Fuzzy logic and genetic algorithms are used to rework more realistic (and more complex) models of competitive markets. The resulting equilibria are significantly different from the ones predicted from the usual static analysis; the methodology solves the Walrasian problem of how markets can reach equilibrium, starting with firms trading at disparate prices. The modified equilibria found in these complex market models involve some mutual self-restraint on the part of the agents involved, relative to economically rational behaviour. Research (using similar techniques) into the evolution of collaborative behaviours in economics, and of altruism generally, is summarized; and the joint significance of these two bodies of work for public policy is reviewed. The possible extension of the fuzzy/ genetic methodology to other technical aspects of economics (including international trade theory, and development) is also discussed, as are the limitations to the usefulness of any type of theory in political domains. For the latter purpose, a more differentiated concept of rationality, appropriate to ill-structured choices, is developed. The philosophical case for laissez-faire policies is considered briefly; and the prospects for change in the way we ‘do economics’ are analysed

The scientific way of thinking in statistics, statistical physics and quantum mechanics

This paper focuses on the way of thinking in both classical and modern Physics and Statistics, Statistical Mechanics or Statistical Physics and Quantum Mechanics. These different statistical ways of thinking and their specific methods have generated new fields for new activities and new scientific disciplines, like Econophysics (between Economics and Physics), Sociophysics (between Sociology and Physics), Mediaphysics (between all media and comunication sciences), etc. After describing some recent definitions of statistical thinking, implications of statistical education for developing Econophysics, Sociophysics, Mediaphysics, etc. from Statistical and Quantum Mechanics are discussed. Several opinions are given as a direct liaison between the classical and modern statistical sciences and thoughts of a scientific research in general. The main conclusion is that Statistics developing habits of mind for Statistical Physics in Econophysics, for the Quantum Mechanics in Quantum Physics, for the Sociology in Sociophysics will be essential for the future of all.Statistics, Statistical Physics, Quantum Mechanics, Econophysics, Sociophysics

Mapping knowledge management and organizational learning in support of organizational memory

The normative literature within the field of Knowledge Management has concentrated on techniques and methodologies for allowing knowledge to be codified and made available to individuals and groups within organizations. The literature on Organizational Learning however, has tended to focus on aspects of knowledge that are pertinent at the macro-organizational level (i.e. the overall business). The authors attempt in this paper to address a relative void in the literature, aiming to demonstrate the inter-locking factors within an enterprise information system that relate knowledge management and organizational learning, via a model that highlights key factors within such an inter-relationship. This is achieved by extrapolating data from a manufacturing organization using a case study, with these data then modeled using a cognitive mapping technique (Fuzzy Cognitive Mapping, FCM). The empirical enquiry explores an interpretivist view of knowledge, within an Information Systems Evaluation (ISE) process, through the associated classification of structural, interpretive and evaluative knowledge. This is achieved by visualizng inter-relationships within the ISE decision-making approach in the case organization. A number of decision paths within the cognitive map are then identified such that a greater understanding of ISE can be sought. The authors therefore present a model that defines a relationship between Knowledge Management (KM) and Organisational Learning (OL), and highlights factors that can lead a firm to develop itself towards a learning organization

New perspectives on realism, tractability, and complexity in economics

Fuzzy logic and genetic algorithms are used to rework more realistic (and more complex) models of competitive markets. The resulting equilibria are significantly different from the ones predicted from the usual static analysis; the methodology solves the Walrasian problem of how markets can reach equilibrium, starting with firms trading at disparate prices. The modified equilibria found in these complex market models involve some mutual self-restraint on the part of the agents involved, relative to economically rational behaviour. Research (using similar techniques) into the evolution of collaborative behaviours in economics, and of altruism generally, is summarized; and the joint significance of these two bodies of work for public policy is reviewed. The possible extension of the fuzzy/ genetic methodology to other technical aspects of economics (including international trade theory, and development) is also discussed, as are the limitations to the usefulness of any type of theory in political domains. For the latter purpose, a more differentiated concept of rationality, appropriate to ill-structured choices, is developed. The philosophical case for laissez-faire policies is considered briefly; and the prospects for change in the way we ‘do economics’ are analysed.Fuzzy logic; genetic algorithms; complexity; emergence; rationality; ill-structured choice; equilibrium; Walrasian Crier; paradigm change;

An Intelligent Knowledge Management System from a Semantic Perspective

Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

Defining Urban Complex Problems with Fuzzy Analysis: The Case of Söke Settlement in Turkey

This article aims to follow the application of fuzzy approach in the analysis of urban complex problems; classifying urban problems according to different criteria. It proposes a methodology to combine different dimensions of quality of life, with the economic (income, employment), social (education) physical (health and infrastructure) indicators into Quality of Life Index (QLI) by applying Totally Fuzzy Analysis (TFA). The objective of the present work is to identify, based on survey data of Söke settlement in Turkey, to define the sub zones according to life quality indicators. The sample for the survey is designed to provide representative samples of private households in Söke. A stratified random sample is selected such that every sampling unit in the population has an equal probability of being selected for the sample. From the population of 14582 housing units in Söke, a sample size of 366 was chosen. As a result, 366 households were interviewed without missing. The indicators that have been used for the fuzzy model consist of three main blocks. The first one in the indicators that describe development of socio-economic system is the economic indicators such as urban poverty (income and expenditures), property ownership, employment and attributes of the labor force. The second one is physical indicators that consist of availability of residential services, housing density and the quality of housing units. The third one is the social indicators which can be described as household profile, cultural expenditures and life patterns. The goal is achieved by applying a new and straightforward method of GIS and fuzzy logic. This methodology was applied in the study area and the results presented in the form of tables and maps. The results revealed that there are spatial, social and economic disparities in some parts of the area. The findings indicate that the fuzzy technics are powerful analytic tools for helping planners define urban complex problems and to see relations between social, economic and physical factors.