CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

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 Change Execution System for Enterprise Services with Compensation Support

Modern enterprises rely on a distributed IT infrastructure to execute their business processes, adopting Service Oriented Architectures in order to improve the flexibility and ease of adaptation of their functions. Nowadays this is a vital characteristic, as the increased competition forces companies to continuously evolve and adapt. SOA applications must be supported by management and deployment systems, which have to continuously apply modifications to the distributed infrastructure. This article presents a modelbased solution for automatically applying change plans to heterogeneous enterprise managed environments. The proposed solution uses models which describe in an abstract language the changes that need to be applied to the environment, and executes all the required operations to the specific managed elements. Also, to ensure that the environment ends in a stable state, compensation for previously executed operations is supported. The validation results from a case study taken from the banking domain are also presented here

Pervasive computing at tableside : a wireless web-based ordering system

Purpose &ndash; The purpose of this paper is to introduce a wireless web-based ordering system called iMenu in the restaurant industry. Design/methodology/approach &ndash; By using wireless devices such as personal digital assistants and WebPads, this system realizes the paradigm of pervasive computing at tableside. Detailed system requirements, design, implementation and evaluation of iMenu are presented.Findings &ndash; The evaluation of iMenu shows it explicitly increases productivity of restaurant staff. It also has other desirable features such as integration, interoperation and scalability. Compared to traditional restaurant ordering process, by using this system customers get faster and better services, restaurant staff cooperate more efficiently with less working mistakes, and enterprise owners thus receive more business profits. Originality/value &ndash; While many researchers have explored using wireless web-based information systems in different industries, this paper presents a system that employs wireless multi-tiered web-based architecture to build pervasive computing systems. Instead of discussing theoretical issues on pervasive computing, we focus on practical issues of developing a real system, such as choosing of web-based architecture, design of input methods in small screens, and response time in wireless web-based systems.<br /

Adapter module for self-learning production systems

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica, Sistemas e ComputadoresThe dissertation presents the work done under the scope of the NP7 Self-Learning project regarding the design and development of the Adapter component as a foundation for the Self-Learning Production Systems (SLPS). This component is responsible to confer additional proprieties to production systems such as lifecycle learning, optimization of process parameters and, above all, adaptation to different production contexts. Therefore, the SLPS will be an evolvable system capable to self-adapt and learn in response to dynamic contextual changes in manufacturing production process in which it operates. The key assumption is that a deeper use of data mining and machine learning techniques to process the huge amount of data generated during the production activities will allow adaptation and enhancement of control and other manufacturing production activities such as energy use optimization and maintenance. In this scenario, the SLPS Adapter acts as a doer and is responsible for dynamically adapting the manufacturing production system parameters according to changing manufacturing production contexts and, most important, according to the history of the manufacturing production process acquired during SLPS run time.To do this, a Learning Module has been also developed and embedded into the SLPS Adapter. The SLPS Learning Module represents the processing unit of the SLPS Adapter and is responsible to deliver Self-learning capabilities relying on data mining and operator’s feedback to up-date the execution of adaptation and context extraction at run time. The designed and implemented SLPS Adapter architecture is assessed and validated into several application scenario provided by three industrial partners to assure industrial relevant self-learning production systems. Experimental results derived by the application of the SLPS prototype into real industrial environment are also presented