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PurposeThe concept of knowledge cities (KCs) offers advantages to any urban region. Many cities globally claim themselves as being already KCs, while other cities have elaborated strategic plans in order to integrate this concept into their operational structures. The examination of their approaches reveals however that these initiatives are fragmented. The purpose of this paper is to present a multi‐dimensional and integrated decision support model for a KC's strategy formulation.Design/methodology/approachReference is made to a methodological approach (KnowCis) for the integrated development of a KC, consisting of five main phases and taking into account nine different dimensions. The strategy formulation phase is a particularly complex procedure for any authority (e.g. local government or city's development agency). The reasons for this complexity are related to the amplitude of the KC concept, to the variety of the factors to be considered as well as to the challenge for balancing the needs and interests of different target groups.FindingsThe proposed model consists of the following building blocks: identification of the appropriate actions (based on the KnowCis methodology), modeling of the city's current status as a KC (via the development of related indicators), assessment of actions' necessity (based on the indicators' outcomes and through the benchmarking of other successful KCs cases), selection of the most appropriate form for each proposed action (based on their efficiency during the last reference period) and, finally, prioritisation of the proposed actions (based on a multi‐criteria approach).Research limitations/implicationsThe main suggestion for future research is the development of an intelligent information system which will incorporate the building blocks of the proposed model.Originality/valueThe originality and value of the paper is that the proposed model can be a really helpful decision support tool for any city which is developing a knowledge‐based strategy.

The main scope of this paper is the implementation of a method for production scheduling, using advanced model checking tools. This method makes use of timed automata in order to model complex production scheduling problems like the job-shop and the open-shop scheduling problems. By modeling scheduling problems using timed automata, feasible schedules correspond then to paths in the automata, while finding the optimum schedule corresponds to finding the shortest path in an automaton. Several algorithms and heuristics have been proposed to find the shortest paths in timed automata, which are mainly based on the implementation of graph algorithms that search the transition graph. In our work, an advanced model design and checking tool, called Uppaal, has been used to implement and test the above method on a number of different models and to show the effectiveness of such an approach in terms of finding optimal, or near to optimal, schedules in polynomial time, even for large scale problems.

PurposeThe purpose of this paper is to propose an approach for describing and discovering knowledge resources in distributed environments.Design/methodology/approachThe paper proposes a solution that includes a framework model and a technological infrastructure to achieve distributed knowledge discovery. It provides the solution based on a combination of research from knowledge management and distributed processing areas.FindingsThe paper finds a faceted ontology and three discrete models that best achieve distributed processing in terms of knowledge items and knowledge objects. By these mechanisms are provided to: represent knowledge in a generic manner that will unchain knowledge from tight links to specific contexts, purposes and audiences; discover and retrieve knowledge stored in distributed sources; interpret different knowledge representations to enable comprehension of knowledge on the recipient side and tackle the heterogeneity problem; and simplify and accelerate knowledge exchange in distributed environments.Research limitations/implicationsThe utilization of knowledge in distributed environments (intra‐ or inter‐organizational) presupposes the existence of a representation of knowledge in a commonly understandable manner. Knowledge resources are distributed in the web and databases in an unstructured manner.Practical implicationsThe paper can be used as the basic consensus and infrastructure for the development of a coherent, manageable semantic web. The three models should be addressed in terms of commercial applications.Originality/valueKnowledge management and distributed processing research fields are combined in order to provide a solution that best addresses the problem of distributed knowledge.

Purpose – This paper aims to deal with the development of a risk management application for the modelling, optimal adaptation and implementation of an ERP system.Design/methodology/approach – This paper presented a risk management application for the modeling, optimal adaptation and implementation of an ERP system. The application was tested with the operations and capabilities of the ERP commercial package “SINGULAR Enterprise (SEn)” of the Greek Software House DELTA‐SINGULAR S.A.Findings – The functional result of this application was proved to support considerably the management of risk within the implementation of the ERP system.Originality/value – To the best knowledge of the authors there is no other current generic research in this technological field concerning small or medium‐sized enterprises. With the development of this application, the goals mentioned in the conclusions were achieved.

PurposeTo control and reduce the time and the cost of implementation and to achieve optimal adaptation of the processes and structures of the enterprise in an ERP environment.Design/methodology/approachThis paper presents a fundamental methodology based on goal directed project management (GDPM) principles which supports the complicated process of implementation and optimal adaptation (IOA) of an ERP system.FindingsBy implementing this methodology it was found that it is able to control and reduce the time and the cost of implementation and to achieve optimal adaptation of the processes and structures of the enterprise in an ERP environment.Research limitations/implicationsBased on the above methodology a system must be developed, which will aim at providing the project manager with the ability to create all the necessary elements for the management and realization of the project and to lead the consultant during the IOA process of ERP systems.Practical implicationsThis methodology was connected to, and tested with the operations and capabilities of an ERP commercial package in one of the leading software houses in Greece. The functional result of this methodology was proved to support considerably the process of IOA of SEn in the companies‐customers.Originality/valueThis paper provides a generic research in this technological field concerning small or medium‐sized enterprises based on the GDPM principles which aims at the facilitation and effective guidance for the installation process IOA in all stages and in all levels of analysis.

Environmental legislation and its enforcement has undoubtedly forced the textile industry to be rather cautious in selecting the appropriate processes and equipment. The most efficient, economic and minimal environmental pollution processing methods will be increasingly demanded throughout the 1990s. However, the majority of the textile industry consists of small and medium enterprises, where the lack of expertise on the use of best available techniques leads to levels of operation far away from the optimal. An expert system for a priori economic evaluation of potential interventions in the textile manufacturing processes and equipment is necessary to support decision making in the management level. This paper presents such an expert system, designed and implemented in four stages: formal description of the key factors that affect the dyeing process in the textile industry, development of models for the representation of relevant information, development of models for the representation of knowledge and integration of the above‐mentioned models in a unified information system that supports the decision‐making process in the management of textile enterprises.

Managing large amounts of information and efficiently using this information in improved decision making has become increasingly challenging as businesses collect terabytes of data. Intelligent solutions, based on neural networks (NNs) and genetic algorithms (GAs), to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. The current study provides an overview for the operations researcher of the neural networks and genetic algorithms methodology, as well as their historical and current use in business. The main aim is to present and focus on the wide range of business areas of NN and GA applications, avoiding an in‐depth analysis of all the applications – with varying success – recorded in the literature. This review reveals that, although still regarded as a novel methodology, NN and GA are shown to have matured to the point of offering real practical benefits in many of their applications.

Knowledge management (KM) has recently received considerable attention in the computer information systems community and is continuously gaining interest by industry, enterprises and government. Decision support and KM processes are interdependent activities in many organizations. In all cases, decision makers always combine different types of data and knowledge available in various forms in the organization. One of the key – but also criticized – building blocks for advancing this field of knowledge management and consequently supporting the decision making is artificial intelligence (AI). In this framework, this paper aims to improve understanding of AI towards knowledge management. It examines and discusses both the potential and the limitations of basic AI technologies in terms of their capability to support the KM process and shares thoughts and estimations on further research on the development of the next generation decision support environments.

A great challenge for today’s companies is not only how to adapt to the changing business environment but also how to gain a competitive advantage from the way in which they choose to do so. As a basis for achieving such advantages, companies have started to seek to improve the performance of various operations. Forecasting is one of them; it is important to firms because it can help ensure that effective use of resources is made. In the market there are a number of off‐the‐shelf system products, which provide forecasts. The new trend, of moving traditional software packages to Web services, has pushed forecasting to a new dimension, named by the authors as “e‐forecasting”. In this paper, a first approach to e‐forecasting is made by throwing light on several aspects and a survey is presented which aims at identifying existing Web forecasting services.

Intelligent solutions, based on expert systems (ES), to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. Expert systems are being developed and deployed worldwide in myriad applications, mainly because of their symbolic reasoning and its explanation capabilities. Provides an overview for the operations researcher of the expert systems methodology, as well as their historical and current use in business. Aims to present and focus on the wide range of business areas of ES applications, avoiding an in‐depth analysis of all the applications ‐‐ with varying success ‐‐ recorded in the literature. Reveals that, although still regarded as a novel methodology, ES are shown to have matured to the point of offering real practical benefits in many of their applications.

Companies deal with many decision‐making processes whose impact on the global performance can be very strong. As a consequence, the role of the decision support systems (DSSs) within the organization is critical. Considering the imprecise or fuzzy nature of the data in real‐world problems, it becomes obvious that the ability to manage uncertainty turns out to be a crucial issue for a DSS. In this framework, this paper discusses the key role of fuzzy logic (FL) in the DSSs, presents new applications of FL in DSSs in various sectors and identifies new challenges and new directions for further research.

In the current competitive environment, each company faces a number of challenges: quick response to customers’ demands, high quality of products or services, customers’ satisfaction, reliable delivery dates, high efficiency, and others. As a result, during the last five years many firms have proceeded to the adoption of enterprise resource planning (ERP) solutions. ERP is a packaged software system, which enables the integration of operations, business processes and functions, through common data‐processing and communications protocols. However, the majority, if not all, of these systems do not support the production scheduling process that is of crucial importance in today’s manufacturing and service industries. In this paper, the authors propose a knowledge‐based system for production‐scheduling that could be incorporated as a custom module in an ERP system. This system uses the prevailing conditions in the industrial environment in order to select dynamically and propose the most appropriate scheduling algorithm from a library of many candidate algorithms.

In recent times, Artificial Neural Networks (ANNs) have been receiving increased attention as tools for business applications. In this paper, we focus on the use of ANNs in the field of production scheduling. The history of ANNs in production scheduling is outlined, leading to a discussion of the various approaches, as well as the current research directions. The paper concludes by sharing thoughts and estimations on the future prospects of ANNs in this area.

Knowledge management has recently received considerable attention in the computer information systems community and is continuously gaining interest from industry, enterprises and government. As we move towards building knowledge organizations, knowledge management in combination with information management will play a fundamental role towards the success of transforming individual knowledge into organizational knowledge. In this framework, this paper discusses the key concepts of human‐computer interaction in knowledge management, identifies new challenges of knowledge management for Web‐based business and proposes a “user agent architecture” for knowledge management in e‐learning environments. User agents use artificial neural network technology and can be used in various e‐learning or e‐training environments, in order to provide them with means of managing information stored, filtering content and enabling better knowledge adoption on behalf of their users.

Presents the NUMASS Web‐based system, which was developed for the needs of the European Commission (designed to be used mainly by AIDCO‐A5). Its main objective is to improve the management and monitoring of Tacis Nuclear Safety Programme, by showing in a graphical way the progress of projects/works and prompting for corrective actions.

In this paper we consider the use of ontologies as the basis for structuring and simplifying the process of constructing real‐time problem‐solving tools, focusing specifically on the task of production scheduling. In spite of the commonality in production scheduling system requirements and design, different scheduling environments invariably present different challenges (e.g. different constraints, different objectives, different domain structure, etc.). The proposed methodology for building ontologies used for production scheduling systems represents a synthesis of extensive work in developing constraint‐based scheduling models for a wide range of applications in manufacturing and production planning. Since the effective modeling is one of the most important and difficult steps in the development of reliable information systems, and taking into consideration the fact that the general problem of the production scheduling in the industries is very difficult and still unsolved, one can easily estimate the merit of this methodology.

From the very earliest moments in the modern history of the computer, scientists have dreamed of creating advanced systems which would simulate human meditation and reasoning. Of all the modern technological quests, this research to create artificially intelligent computer systems has been one of the most ambitious and fascinating. Although attempts had been made more than 30 years ago to develop and apply such systems to the medical sciences, the field languished for decades. In this framework, this paper aims to share thoughts and estimations on the important role of expert systems in medicine and address their future as well as the trends that are foreseen in this area.

From the very earliest moments in the modern history of the computer, scientists have dreamed of creating advanced systems that would simulate human thinking and reasoning. Of all the modern technological quests, research to create artificially intelligent computer systems has been one of the most ambitious and fascinating. Although attempts were made more than thirty years ago to develop and apply such systems to the medical sciences, the field languished for decades. In this context, this paper aims to share thoughts about and assessments of the important role of expert systems in medicine and address their future as well as the trends that are foreseen in this area.

E-government, driven by an ever-increasing and pervasive use of information and communication technologies, is affecting the public sector more and more (Bannister, 2005; Eyob, 2004; Metaxiotis & Psarras, 2004). Many governments across the globe have resorted to instituting e-government initiatives as a way of better positioning themselves in the Information Age (Information for Development Programme [InfoDev], 2004), or seem at least to be showing commitments in redressing the imbalances resulting from the low utilization of knowledge resources and ICT in the economy and governance (Joi, 2004). E-government is enabling government organizations to provide better services to their constituents. The ability to improve citizens’ access to services has made e-government an attractive investment for government organizations, fueling worldwide implementation of such applications (Amaravadi, 2005; Scherlis & Eisenberg, 2003). As an emerging practice, e-government seeks to realize processes and structures for harnessing the potentialities of information and communication technologies at various levels of government and the public sector for the purpose of enhancing good governance. The key issues in transformation are the adoption and uptake of interoperable standards, the development of appropriate business models, the legal and policy frameworks that will facilitate integration, and governance arrangements that support both enterprise responsibilities and crossagency approaches and responsibilities.

In recent years the growing complexity of industrial manufacturing and the need for higher efficiency, shortened product life cycle, greater flexibility, better product quality, greater satisfaction of customer’s expectations and lower cost have changed the face of manufacturing practice. A great challenge for today’s companies is not only how to adapt to this changing business environment but also how to draw a competitive advantage from the way in which they choose to do so. As a basis to achieve such advantages, companies have started to seek to optimize the operation of their manufacturing systems. Since traditional, centralized manufacturing planning, scheduling and control mechanisms were found insufficiently flexible to respond to this new situation, many manufacturing companies decided to adopt intelligent solutions. Expert systems technology provides a natural way to overcome such problems, and to design and implement distributed intelligent manufacturing environments.

One of the most difficult works in the world of ERP systems is proven to be their installation and customization to fulfil the needs of the customer. With the great development of Knowledge Management in the last decade a new era has begun; the era of capturing, storing and managing effectively ERP Consultants' knowledge in order to rapidly build customized enterprise systems, which in other times would take months to model and develop. This paper presents such a case, where IT and Knowledge Management power are used in order to surpass the difficulties of this ERP field. The authors describe an advanced generic Case-Toolkit, which is able to support the complicated process of Modelling and optimal adaptation (MOA) of an ERP system to the needs of an enterprise. This tool-kit aims to be used by the ERP consultants in two ways. The first is to analyse, categorise and store the business processes and the second is to use the business processes that are created for presale reasons.

This case study serves to illustrate an integrated and practical methodology for evaluating advanced information database systems. The goal of the integration is to create a top-down evaluation process that reduces user and data requirements to a standard evaluation structure. In this framework, the evaluation of the Integrated Measurement and Evaluation System IMES was implemented by the Energy Policy Unit of the National Technical University of Athens. Evaluation team members successfully followed the proposed evaluation methodology.

Real Estate valuation in urban areas is a very difficult task that has absorbed the interest of many academics in the past years. Many qualitative and quantitative variables affect the value of an estate in urban areas. As a result, multivariate models are more suitable in the appraisal process. One of the most common approaches is multiple linear regression technique (MLR) that is always used as a benchmark in various studies. A very promising way of dealing with uncertainty in real estate analysis and producing sufficient evaluations is the use of Artificial Neural Networks (ANNs). Also, an expert method combining the strengths of MLR and ANN is tried out successfully. The purpose of this study is to compare these approaches on the basis of the data from the Attica urban area in Greece.

The main scope of this paper is the implementation of a method for production scheduling, using advanced model checking tools. This method makes use of timed automata to model complex production scheduling problems such as the job-shop and the open-shop scheduling problems. By modelling scheduling problems using timed automata, feasible schedules correspond to paths in the automata, while finding the optimum schedule corresponds to finding the shortest path in an automaton. Several algorithms and heuristics have been proposed for finding the shortest paths in timed automata, which are mainly based on the implementation of graph algorithms that search the transition graph. In our work, an advanced model design and checking tool, called UPPAAL, has been used to implement and test the above method on a number of different models and to show the effectiveness of such an approach in terms of finding optimal or near to optimal, schedules in polynomial time, even for large-scale problems.

Companies deal with many decision-making processes whose impact on the global performance can be very strong. As a consequence, the role of decision support systems (DSSs) within the organisation is critical. Considering the imprecise or fuzzy nature of the data in real-world problems, it becomes obvious that the ability of managing uncertainty turns to be a crucial issue for a DSS. Fuzzy logic (FL) is a method for understanding, quantifying and dealing with vague, ambiguous and uncertain characteristics, ideas and judgments. In more specific terms, what is central about fuzzy logic is that, unlike classical logical systems, it aims at modelling the imprecise modes of reasoning that play essential role in the remarkable human ability to make rational decisions in an environment of uncertainty and imprecision. In this framework, this paper aims at promoting the integration of fuzzy logic into DSSs for the benefit of decision-makers. It discusses the key role of FL in DSSs, presents new applications of FL in DSSs in various sectors and identifies new challenges and new directions for further research. This review reveals that, although still regarded as a new methodology, FL is shown to have matured to the point of offering real practical benefits in many of its applications.