An intelligent group decision-support system and its application for project performance evaluation

Purpose: In any organization there are main goals, with lots of projects designed to achieve these goals. It is important for any organization to determine how much these projects affect the achievement of these goals. The purpose of this paper is to develop a fuzzy multiple attribute-based group decision-support system (FMAGDSS) to evaluate projects' performance in promoting the organization's goals utilizing simple additive weighting (SAW) algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. The proposed FMAGDSS deals with choosing the most appropriate fuzzy ranking algorithm for solving a given fuzzy multi attribute decision making (FMADM) problem with both qualitative and quantitative criteria (attributes), and uncertain judgments of decision makers. Design/methodology/approach: In this paper, a FMAGDSS model is designed to determine scores and ranks of every project in promoting the organization's goals. In the first step of FMAGDSS model, all projects are assessed by experts based on evaluation criteria and the organization's goals. The proposed FMAGDSS model will then choose the most appropriate fuzzy ranking method to solve the given FMADM problem. Finally, a sensitivity analysis system is developed to assess the reliability of the decision-making process and provide an opportunity to analyze the impacts of "criteria weights" and "projects" performance' on evaluating projects in achieving the organizations' goals, and to assess the reliability of the decision-making process. In addition, a software prototype has been developed on the basis of FMAGDSS model that can be applied to solve every FMADM problem that needs to rank alternatives according to certain attributes. Findings: The result of this study simplifies and accelerates the evaluation process. The proposed system not only helps organizations to choose the most efficient projects for sustainable development, but also helps them to assess the reliability of the decision-making process, and decrease the uncertainty in final decision caused by uncertain judgment of decision makers. Research limitations/implications: Future studies are suggested to expand this system to evaluate and rank the project proposals. To achieve this goal, the efficiency of the projects in line with organization's goals, should be predicted.Originality/value: This study contributes to the relevant literature by proposing a FMAGDSS model to evaluate projects in promoting organization's goals. The proposed FMAGDSS has ability to choose the most appropriate fuzzy ranking algorithm to solve a given FMADM problem based on the type and the number of attributes and alternatives, considering the least computation and time consumption for ranking alternatives. © Emerald Group Publishing Limited

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types

An Approach to Agent-Based Service Composition and Its Application to Mobile

This paper describes an architecture model for multiagent systems that was developed in the European project LEAP (Lightweight Extensible Agent Platform). Its main feature is a set of generic services that are implemented independently of the agents and can be installed into the agents by the application developer in a flexible way. Moreover, two applications using this architecture model are described that were also developed within the LEAP project. The application domain is the support of mobile, virtual teams for the German automobile club ADAC and for British Telecommunications

Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach

Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy