Abstract: There are numerous real life examples done by many people which show that the alternatives of a decision sometimes can reverse their original rank order when new alternatives are added or old ones deleted and without bringing in new criteria. There is no mathematical theorem which proves that rank must always be preserved and there cannot be because of real life and hypothetical counter examples in decision making methods. Rank preservation came to be accepted as the standard because of techniques that could only rate alternatives one at a time treating them as independent. Thus an alternative receives a score and it will not change when other alternatives are added or deleted. All methods that only rate alternatives one at a time, thus always preserving rank, may not lead to the right decision; even if they may be right in certain areas of application. In reality, to determine how good an alternative is on an intangible criterion needs experience and knowledge about other alternatives and hence in their evaluation, the alternatives cannot be completely considered as independent of one another.

Keywords: Decision Making, priorities, ranking, rank preservation, rank reversal.

Abstract: Recently, ten knowledge areas (KAs) of project management have been published by the PMBOK® Guide. They comprise specific skills and experiences to ensure accomplishing project goals, and include management of: integration, scope, cost, time, quality, communications, procurement, risk, human resources and stakeholders. This research paper focuses on the ten required KAs for a project manager or a project to be successful. It aims at applying the Softcomputing modeling techniques to describe the relations between the 47 processes and the KAs. Such a model will enable users to predict the overall competencies of the project management. Thus, it provides an assessment tool to envisage, visualize and indicate the overall performance and competency of a project.

The proposed Softcomputing Knowledge Areas Model (SKAM) is a two-stage model. The first stage involves ten models. Each model describes relations between a specific KA and its related processes. The outputs of these ten models will feed into the second stage that will represent the relationship between all the ten KAs and the overall predicted competencies of a project. A combination of Subtractive Clustering and Neurofuzzy modeling techniques are used. Three measures are used to validate the adequacy of the models: the mean average percentage errors, the correlation coefficient and the maximum percentage errors. The highest achieved values for these measures are0.5751, 0.9999 and 4.7283, respectively.

However, although the preliminary findings of the proposed SKAM model are promising, more testing is still required before declaring the adequacy of applying the Softcomputing modeling approach in the project management field.

Keywords: Knowledge Areas, Project Management, PMBOK® Guide, Softcomputing, Modeling.

Abstract: Today, markets are equipped with IT-based systems to facilitate the flow of information within markets and to provide useful information for producers and costumers. Therefore, real time decision making is a significant issue of IT environment for obtaining maximum profit in markets. A valuable tool for real time decision making are Decision Support Systems (DSSs). Here, we propose a DSS to identify a set of optimal markets for a producer. The producer aims to determine the markets that provide more profit for him via information systems of markets that analyze all transactions and prepare reports. Due to these reports the producer would decide about markets that provide the maximum profit. The effectiveness of the proposed integrated model is illustrated through numerical example.

Keywords: Information Technology (IT), Decision Support Systems (DSS), Markets.