8). The computing paradigms that support soft computing methods include fuzzy logic and genetic algorithms (Castellano & Fanelli, 2009)
, number of cycles, time passed, good score) (Roudier, 2007). Genetic algorithms have also been integrated with rule-based reasoning techniques in finding solutions for highly complex decisions (Desouza, 2002)
Acceleration and deceleration rate of the LV. Therefore, the consequence of the fuzzy rules in this fuzzy inference system is in terms of acceleration/deceleration rate of the following vehicle (Dewitte & Willey, 1995)
Furthermore, the portfolio manager maps and tracks the project execution against the strategies and raises the red flag when there is danger of missing a goal. Finally, the portfolio manager also lets strategic planning know when the strategy is not practical relative to project resources available (Dinsmore & Cabanis-Brown, 2011, p
Despite the need, though, the majority of organizations in the United States do not possess the information to effectively manage their strategic initiatives because the requisite data is stored away in organizational silos (Project and portfolio management, 2014). The need for effective project portfolio management has become even more pronounced as the number and complexity of information technology projects has increased in recent years (Drake & Byrd, 2006)
1). To be effective, though, project portfolio management requires more than the correct selection of projects because it is also necessary to take into account the respective collective as well as individual contributions of selected projects with organizational objectives in order to make appropriate decisions concerning portfolio balancing and the identification of gaps in achieving objectives (Enoch, 2010)
, 2006). As shown in Figure 1 below, a fuzzy rule-based system is comprised of the following: A Rule Base (RB) of fuzzy rules A Data Base (DB) of linguistic terms and their membership functions Together the RB and DB are the knowledge base (KB) A fuzzy inference system which maps from fuzzy inputs to a fuzzy output Fuzzification and defuzzification processes (Kovacs, 2012)
Building the Fuzzy Inference System Solution As noted in the introductory chapter, this study proposed a solution to accurately estimate completion time and cost for project tasks. The solution is based on a fuzzy inference system which is based on fuzzy logic (Guan & Zurada, 2008)
2). Fuzzy inference systems have been widely applied to simulate processes or for control (Guillaume, 2001)
223). This same process applies to fuzzy sets which are collections of couples of elements (Hamzeh et al
The pioneer of modern chaos theory is a meteorologist, Edward Lorenz. Weather conditions represent an appropriate application of chaos theory because of the enormous range of variables that can influence systems in different parts of the world (Khalil, 1997)
In other words, "To make an inference is to perform an act: It is to do something. Again, however, it is difficult to tell when an act conforms to the canons of logic" (Kyburg & Choh, 2001, p
(2012), project portfolio selection is one of the most important decision-making problems for most organizations in project management and engineering management. Usually project portfolio decisions are very complicated when project interactions in terms of multiple selection criteria and preference information of decision makers in terms of the criteria importance are taken into consideration simultaneously (Lean et al
There are two functions that comprise IT project portfolio management: (a) the planning of new projects and migration to new systems and (b) reassessing ongoing projects (Drake & Byrd, 2006). The first function, the planning phase, can still be initiated using strategic information system planning which is "the process of identifying which computer based applications that will assist an organization in executing its business plans and realizing its business goals" (Lederer & Sethi, 1988, p
3). Even though the majority of real-time scheduling algorithms focus on constraints involving timing, there are some other constraints in the environment that must be considered as well (Lee, Tiao & Yen, 1994)
25). By using off-the-shelf fuzzy toolboxes provided by software vendors such as Brainmaker and MATLAB, it is possible for practitioners to develop fuzzy inference systems that accurately model phenomenon of interest (Malhotra & Malhotra, 1999)
Recently, there have been some new developments in how optimization models are formulated, solved, and applied, coming from outside of the traditional optimization field. Examples include genetic algorithms, swarm optimization, and constraint programming (North & Macal, 2006)
propose the use of multi-objective evolutionary algorithms (EA), and they offer comparison of several EA oriented approaches for optimization of persistent search queries. Among the aforesaid studies the works of Kraft and Cordon offer more promises still (Owais, 2006)
69). In other cases, some companies may fully appreciate the importance of effective project portfolio management but remain uncertain concerning how to implement it in a fashion that proves this value to organizational decision-makers (Pennypacker, 2010)
' (p. 55) The successful application of fuzzy logical to a wide range of engineering problems has resulted in increased interest in applying fuzzy logic to more complicated "expert systems" that require formulating a diagnosis or recommending a certain course of action (Peterson, 2009)