Fuzzy Rules for Risk Assessment and Contingency Estimation within COCOMO Software Project Planning Model

Software development can be considered to be the most uncertain project when compared to other projects due to uncertainty in the customer requirements, the complexity of the process, and the intangible nature of the product. In order to increase the chance of success in managing a software project, the project manager(s) must invest more time and effort in the project planning phase, which involves such primary and integrated activities as effort estimation and risk management, because the accuracy of the effort estimation is highly dependent on the size and number of project risks in a particular software project. However, as is common practice, these two activities are often disconnected from each other and project managers have come to consider such steps to be unreliable due to their lack of accuracy. This chapter introduces the Fuzzy-ExCOM Model, which is used for software project planning and is based on fuzzy technique. It has the capability to not only integrate the effort estimation and risk assessment activities but also to provide information about the estimated effort, the project risks, and the effort contingency allowance necessary to accommodate the identified risk. A validation of this model using the project’s research data shows that this new approach is capable of improving the existing COCOMO estimation performance.

A Feature Selection Approach in the Study of Azorean Proverbs

A paremiologic (study of proverbs) case is presented as part of a wider project based on data collected among the Azorean population. Given the considerable distance between the Azores islands, the authors present the hypothesis that there are significant differences in the proverbs from each island, thus permitting the identification of the native island of the interviewee based on his or her knowledge of proverbs. In this chapter, a feature selection algorithm that combines Rough Sets and the Logical Analysis of Data (LAD) is presented. The algorithm named LAID (Logical Analysis of Inconsistent Data) deals with noisy data, and the authors believe that an important link was established between the two different schools with similar approaches. The algorithm was applied to a real world dataset based on data collected using thousands of interviews of Azoreans, involving an initial set of twenty-two thousand Portuguese proverbs.

A Soft Computing-Based Idea Applied to the Truck and Trailer Routing Problem

Techniques based on Soft Computing are useful to model and solve real-world problems where decision makers use subjective knowledge or linguistic information when making decisions, measuring parameters, objectives, and constraints, and even when modeling the problem. In many problems in transport and logistics, it is necessary to take into account that the available knowledge about some data and parameters of the problem model is imprecise or uncertain. Truck and Trailer Routing Problem, TTRP, is one of most recent and interesting problems in transport routing planning. TTRP is a combinatorial optimization problem, and it is computationally more difficult to solve than the known Vehicle Routing Problem, VRP. Most of models used in the literature assume that the data available is accurate; but this consideration does not correspond with reality. For this reason, it is appropriate to focus research toward defining TTRP models for incorporating the uncertainty present in their data. The aims of the present chapter are: a) to provide a study on the Truck and Trailer Routing Problem that serves as help to researchers interested on this topic and b) to present an approach using techniques of Soft Computing to solve this problem.

Bipolarity in Decision Analysis

Decision analysis, the mechanism by which a final decision is reached in terms of choice (choosing an alternative or a subset of alternatives from a large set of alternatives), ranking (ranking alternatives of a set from the worst to the best), classification (assigning alternatives to some known classes or categories), or sorting (clustering alternatives to form homogeneous classes or categories) is certainly the most pervasive human activity. Some decisions are made routinely and do not need sophisticated algorithms to support decision analysis process whereas other decisions need more or less complex processes to reach a final decision. Methods and models developed to solve decision analysis problems are in constant evolution going from mechanist models of operational research to more sophisticated and soft computing-oriented models that attempt to integrate human attitude (emotion, affect, fear, egoism, altruism, selfishness, etc.). This complex, soft computing and near human mechanism of problem solving is rendered possible thanks to the overwhelming computational power and data storage possibility of modern computers. The purpose of this chapter is to present new and recent developments in decision analysis that attempt to integrate human judgment through bipolarity notion.

Soft Computing in the Quality of Services Evaluation

The evaluation of the Quality of Services (QoS) has been a topic of particular interest to many authors. In the literature, many works have been developed where different models are proposed to assess the QoS in different environments. These models evaluate the QoS from a set of criteria, which may vary from one environment to another, and thus they do not always have the same importance. Considering this, there have been many studies proposing techniques to evaluate the performance of the quality criteria. Techniques have also been developed to obtain the ranking of a given service provider. The purpose of this chapter is to make a literature review of service quality models, methods for determining the weights of the criteria, and the methods used to conduct an overall assessment of service providers.

Hydropower Projects within a Municipal Water Supply System

The interest of those involved in hydroelectricity has been attracted by mini-hydro projects due to their minimal environmental impact and low installation cost. Besides, mini hydros can cooperate with an impressively wide extent of water-related infrastructure, offering a broad potential for investment. In the present chapter, the integrated solution of hydro implementation in water supply systems is presented. Thus, the benefits of a water-supply installation (with constant Q) are extended to energy production. However, defining the optimum operation of such a project is a complicated task, which may involve environmental, hydraulic, technical, and economical parameters. In the present chapter a novel approach is presented, the optimum management of mini hydros in a water supply system with the use of an optimization algorithm (i.e. Harmony Search Algorithm [HAS]). This approach is applied at a site in Northern Greece and is used as a case study of the present chapter.

Modeling and Prediction of Time-Varying Environmental Data Using Advanced Bayesian Methods

The problem of state/parameter estimation represents a key issue in crop models, which are nonlinear, non-Gaussian, and include a large number of parameters. The prediction errors are often important due to uncertainties in the equations, the input variables, and the parameters. The measurements needed to run the model and to perform calibration and validation are sometimes not numerous or known with some uncertainty. In these cases, estimating the state variables and/or parameters from easily obtained measurements can be extremely useful. In this chapter, the authors address the problem of modeling and prediction of time-varying Leaf area index and Soil Moisture (LSM) to better handle nonlinear and non-Gaussian processes without a priori state information. The performances of various conventional and state-of-the-art estimation techniques are compared when they are utilized to achieve this objective. These techniques include the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and the more recently developed technique Variational Bayesian Filter (VF). The original data was issued from experiments carried out on silty soil in Belgium with a wheat crop during two consecutive years, the seasons 2008-09 and 2009-10.

Decision Criteria for Optimal Location of Wind Farms

The objective of the present chapter is to obtain the weights of the criteria that influences a decision problem of vital necessity to the current energy perspectives, which is the optimal localisation of wind farms. The location problem posed presents a hierarchical structure on three levels. The objective or goal to be achieved is in the top level, that is to say the optimal location of wind farms. The second level is constituted by the general criteria that influences the decision and which are the environmental, orographic, location, and climate criteria. These general criteria are then divided into sub-criteria, which constitute the third level of the hierarchy. The information provided by the criteria are of different natures, with qualitative-type criteria coexisting with quantitative-type criteria, and therefore, linguistic labels and numerical values are employed to model, by means of fuzzy triangular numbers, the importance coefficients of the criteria. In order to compare different models for extracting the knowledge, two surveys are prepared based on the Fuzzy AHP methodology, which are submitted to experts in the specific field.

Fuzzy Techniques for Improving Satisfaction in Economic Decisions

The authors use fuzzy set theory to improve classical decision-making problems by incorporating the inherent vagueness in decision-makers’ preferences into the model. They specifically study two representative models: the p-median problem and the portfolio selection problem. The first one is a location problem, which on the one hand fits many real world management situations and on the other hand is suitable for a theoretical analysis of the techniques. The version of the portfolio selection problem presented here is a harder problem, which allows the authors to show the scope of their methods. Some numerical examples are provided to illustrate how fuzzy optimal solutions improve classical ones. Finally, the authors present some results about how fuzzy solutions depend on the membership functions of fuzzy parameters.

Group Genetic Algorithm for Heterogeneous Vehicle Routing

Cost-efficient transportation is a central concern in the transportation and logistics industry. In particular, the Heterogeneous Vehicle Routing Problem (HVRP) has become a major optimization problem in supply chains involved with delivery (collection) of goods to (from) customers. In this problem, there are limited vehicles of different types with respect to capacity, fixed cost, and variable cost. The solution to this problem involves assigning customers to existing vehicles and, in relation to each vehicle, defining the order of visiting each customer for the delivery or collection of goods. Hence, the objective is to minimize the total costs, while satisfying customer requirements and visiting each customer exactly once. In this chapter, an enhanced Group Genetic Algorithm (GGA) based on the group structure of the problem is developed and tested on several benchmark problems. Computational results show that the proposed GGA algorithm is able to produce high quality solutions within a reasonable computation time.