Abstract
Risk is inherent in all software projects. To manage and control them represents gains for the development and success of projects. The risk management goal is to control, in a continue way, the risks that arise in all the phases of projects, and it's considered determinant to the projects' success. The risk management process is defined through models that specify the activities to accomplish during the project, with the aim to eliminate or minimize the impact of risks. Given the popularity of agile approaches, agile risk management has become central, because agile methodologies by themselves don't give an answer to the risks that might arise in a software project. The work presented in this article has a goal the definition of a risk management model, suited to agile development, in order to improve the existing risk management.

Abstract
Geostatistics has been successfully used to analyse and characterize the spatial variability of environmental properties. Besides providing estimated values at unsampled locations, geostatistics measures the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. This work uses universal block kriging to model and map the spatial distribution of salinity measurements gathered by an Autonomous Underwater Vehicle in a sea outfall monitoring campaign. The aim is to distinguish the effluent plume from the receiving waters, characterizing its spatial variability in the vicinity of the discharge and estimating dilution. The results demonstrate that geostatistical methodology can provide good estimates of the dispersion of effluents, which are valuable in assessing the environmental impact and managing sea outfalls. Moreover, since accurate measurements of the plume's dilution are rare, these studies may be very helpful in the future to validate dispersion models.

Abstract
In recent years several countries have set up policies that allow exchange of kidneys between two or more incompatible patient-donor pairs. These policies lead to what is commonly known as kidney exchange programs. The underlying optimization problems can be formulated as integer programming models. Previously proposed models for kidney exchange programs have exponential numbers of constraints or variables, which makes them fairly difficult to solve when the problem size is large. In this work we propose two compact formulations for the problem, explain how these formulations can be adapted to address some problem variants, and provide results on the dominance of some models over others. Finally we present a systematic comparison between our models and two previously proposed ones via thorough computational analysis. Results show that compact formulations have advantages over non-compact ones when the problem size is large.

Abstract
This paper is addressed to the generalization of simple plant location problem where customer's preferences are taken into account. Some basic polyhedral studies and a new family of facet-defining inequalities are given. The effectiveness of the proposed approach is illustrated by the computational experience.

Abstract
According to previous researches, automated guided vehicles and quay cranes in container terminals have a high potential synergy. In this paper, a mixed integer programming model is formulated to optimize the coordinated scheduling of cranes and vehicles in container terminals. Objectives of the model are to minimize total traveling time of the vehicles and delays in tasks of cranes. A genetic algorithm is developed to solve the problem in reasonable computational time. The most appropriate control parameters for the proposed genetic algorithm are investigated in a medium size numerical test case. It is shown that balanced crossover and mutation rates have the best performance in finding a near optimal solution for the problem. Then, ten small size test cases are solved to evaluate the performance of the proposed optimization methods. The results show the applicability of the genetic algorithm since it can find near optimal solutions, precisely and accurately.

Abstract
Job shop scheduling problem (JSP) with sequence-dependent setup time and re-entrant work flows is considered in this paper. This is an NP-hard problem which needs to be solved using (meta)heuristic methods (e.g. genetic algorithm (GA)), especially for relatively large instances. However, the GA may face premature convergence (i.e. converging to a local optima), especially for rough solution spaces. In this paper, a fuzzy genetic algorithm (FGA) is proposed to overcome this issue. The objective is to minimize makespan of such problem. Research results show that the FGA outperforms the standard GA and offers better solutions in the same number of runs. © 2013 IEEE.