Alfredo Candia-Véjar

The optimization of success probability for software projects using genetic algorithms

The software development process is usually affected by many risk factors that may cause the loss of control and failure, thus which need to be identified and mitigated by project managers. Software development companies are currently improving their process by adopting internationally accepted practices, with the aim of avoiding risks and demonstrating the quality of their work. This paper aims to develop a method to identify which risk factors are more influential in determining project outcome. This method must also propose a cost effective investment of project resources to improve the probability of project success. To achieve these aims, we use the probability of success relative to cost to calculate the efficiency of the probable project outcome. The definition of efficiency used in this paper was proposed by researchers in the field of education. We then use this efficiency as the fitness function in an optimization technique based on genetic algorithms. This method maximizes the success probability output of a prediction model relative to cost. The optimization method was tested with several software risk prediction models that have been developed based on the literature and using data from a survey which collected information from in-house and outsourced software development projects in the Chilean software industry. These models predict the probability of success of a project based on the activities undertaken by the project manager and development team. The results show that the proposed method is very useful to identify those activities needing greater allocation of resources, and which of these will have a higher impact on the projects success probability. Therefore using the measure of efficiency has allowed a modular approach to identify those activities in software development on which to focus the projects limited resources to improve its probability of success. The genetic algorithm and the measure of efficiency presented in this paper permit model independence, in both prediction of success and cost evaluation.

Minmax regret combinatorial optimization problems: an Algorithmic Perspective

Uncertainty in optimization is not a new ingredient. Diverse models considering uncertainty have been developed over the last 40 years. In our paper we essentially discuss a particular uncertainty model associated with combinatorial optimization problems, developed in the 90s and broadly studied in the past years. This approach named minmax regret (in particular our emphasis is on the robust deviation criteria) is different from the classical approach for handling uncertainty, stochastic approach , where uncertainty is modeled by assumed probability distributions over the space of all possible scenarios and the objective is to find a solution with good probabilistic performance. In the minmax regret (MMR) approach, the set of all possible scenarios is described deterministically, and the search is for a solution that performs reasonably well for all scenarios, i.e. , that has the best worst-case performance. In this paper we discuss the computational complexity of some classic combinatorial optimization problems using MMR approach, analyze the design of several algorithms for these problems, suggest the study of some specific research problems in this attractive area, and also discuss some applications using this model.

The multiple team formation problem using sociometry

The Team Formation problem (TFP) has become a well-known problem in the OR literature over the last few years. In this problem, the allocation of multiple individuals that match a required set of skills as a group must be chosen to maximise one or several social positive attributes.Specifically, the aim of the current research is two-fold. First, two new dimensions of the TFP are added by considering multiple projects and fractions of peoples dedication. This new problem is named the Multiple Team Formation Problem (MTFP).Second, an optimisation model consisting in a quadratic objective function, linear constraints and integer variables is proposed for the problem. The optimisation model is solved by three algorithms: a Constraint Programming approach provided by a commercial solver, a Local Search heuristic and a Variable Neighbourhood Search metaheuristic. These three algorithms constitute the first attempt to solve the MTFP, being a variable neighbourhood local search metaheuristic the most efficient in almost all cases.Applications of this problem commonly appear in real-life situations, particularly with the current and ongoing development of social network analysis. Therefore, this work opens multiple paths for future research. HighlightsOptimisation of human resource allocation in multiple simultaneous projects.Time-fraction allocations are now allowed.Comparison of CP, LS and VNS algorithm performance.Proposal of multiple options for future research.

On exact solutions for the Minmax Regret Spanning Tree problem

The Minmax Regret Spanning Tree problem is studied in this paper. This is a generalization of the well-known Minimum Spanning Tree problem, which considers uncertainty in the cost function. Particularly, it is assumed that the cost parameter associated with each edge is an interval whose lower and upper limits are known, and the Minmax Regret is the optimization criterion. The Minmax Regret Spanning Tree problem is an NP-Hard optimization problem for which exact and heuristic approaches have been proposed. Several exact algorithms are proposed and computationally compared with the most effective approaches of the literature. It is shown that a proposed branch-and-cut approach outperforms the previous approaches when considering several classes of instances from the literature.

Worst-case performance of Wong's Steiner tree heuristic

A study of the worst-case performance of Wongs heuristic for the Steiner problem in directed networks (SPDN) is presented in this paper.SPDN is a classic combinatorial optimization problem having the status of a very difficult problem (N P-hard problem) and it is known as an optimization model for a broad class of problems in networks. Several exact and heuristic approaches have been designed for SPDN in the last twenty five years.Some papers analyze theoretical and experimental behavior of heuristics for SPDN, specially for undirected networks, but none of these has studied the worst-case performance of Wongs heuristic. In this paper, we find a lower bound for that performance and show that this bound is consistent with comparable results in the literature on SPDN and its undirected version.

Vulnerability Assessment of Spatial Networks: Models and Solutions

In this paper we present a collection of combinatorial optimization problems that allows to assess the vulnerability of spatial networks in the presence of disruptions. The proposed measures of vulnerability along with the model of failure are suitable in many applications where the consideration of failures in the transportation system is crucial. By means of computational results, we show how the proposed methodology allows us to find useful information regarding the capacity of a network to resist disruptions and under which circumstances the network collapses.

Performance Analysis of Algorithms for the Steiner Problem in Directed Networks

Abstract The Steiner problem in directed networks asks for the minimum total weight directed tree spanning a group of nodes of a directed and arc-weighted network. We analize the worst-case performance of several algorithms for this problem and also we discuss the comparability problem between these algorithms. Furthermore, the empirical performance of a proposed hybrid approach is analized for some instances from the simple location problem.

Algorithms for the Minmax Regret Path problem with interval data

Abstract The Shortest Path in networks is an important problem incombinatorial optimization and has many applications in areas like telecommunications and transportation. It is known that this problem is easy to solve in its classic deterministic version, but it is also known that it is an NP-Hard problem for several generalizations. The Shortest Path Problem consists in finding a simple path connecting a source node and a terminal node in an arc-weighted directed network. In some real-world situations the weights are not completely known and then this problem is transformed into an optimization one under uncertainty. It is assumed that an interval estimate is given for each arc length and no further information about the statistical distribution of the weights is known. Uncertainty has been modeled in different ways in optimization. Our aim in this paper is to study the Minmax Regret path with interval data problem by presenting a new exact branch and cut algorithm and, additionally, new heuristics. A set of difficult and large size instances are defined and computational experiments are conducted for the analysis of the different approaches designed to solve the problem. The main contribution of our paper is to provide an assessment of the performance of the proposed algorithms and an empirical evidence of the superiority of a simulated annealing approach based on a new neighborhood over the other heuristics proposed.

Optimization of the harvest planning in the olive oil production: A case study in Chile

Abstract In this work, a mathematical programming model for aiding the decision-making process of olive harvest planning is proposed. The model aims at finding a harvest schedule of different land units that maximizes the total amount of the oil extracted in the mill. Such a harvest plan must ensure quality standards, respect technological limitations, coordinate operations between the field and the mill, and satisfy a budget associated with the harvest operations. Moreover, the presented approach considers the effect of climatological phenomena (rain and frost) during the harvest season, which results in a reduction of olive crops. The model was tested on a real problem of a company located in the central zone of Chile. The experiments with the model show that it is able to obtain better solutions than those obtained by the traditional operation planning when it is tested with real datasets from the company. The optimization model is flexible, allowing the management of several parameters like the project budget and the risks generated by the climate. Thus, it can provide alternative harvest plans in a short time by simulating different climatological scenarios. From a managerial point of view, some lessons about the advantages and difficulties of the model were learned from its use in the company.

On a Class of Interval Data Minmax Regret CO Problems

Some remarks about the Kasperski and Zielinski approximation algorithm for a class of interval data minmax regret combinatorial optimization problems (Algorithm K&Z) are presented. These remarks help to give a better understanding of both the design of the algorithm and its possible applications.