Hybrid metaheuristic approach for handling many objectives and decisions on partial support in project portfolio optimisation

Fernández, Eduardo; Gómez, Consuelo; Rivera, Gilberto; Cruz-Reyes, Laura

doi:10.1016/j.ins.2015.03.064

Cited by 53 publications

(23 citation statements)

References 36 publications

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“…The method is based on monoobjective optimization algorithms that solve problem (2). It exploits the preemptive priority established for the problem and finds the best value for the first objective; then, this value is used as a bound when the second objective is minimized.…”

Section: Optimization Approach Based On a Pda Methodmentioning

confidence: 99%

See 1 more Smart Citation

Robustness Analysis of an Outranking Model Parameters’ Elicitation Method in the Presence of Noisy Examples

Rangel-Valdez

Fernández

Cruz-Reyes

et al. 2018

Mathematical Problems in Engineering

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One of the main concerns in Multicriteria Decision Aid (MCDA) is robustness analysis. Some of the most important approaches to model decision maker preferences are based on fuzzy outranking models whose parameters (e.g., weights and veto thresholds) must be elicited. The so-called preference-disaggregation analysis (PDA) has been successfully carried out by means of metaheuristics, but this kind of works lacks a robustness analysis. Based on the above, the present research studies the robustness of a PDA metaheuristic method to estimate model parameters of an outranking-based relational system of preferences. The method is considered robust if the solutions obtained in the presence of noise can maintain the same performance in predicting preference judgments in a new reference set. The research shows experimental evidence that the PDA method keeps the same performance in situations with up to 10% of noise level, making it robust.

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Section: Optimization Approach Based On a Pda Methodmentioning

confidence: 99%

“…Also, it allows the mapping of a manyobjective problem into a surrogate problem with only three objectives. The method has been applied in a wide variety of problems, including the portfolio problems with many objectives and project partial support [2].…”

Section: Introductionmentioning

confidence: 99%

Robustness Analysis of an Outranking Model Parameters’ Elicitation Method in the Presence of Noisy Examples

Rangel-Valdez

Fernández

Cruz-Reyes

et al. 2018

Mathematical Problems in Engineering

Self Cite

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“…The proposed framework simultaneously considered the accuracy maximization and the complexity minimization objectives. Fernandez et al [29] proposed a nonoutranked ant colony optimization II method for optimizing portfolio problem. The method incorporates integer linear programming to avoid clearly suboptimal regions in the search space and a priori preference system to focus the algorithmic effort on the most preferred region in the search space.…”

Section: Related Workmentioning

confidence: 99%

An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Xiao

Hong

et al. 2018

Complexity

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As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

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“…The ACO meta-heuristic is selected for addressing the problem that this paper is dealing with because many works in the literature attest that the use of ACO is very promising in project management especially in providing near optimal solutions to handle issues that are too expensive computationally. In Fernandez et al (2015), the authors have developed a hybrid approach based mainly on ACO meta-heuristic to handle many objectives in the case of portfolio problems. It provided high-quality portfolios compared with other powerful meta-heuristics that deal with Pareto-front solutions.…”

Section: Related Workmentioning

confidence: 99%

A risk-based approach applied to system engineering projects: A new learning based multi-criteria decision support tool based on an Ant Colony Algorithm

Lachhab

Béler

Coudert

2018

Engineering Applications of Artificial Intelligence

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This is an author-deposited version published in: http://oatao.univ-toulouse.fr/ Eprints ID: 19945 A B S T R A C TThis article proposes a multi-criteria decision support tool fully integrated within system engineering and project management processes that allows decision makers to select an optimal scenario of a project. A model based on an oriented graph includes all the alternative choices of a new system's conception and realization. These choices take into account the risks inherent to perform project tasks in terms of cost and duration. The model of the graph is constructed by considering all the collaborative decisions of the different actors involved in the project. This decision support tool is based on an Ant Colony Algorithm (ACO) for its ability to provide optimal solutions in a reasonable amount of time. The model developed is a multi-objective new ant colony algorithm based on an innovative learning mechanism (named MONACO) that allows ants to learn from their previous choices in order to influence the future ones. The objectives to be minimized are the total cost of the project, its global duration and the risk associated with these criteria. The risk is modeled as an uncertainty related to the increase of the nominal values of cost and duration. The optimization tool is a part of an integrated and more global process, based on industrial standards (the System Engineering process and the Project Management one) that are widely known and used in companies.

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Hybrid metaheuristic approach for handling many objectives and decisions on partial support in project portfolio optimisation

Cited by 53 publications

References 36 publications

Robustness Analysis of an Outranking Model Parameters’ Elicitation Method in the Presence of Noisy Examples

Robustness Analysis of an Outranking Model Parameters’ Elicitation Method in the Presence of Noisy Examples

An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

A risk-based approach applied to system engineering projects: A new learning based multi-criteria decision support tool based on an Ant Colony Algorithm

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