A survey of hybrid metaheuristics for the resource-constrained project scheduling problem

Pellerin, Robert; Perrier, Nathalie; Berthaut, François

doi:10.1016/j.ejor.2019.01.063

Cited by 215 publications

(90 citation statements)

References 83 publications

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“…The paper Pellerin et al (2020) examines the general tendency of moving from pure metaheuristic methods to solving the RCPSP to hybrid methods that rely on different metaheuristic strategies.…”

Section: Project Planningmentioning

confidence: 99%

Peer Review #2 of "Approaches combining methods of Operational Research with Business Process Model and Notation: A systematic review (v0.2)"

2020

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Section: Project Planningmentioning

confidence: 99%

Peer Review #2 of "Approaches combining methods of Operational Research with Business Process Model and Notation: A systematic review (v0.2)"

2020

View full text Add to dashboard Cite

“…Recently, a great interest has been given to using some artificial tools in optimization. Metaheuristics play a great role in either real-life simulation or invoking intelligent learned procedures [15][16][17][18][19][20][21][22][23][24]. Metaheuristics exhibit good degrees of robustness for a wide range of problems.…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based EDAs for Stochastic Programming Problems

2020

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With the rapid growth of simulation software packages, generating practical tools for simulation-based optimization has attracted a lot of interest over the last decades. In this paper, a modified method of Estimation of Distribution Algorithms (EDAs) is constructed by a combination with variable-sample techniques to deal with simulation-based optimization problems. Moreover, a new variable-sample technique is introduced to support the search process whenever the sample sizes are small, especially in the beginning of the search process. The proposed method shows efficient results by simulating several numerical experiments.

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“…Recently, the use of artificial intelligence methods in optimization has been of great interest. Metaheuristics play a significant role in both real-life simulations and invoking smart methods [18][19][20][21][22][23][24]. Metaheuristics show strong validity rates across a wide variety of applications.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems

2020

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Generating practical methods for simulation-based optimization has attracted a great deal of attention recently. In this paper, the estimation of distribution algorithms are used to solve nonlinear continuous optimization problems that contain noise. One common approach to dealing with these problems is to combine sampling methods with optimal search methods. Sampling techniques have a serious problem when the sample size is small, so estimating the objective function values with noise is not accurate in this case. In this research, a new sampling technique is proposed based on fuzzy logic to deal with small sample sizes. Then, simulation-based optimization methods are designed by combining the estimation of distribution algorithms with the proposed sampling technique and other sampling techniques to solve the stochastic programming problems. Moreover, additive versions of the proposed methods are developed to optimize functions without noise in order to evaluate different efficiency levels of the proposed methods. In order to test the performance of the proposed methods, different numerical experiments were carried out using several benchmark test functions. Finally, three real-world applications are considered to assess the performance of the proposed methods.

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A survey of hybrid metaheuristics for the resource-constrained project scheduling problem

Cited by 215 publications

References 83 publications

Peer Review #2 of "Approaches combining methods of Operational Research with Business Process Model and Notation: A systematic review (v0.2)"

Peer Review #2 of "Approaches combining methods of Operational Research with Business Process Model and Notation: A systematic review (v0.2)"

Simulation-Based EDAs for Stochastic Programming Problems

Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems

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