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Mauricio G. C. Resende - One of the best experts on this subject based on the ideXlab platform.

  • A random key based genetic algorithm for the resource constrained project scheduling problem
    Computers & Operations Research, 2020
    Co-Authors: Jorge José De Magalhães Mendes, Jose Fernando Goncalves, Mauricio G. C. Resende
    Abstract:

    This paper presents a genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The Chromosome Representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities of the activities are defined by the genetic algorithm. The heuristic generates parameterized active schedules. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

  • A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem
    Journal of Heuristics, 2011
    Co-Authors: Jose Fernando Goncalves, Mauricio G. C. Resende, Jorge J. M. Mendes
    Abstract:

    This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The Chromosome Representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The Chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

  • A genetic algorithm for the resource constrained multi-project scheduling problem
    European Journal of Operational Research, 2008
    Co-Authors: Jose Fernando Goncalves, Jorge José De Magalhães Mendes, Mauricio G. C. Resende
    Abstract:

    This paper presents a genetic algorithm for the resource constrained multi-project scheduling problem. The Chromosome Representation of the problem is based on random keys. The schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm. The approach is tested on a set of randomly generated problems. The computational results validate the effectiveness of the proposed algorithm.

  • A hybrid genetic algorithm for the job shop scheduling problem
    European Journal of Operational Research, 2005
    Co-Authors: Jose Fernando Goncalves, Jorge José De Magalhães Mendes, Mauricio G. C. Resende
    Abstract:

    This paper presents a hybrid genetic algorithm for the job shop scheduling problem. The Chromosome Representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

  • A hybrid genetic algorithm for the job shop scheduling problem
    European Journal of Operational Research, 2005
    Co-Authors: Jose Fernando Goncalves, Jorge José De Magalhães Mendes, Mauricio G. C. Resende
    Abstract:

    This paper presents a hybrid genetic algorithm for the job shop scheduling problem. The Chromosome Representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm. © 2004 Elsevier B.V. All rights reserved.

Jose Fernando Goncalves - One of the best experts on this subject based on the ideXlab platform.

  • A random key based genetic algorithm for the resource constrained project scheduling problem
    Computers & Operations Research, 2020
    Co-Authors: Jorge José De Magalhães Mendes, Jose Fernando Goncalves, Mauricio G. C. Resende
    Abstract:

    This paper presents a genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The Chromosome Representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities of the activities are defined by the genetic algorithm. The heuristic generates parameterized active schedules. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

  • A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem
    Journal of Heuristics, 2011
    Co-Authors: Jose Fernando Goncalves, Mauricio G. C. Resende, Jorge J. M. Mendes
    Abstract:

    This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The Chromosome Representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The Chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

  • A genetic algorithm for the resource constrained multi-project scheduling problem
    European Journal of Operational Research, 2008
    Co-Authors: Jose Fernando Goncalves, Jorge José De Magalhães Mendes, Mauricio G. C. Resende
    Abstract:

    This paper presents a genetic algorithm for the resource constrained multi-project scheduling problem. The Chromosome Representation of the problem is based on random keys. The schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm. The approach is tested on a set of randomly generated problems. The computational results validate the effectiveness of the proposed algorithm.

  • A hybrid genetic algorithm for the job shop scheduling problem
    European Journal of Operational Research, 2005
    Co-Authors: Jose Fernando Goncalves, Jorge José De Magalhães Mendes, Mauricio G. C. Resende
    Abstract:

    This paper presents a hybrid genetic algorithm for the job shop scheduling problem. The Chromosome Representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

  • A hybrid genetic algorithm for the job shop scheduling problem
    European Journal of Operational Research, 2005
    Co-Authors: Jose Fernando Goncalves, Jorge José De Magalhães Mendes, Mauricio G. C. Resende
    Abstract:

    This paper presents a hybrid genetic algorithm for the job shop scheduling problem. The Chromosome Representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm. © 2004 Elsevier B.V. All rights reserved.

Chie Wun Chiou - One of the best experts on this subject based on the ideXlab platform.

  • a comparison of two Chromosome Representation schemes used in solving a family based scheduling problem
    Robotics and Computer-integrated Manufacturing, 2013
    Co-Authors: Chen Fu Chen, Yi Hsun Li, Muh-cherng Wu, Chie Wun Chiou
    Abstract:

    Meta-heuristic algorithms have been widely used in solving scheduling problems; previous studies focused on enhancing existing algorithmic mechanisms. This study advocates a new perspective-developing new Chromosome (solution) Representation schemes may improve the performance of existing meta-heuristic algorithms. In the context of a scheduling problem, known as permutation manufacturing-cell flow shop (PMFS), we compare the effectiveness of two Chromosome Representation schemes (S"o"l"d and S"n"e"w) while they are embedded in a meta-heuristic algorithm to solve the PMFS scheduling problem. Two existing meta-heuristic algorithms, genetic algorithm (GA) and ant colony optimization (ACO), are tested. Denote a tested meta-heuristic algorithm by X_Y, where X represents an algorithmic mechanism and Y represents a Chromosome Representation. Experiment results indicate that GA_ S"n"e"w outperforms GA_S"o"l"d, and ACO_S"n"e"w also outperforms ACO_S"o"l"d. These findings reveal the importance of developing new Chromosome Representations in the application of meta-heuristic algorithms.

  • A comparison of two Chromosome Representation schemes used in solving a family-based scheduling problem
    Robotics and Computer-Integrated Manufacturing, 2013
    Co-Authors: Chen Fu Chen, Yi Hsun Li, Pang Hao Tai, Muh-cherng Wu, Chie Wun Chiou
    Abstract:

    Meta-heuristic algorithms have been widely used in solving scheduling problems; previous studies focused on enhancing existing algorithmic mechanisms. This study advocates a new perspective-developing new Chromosome (solution) Representation schemes may improve the performance of existing meta-heuristic algorithms. In the context of a scheduling problem, known as permutation manufacturing-cell flow shop (PMFS), we compare the effectiveness of two Chromosome Representation schemes (Soldand Snew) while they are embedded in a meta-heuristic algorithm to solve the PMFS scheduling problem. Two existing meta-heuristic algorithms, genetic algorithm (GA) and ant colony optimization (ACO), are tested. Denote a tested meta-heuristic algorithm by X-Y, where X represents an algorithmic mechanism and Y represents a Chromosome Representation. Experiment results indicate that GA- Snewoutperforms GA-Sold, and ACO-Snewalso outperforms ACO-Sold. These findings reveal the importance of developing new Chromosome Representations in the application of meta-heuristic algorithms. © 2012 Elsevier Ltd. All rights reserved.

Chen Fu Chen - One of the best experts on this subject based on the ideXlab platform.

  • A genetic algorithm embedded with a concise Chromosome Representation for distributed and flexible job-shop scheduling problems
    Journal of Intelligent Manufacturing, 2018
    Co-Authors: Po-hsiang Lu, Muh-cherng Wu, Yong-han Peng, Chen Fu Chen
    Abstract:

    This paper proposes a genetic algorithm $$GA\_JS$$ G A _ J S for solving distributed and flexible job-shop scheduling (DFJS) problems. A DFJS problem involves three scheduling decisions: (1) job-to-cell assignment, (2) operation-sequencing, and (3) operation-to-machine assignment. Therefore, solving a DFJS problem is essentially a 3-dimensional solution space search problem; each dimension represents a type of decision. The $$GA\_JS$$ G A _ J S algorithm is developed by proposing a new and concise Chromosome Representation $${\varvec{S}}_{{\varvec{JOB}}}$$ S J O B , which models a 3-dimensional scheduling solution by a 1-dimensional scheme (i.e., a sequence of all jobs to be scheduled). That is, the Chromosome space is 1-dimensional (1D) and the solution space is 3-dimensional (3D). In $$GA\_JS$$ G A _ J S , we develop a 1D-to-3D decoding method to convert a 1D Chromosome into a 3D solution. In addition, given a 3D solution, we use a refinement method to improve the scheduling performance and subsequently use a 3D-to-1D encoding method to convert the refined 3D solution into a 1D Chromosome. The 1D-to-3D decoding method is designed to obtain a “good” 3D solution which tends to be load-balanced . In contrast, the refinement and 3D-to-1D encoding methods of a 3D solution provides a novel way (rather than by genetic operators) to generate new Chromosomes, which are herein called shadow Chromosomes . Numerical experiments indicate that $$GA\_JS$$ G A _ J S outperforms the IGA developed by De Giovanni and Pezzella (Eur J Oper Res 200:395–408, 2010 ), which is the up-to-date best-performing genetic algorithm in solving DFJS problems.

  • a comparison of two Chromosome Representation schemes used in solving a family based scheduling problem
    Robotics and Computer-integrated Manufacturing, 2013
    Co-Authors: Chen Fu Chen, Yi Hsun Li, Muh-cherng Wu, Chie Wun Chiou
    Abstract:

    Meta-heuristic algorithms have been widely used in solving scheduling problems; previous studies focused on enhancing existing algorithmic mechanisms. This study advocates a new perspective-developing new Chromosome (solution) Representation schemes may improve the performance of existing meta-heuristic algorithms. In the context of a scheduling problem, known as permutation manufacturing-cell flow shop (PMFS), we compare the effectiveness of two Chromosome Representation schemes (S"o"l"d and S"n"e"w) while they are embedded in a meta-heuristic algorithm to solve the PMFS scheduling problem. Two existing meta-heuristic algorithms, genetic algorithm (GA) and ant colony optimization (ACO), are tested. Denote a tested meta-heuristic algorithm by X_Y, where X represents an algorithmic mechanism and Y represents a Chromosome Representation. Experiment results indicate that GA_ S"n"e"w outperforms GA_S"o"l"d, and ACO_S"n"e"w also outperforms ACO_S"o"l"d. These findings reveal the importance of developing new Chromosome Representations in the application of meta-heuristic algorithms.

  • A comparison of two Chromosome Representation schemes used in solving a family-based scheduling problem
    Robotics and Computer-Integrated Manufacturing, 2013
    Co-Authors: Chen Fu Chen, Yi Hsun Li, Pang Hao Tai, Muh-cherng Wu, Chie Wun Chiou
    Abstract:

    Meta-heuristic algorithms have been widely used in solving scheduling problems; previous studies focused on enhancing existing algorithmic mechanisms. This study advocates a new perspective-developing new Chromosome (solution) Representation schemes may improve the performance of existing meta-heuristic algorithms. In the context of a scheduling problem, known as permutation manufacturing-cell flow shop (PMFS), we compare the effectiveness of two Chromosome Representation schemes (Soldand Snew) while they are embedded in a meta-heuristic algorithm to solve the PMFS scheduling problem. Two existing meta-heuristic algorithms, genetic algorithm (GA) and ant colony optimization (ACO), are tested. Denote a tested meta-heuristic algorithm by X-Y, where X represents an algorithmic mechanism and Y represents a Chromosome Representation. Experiment results indicate that GA- Snewoutperforms GA-Sold, and ACO-Snewalso outperforms ACO-Sold. These findings reveal the importance of developing new Chromosome Representations in the application of meta-heuristic algorithms. © 2012 Elsevier Ltd. All rights reserved.

Dongxia Chang - One of the best experts on this subject based on the ideXlab platform.

  • a novel genetic clustering algorithm with variable length Chromosome Representation
    Genetic and Evolutionary Computation Conference, 2014
    Co-Authors: Mingan Zhang, Yong Deng, Dongxia Chang
    Abstract:

    The paper proposed a new genetic clustering algorithm with variable-length Chromosome Representation (GCVCR), which can automatically evolve and find the optimal number of clusters as well as proper cluster centers of the data set. A new clustering criterion based on message passing between data points and the candidate centers described by the Chromosome are presented to make the clustering problem more effective. The simulation results show the effectiveness of the proposed algorithm.

  • GECCO (Companion) - A novel genetic clustering algorithm with variable-length Chromosome Representation
    Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14, 2014
    Co-Authors: Mingan Zhang, Yong Deng, Dongxia Chang
    Abstract:

    The paper proposed a new genetic clustering algorithm with variable-length Chromosome Representation (GCVCR), which can automatically evolve and find the optimal number of clusters as well as proper cluster centers of the data set. A new clustering criterion based on message passing between data points and the candidate centers described by the Chromosome are presented to make the clustering problem more effective. The simulation results show the effectiveness of the proposed algorithm.