The Experts below are selected from a list of 155916 Experts worldwide ranked by ideXlab platform

Andrew Lim - One of the best experts on this subject based on the ideXlab platform.

  • An enhanced branch-and-Bound Algorithm for the talent scheduling problem
    European Journal of Operational Research, 2016
    Co-Authors: Hu Qin, Zizhen Zhang, Andrew Lim, Xiaocong Liang
    Abstract:

    The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. In this article, we first formulate the problem as an integer linear programming model. Next, we devise a branch-and-Bound Algorithm to solve the problem. The branch-and-Bound Algorithm is enhanced by several accelerating techniques, including preprocessing, dominance rules and caching search states. Extensive experiments over two sets of benchmark instances suggest that our Algorithm is superior to the current best exact Algorithm. Finally, the impacts of different parameter settings, Algorithm components and instance generation distributions are disclosed by some additional experiments.

  • An Enhanced Branch-and-Bound Algorithm for the Talent Scheduling Problem
    arXiv: Artificial Intelligence, 2014
    Co-Authors: Zizhen Zhang, Hu Qin, Xiaocong Liang, Andrew Lim
    Abstract:

    The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. In this article, we first formulate the problem as an integer linear programming model. Next, we devise a branch-and-Bound Algorithm to solve the problem. The branch-and-Bound Algorithm is enhanced by several accelerating techniques, including preprocessing, dominance rules and caching search states. Extensive experiments over two sets of benchmark instances suggest that our Algorithm is superior to the current best exact Algorithm. Finally, the impacts of different parameter settings are disclosed by some additional experiments.

  • IEA/AIE (1) - A Branch-and-Bound Algorithm for the Talent Scheduling Problem
    Modern Advances in Applied Intelligence, 2014
    Co-Authors: Xiaocong Liang, Zizhen Zhang, Hu Qin, Songshan Guo, Andrew Lim
    Abstract:

    The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. We devise a branch-and-Bound Algorithm to solve the problem. A novel lower Bound function is employed to help eliminate the non-promising search nodes. Extensive experiments over the benchmark instances suggest that our branch-and-Bound Algorithm performs better than the currently best exact Algorithm for the talent scheduling problem.

Xiaocong Liang - One of the best experts on this subject based on the ideXlab platform.

  • An enhanced branch-and-Bound Algorithm for the talent scheduling problem
    European Journal of Operational Research, 2016
    Co-Authors: Hu Qin, Zizhen Zhang, Andrew Lim, Xiaocong Liang
    Abstract:

    The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. In this article, we first formulate the problem as an integer linear programming model. Next, we devise a branch-and-Bound Algorithm to solve the problem. The branch-and-Bound Algorithm is enhanced by several accelerating techniques, including preprocessing, dominance rules and caching search states. Extensive experiments over two sets of benchmark instances suggest that our Algorithm is superior to the current best exact Algorithm. Finally, the impacts of different parameter settings, Algorithm components and instance generation distributions are disclosed by some additional experiments.

  • An Enhanced Branch-and-Bound Algorithm for the Talent Scheduling Problem
    arXiv: Artificial Intelligence, 2014
    Co-Authors: Zizhen Zhang, Hu Qin, Xiaocong Liang, Andrew Lim
    Abstract:

    The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. In this article, we first formulate the problem as an integer linear programming model. Next, we devise a branch-and-Bound Algorithm to solve the problem. The branch-and-Bound Algorithm is enhanced by several accelerating techniques, including preprocessing, dominance rules and caching search states. Extensive experiments over two sets of benchmark instances suggest that our Algorithm is superior to the current best exact Algorithm. Finally, the impacts of different parameter settings are disclosed by some additional experiments.

  • IEA/AIE (1) - A Branch-and-Bound Algorithm for the Talent Scheduling Problem
    Modern Advances in Applied Intelligence, 2014
    Co-Authors: Xiaocong Liang, Zizhen Zhang, Hu Qin, Songshan Guo, Andrew Lim
    Abstract:

    The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. We devise a branch-and-Bound Algorithm to solve the problem. A novel lower Bound function is employed to help eliminate the non-promising search nodes. Extensive experiments over the benchmark instances suggest that our branch-and-Bound Algorithm performs better than the currently best exact Algorithm for the talent scheduling problem.

Zizhen Zhang - One of the best experts on this subject based on the ideXlab platform.

  • An enhanced branch-and-Bound Algorithm for the talent scheduling problem
    European Journal of Operational Research, 2016
    Co-Authors: Hu Qin, Zizhen Zhang, Andrew Lim, Xiaocong Liang
    Abstract:

    The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. In this article, we first formulate the problem as an integer linear programming model. Next, we devise a branch-and-Bound Algorithm to solve the problem. The branch-and-Bound Algorithm is enhanced by several accelerating techniques, including preprocessing, dominance rules and caching search states. Extensive experiments over two sets of benchmark instances suggest that our Algorithm is superior to the current best exact Algorithm. Finally, the impacts of different parameter settings, Algorithm components and instance generation distributions are disclosed by some additional experiments.

  • An Enhanced Branch-and-Bound Algorithm for the Talent Scheduling Problem
    arXiv: Artificial Intelligence, 2014
    Co-Authors: Zizhen Zhang, Hu Qin, Xiaocong Liang, Andrew Lim
    Abstract:

    The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. In this article, we first formulate the problem as an integer linear programming model. Next, we devise a branch-and-Bound Algorithm to solve the problem. The branch-and-Bound Algorithm is enhanced by several accelerating techniques, including preprocessing, dominance rules and caching search states. Extensive experiments over two sets of benchmark instances suggest that our Algorithm is superior to the current best exact Algorithm. Finally, the impacts of different parameter settings are disclosed by some additional experiments.

  • IEA/AIE (1) - A Branch-and-Bound Algorithm for the Talent Scheduling Problem
    Modern Advances in Applied Intelligence, 2014
    Co-Authors: Xiaocong Liang, Zizhen Zhang, Hu Qin, Songshan Guo, Andrew Lim
    Abstract:

    The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. We devise a branch-and-Bound Algorithm to solve the problem. A novel lower Bound function is employed to help eliminate the non-promising search nodes. Extensive experiments over the benchmark instances suggest that our branch-and-Bound Algorithm performs better than the currently best exact Algorithm for the talent scheduling problem.

Hu Qin - One of the best experts on this subject based on the ideXlab platform.

  • An enhanced branch-and-Bound Algorithm for the talent scheduling problem
    European Journal of Operational Research, 2016
    Co-Authors: Hu Qin, Zizhen Zhang, Andrew Lim, Xiaocong Liang
    Abstract:

    The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. In this article, we first formulate the problem as an integer linear programming model. Next, we devise a branch-and-Bound Algorithm to solve the problem. The branch-and-Bound Algorithm is enhanced by several accelerating techniques, including preprocessing, dominance rules and caching search states. Extensive experiments over two sets of benchmark instances suggest that our Algorithm is superior to the current best exact Algorithm. Finally, the impacts of different parameter settings, Algorithm components and instance generation distributions are disclosed by some additional experiments.

  • An Enhanced Branch-and-Bound Algorithm for the Talent Scheduling Problem
    arXiv: Artificial Intelligence, 2014
    Co-Authors: Zizhen Zhang, Hu Qin, Xiaocong Liang, Andrew Lim
    Abstract:

    The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. In this article, we first formulate the problem as an integer linear programming model. Next, we devise a branch-and-Bound Algorithm to solve the problem. The branch-and-Bound Algorithm is enhanced by several accelerating techniques, including preprocessing, dominance rules and caching search states. Extensive experiments over two sets of benchmark instances suggest that our Algorithm is superior to the current best exact Algorithm. Finally, the impacts of different parameter settings are disclosed by some additional experiments.

  • IEA/AIE (1) - A Branch-and-Bound Algorithm for the Talent Scheduling Problem
    Modern Advances in Applied Intelligence, 2014
    Co-Authors: Xiaocong Liang, Zizhen Zhang, Hu Qin, Songshan Guo, Andrew Lim
    Abstract:

    The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. We devise a branch-and-Bound Algorithm to solve the problem. A novel lower Bound function is employed to help eliminate the non-promising search nodes. Extensive experiments over the benchmark instances suggest that our branch-and-Bound Algorithm performs better than the currently best exact Algorithm for the talent scheduling problem.

Jose Manuel Tamarit - One of the best experts on this subject based on the ideXlab platform.

  • a branch Bound Algorithm for cutting and packing irregularly shaped pieces
    International Journal of Production Economics, 2013
    Co-Authors: Ramon Alvarezvaldes, Antonio Martinez, Jose Manuel Tamarit
    Abstract:

    Cutting and packing problems involving irregular shapes, usually known as Nesting Problems, are common in industries ranging from clothing and footwear to furniture and shipbuilding. Research publications on these problems are relatively scarce compared with other cutting and packing problems with rectangular shapes, and are focused mostly on heuristic approaches. In this paper we make a systematic study of the problem and develop an exact Branch & Bound Algorithm. The initial existing mixed integer formulations are reviewed, tested and used as a starting point to develop a new and more efficient formulation. We also study several branching strategies, lower Bounds and procedures for fixing variables, reducing the size of the problem to be solved at each node. An extensive computational study allows us first to determine the best strategies to be used in the Branch & Bound Algorithm and then to explore its performance and limits. The results show that the Algorithm is able to solve instances of up to 16 pieces to optimality.

  • a branch and Bound Algorithm for the strip packing problem
    OR Spectrum, 2009
    Co-Authors: Ramon Alvarezvaldes, Francisco Parreno, Jose Manuel Tamarit
    Abstract:

    We propose a new branch and Bound Algorithm for the two dimensional strip packing problem, in which a given set of rectangular pieces have to be packed into a strip of given width and infinite length so as to minimize the required height of the packing. We develop lower Bounds based on integer formulations of relaxations of the problem as well as new Bounds based on geometric considerations, and reduce the tree search with some dominance criteria. An extensive computational study shows the relative efficiency of the Bounds and the good performance of the exact Algorithm.