The Experts below are selected from a list of 3989424 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.

Marco Pranzo - One of the best experts on this subject based on the ideXlab platform.

  • a branch and bound Algorithm for scheduling trains in a railway network
    European Journal of Operational Research, 2007
    Co-Authors: Andrea Dariano, Dario Pacciarelli, Marco Pranzo
    Abstract:

    The paper studies a train scheduling problem faced by railway infrastructure managers during real-time traffic control. When train operations are perturbed, a new conflict-free timetable of feasible arrival and departure times needs to be re-computed, such that the deviation from the original one is minimized. The problem can be viewed as a huge job shop scheduling problem with no-store constraints. We make use of a careful estimation of time separation among trains, and model the scheduling problem with an alternative graph formulation. We develop a branch and bound Algorithm which includes implication rules enabling to speed up the computation. An experimental study, based on a bottleneck area of the Dutch rail network, shows that a truncated version of the Algorithm provides proven optimal or near optimal solutions within short time limits.

John L. Hunsucker - One of the best experts on this subject based on the ideXlab platform.

  • BRANCH AND BOUND Algorithm FOR THE FLOW SHOP WITH MULTIPLE PROCESSORS
    European Journal of Operational Research, 1991
    Co-Authors: Shaukat A. Brah, John L. Hunsucker
    Abstract:

    Abstract The sequencing of a flow shop with multiple processors at each stage is a general case of the flow shop problem. It involves sequencing n jobs in a flow shop where, for at least one stage, the facility has one or more identical machines. The purpose of this paper is to present a branch and bound Algorithm to solve scheduling problems of such facilities for optimizing the maximum completion time. The lower bounds and elimination rules developed in this research are based upon the generalization of the flow shop problem. Furthermore, a computational Algorithm, along with results, is also presented. The branch and bound Algorithm can also be used to optimize other measures of performance in a flow shop with multiple processors.

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.