The Experts below are selected from a list of 29556 Experts worldwide ranked by ideXlab platform

Kleanthis Sirakoulis - One of the best experts on this subject based on the ideXlab platform.

  • the effectiveness of Resource levelling tools for Resource Constraint project scheduling problem
    International Journal of Project Management, 2009
    Co-Authors: A Kastor, Kleanthis Sirakoulis
    Abstract:

    Abstract The PERT/CPM network techniques are based on the assumption that all needed Resources will be available. The scarcity of Resources is a usual reason for project delays. For the solution of the Resource Constrained Project Scheduling Problem (RCPSP) other methods have been applied. The objective of these methods consists in minimizing the project’s duration by considering both the precedence and Resource Constraints. Project Management software packages solve the Resource conflicts using Resource levelling. The paper evaluates the effectiveness of Resource levelling tools of three popular packages by comparing the results when levelling two real construction projects as case studies.

Jinxing Xie - One of the best experts on this subject based on the ideXlab platform.

  • LOT-SIZING RULE AND FREEZING THE MASTER PRODUCTION SCHEDULE UNDER CAPACITY Constraint AND DETERMINISTIC DEMAND
    Production and Operations Management, 2009
    Co-Authors: Xiande Zhao, Jinxing Xie, Qiyuan Jiang
    Abstract:

    This paper investigates the performance impact of lot-sizing rule (LSR) selection and freezing of the master production schedule (MPS) in multi-item single-level systems with a single Resource Constraint under deterministic demand. The results of the study show that the selection of LSRS and the parameters for freezing the MPS have a significant impact on total cost, schedule instability, and the service level of the system. However, the selection of LSRS does not significantly influence the selection of the MPS freezing parameters. The basic conclusions concerning the performance of the freezing parameters under a capacity Constraint agreed with previous research findings without consideration of capacity Constraints.

  • Freezing the master production schedule under single Resource Constraint and demand uncertainty
    International Journal of Production Economics, 2003
    Co-Authors: Jinxing Xie, Xiande Zhao, T. S. Lee
    Abstract:

    Abstract This paper investigates the impact of freezing the master production schedule (MPS) in multi-item single-level systems with a single Resource Constraint under demand uncertainty. It also examines the impact of environmental factors on the selection of MPS freezing parameters. A computer model is built to simulate master production scheduling activities in a multi-item system under a rolling time horizon. The result of the study shows that the parameters for freezing the MPS have a significant impact on total cost, schedule instability and the service level of the system. Furthermore, the selection of freezing parameters is also significantly influenced by some environmental factors such as capacity tightness and cost structure. While some findings concerning the performance of MPS freezing parameters without capacity Constraints can be generalised to the case of limited capacity, other conclusions under capacity Constraints are different from those without capacity Constraints.

Peter J Stuckey - One of the best experts on this subject based on the ideXlab platform.

  • explaining time table edge finding propagation for the cumulative Resource Constraint
    Integration of AI and OR Techniques in Constraint Programming, 2013
    Co-Authors: Andreas Schutt, Thibaut Feydy, Peter J Stuckey
    Abstract:

    Cumulative Resource Constraints can model scarce Resources in scheduling problems or a dimension in packing and cutting problems.In order to efficiently solve such problems with a Constraint programming solver, it is important to have strong and fast propagators for cumulative Resource Constraints. Time-table-edge-finding propagators are a recent development in cumulative propagators, that combine the current Resource profile (time-table) during the edge-finding propagation. The current state of the art for solving scheduling and cutting problems involving cumulative Constraints are lazy clause generation solvers, i.e., Constraint programming solvers incorporating nogood learning, have proved to be excellent at solving scheduling and cutting problems. For such solvers, concise and accurate explanations of the reasons for propagation are essential for strong nogood learning. In this paper, we develop a time-table-edge-finding propagator for cumulative that explains its propagations. We give results using this propagator in a lazy clause generation system on Resource-constrained project scheduling problems from various standard benchmark suites. On the standard benchmark suite PSPLib, we are able to improve the lower bound of about 60% of the remaining open instances, and close 6 open instances.

  • explaining time table edge finding propagation for the cumulative Resource Constraint
    arXiv: Artificial Intelligence, 2012
    Co-Authors: Andreas Schutt, Thibaut Feydy, Peter J Stuckey
    Abstract:

    Cumulative Resource Constraints can model scarce Resources in scheduling problems or a dimension in packing and cutting problems. In order to efficiently solve such problems with a Constraint programming solver, it is important to have strong and fast propagators for cumulative Resource Constraints. One such propagator is the recently developed time-table-edge-finding propagator, which considers the current Resource profile during the edge-finding propagation. Recently, lazy clause generation solvers, i.e. Constraint programming solvers incorporating nogood learning, have proved to be excellent at solving scheduling and cutting problems. For such solvers, concise and accurate explanations of the reasons for propagation are essential for strong nogood learning. In this paper, we develop the first explaining version of time-table-edge-finding propagation and show preliminary results on Resource-constrained project scheduling problems from various standard benchmark suites. On the standard benchmark suite PSPLib, we were able to close one open instance and to improve the lower bound of about 60% of the remaining open instances. Moreover, 6 of those instances were closed.

T. S. Lee - One of the best experts on this subject based on the ideXlab platform.

  • Freezing the master production schedule under single Resource Constraint and demand uncertainty
    International Journal of Production Economics, 2003
    Co-Authors: Jinxing Xie, Xiande Zhao, T. S. Lee
    Abstract:

    Abstract This paper investigates the impact of freezing the master production schedule (MPS) in multi-item single-level systems with a single Resource Constraint under demand uncertainty. It also examines the impact of environmental factors on the selection of MPS freezing parameters. A computer model is built to simulate master production scheduling activities in a multi-item system under a rolling time horizon. The result of the study shows that the parameters for freezing the MPS have a significant impact on total cost, schedule instability and the service level of the system. Furthermore, the selection of freezing parameters is also significantly influenced by some environmental factors such as capacity tightness and cost structure. While some findings concerning the performance of MPS freezing parameters without capacity Constraints can be generalised to the case of limited capacity, other conclusions under capacity Constraints are different from those without capacity Constraints.

Xiande Zhao - One of the best experts on this subject based on the ideXlab platform.

  • LOT-SIZING RULE AND FREEZING THE MASTER PRODUCTION SCHEDULE UNDER CAPACITY Constraint AND DETERMINISTIC DEMAND
    Production and Operations Management, 2009
    Co-Authors: Xiande Zhao, Jinxing Xie, Qiyuan Jiang
    Abstract:

    This paper investigates the performance impact of lot-sizing rule (LSR) selection and freezing of the master production schedule (MPS) in multi-item single-level systems with a single Resource Constraint under deterministic demand. The results of the study show that the selection of LSRS and the parameters for freezing the MPS have a significant impact on total cost, schedule instability, and the service level of the system. However, the selection of LSRS does not significantly influence the selection of the MPS freezing parameters. The basic conclusions concerning the performance of the freezing parameters under a capacity Constraint agreed with previous research findings without consideration of capacity Constraints.

  • Freezing the master production schedule under single Resource Constraint and demand uncertainty
    International Journal of Production Economics, 2003
    Co-Authors: Jinxing Xie, Xiande Zhao, T. S. Lee
    Abstract:

    Abstract This paper investigates the impact of freezing the master production schedule (MPS) in multi-item single-level systems with a single Resource Constraint under demand uncertainty. It also examines the impact of environmental factors on the selection of MPS freezing parameters. A computer model is built to simulate master production scheduling activities in a multi-item system under a rolling time horizon. The result of the study shows that the parameters for freezing the MPS have a significant impact on total cost, schedule instability and the service level of the system. Furthermore, the selection of freezing parameters is also significantly influenced by some environmental factors such as capacity tightness and cost structure. While some findings concerning the performance of MPS freezing parameters without capacity Constraints can be generalised to the case of limited capacity, other conclusions under capacity Constraints are different from those without capacity Constraints.