The multi-mode resource-constrained project scheduling problem (MRCPSP) has been confirmed to be an NP-hard problem, and has been widely studied. The particle swarm optimization is an effective method and well applied...
详细信息
ISBN:
(纸本)9781467308946
The multi-mode resource-constrained project scheduling problem (MRCPSP) has been confirmed to be an NP-hard problem, and has been widely studied. The particle swarm optimization is an effective method and well applied to solve all kind of schedulingproblems. MRCPSP is regarded as two sub-problems: the activity mode selection and the activity priority sub-problems. Therefore, discrete version PSO and conventional PSO were used for solving these two sub-problems respectively. In discrete version PSO, the velocity update rule based on constriction PSO is applied. Meanwhile, an inverse S curve based inertia weight adjustment mechanism was proposed to enhance both the global search and local search to improve search efficiency. Moreover, a grouped communication topology was designed to avoid premature convergence and slow convergence problems, i.e., to balance convergence rate. Instances of MRCPSP in PSPLIB were tested to verify the performance of the proposed scheme. The experimental results confirmed that the proposed scheme is effective and efficient for solving MRCPSP type schedulingproblems.
The particle swarm optimization (PSO) has been widely used to solve continuous problems. The discrete problems have just begun to be also solved by the discrete PSO. However, the combinatorial problems remain a prohib...
详细信息
The particle swarm optimization (PSO) has been widely used to solve continuous problems. The discrete problems have just begun to be also solved by the discrete PSO. However, the combinatorial problems remain a prohibitive area to the PSO mainly in case of integer values. In this paper, we propose a combinatorial PSO (CPSO) algorithm that we take up challenge to use in order to solve a multi-mode resource-constrained project scheduling problem (MRCPSP). The results that have been obtained using a standard set of instances, after extensive experiments, prove to be very competitive in terms of number of problems solved to optimality. By comparing average deviations and percentages of optima found, our CPSO algorithm outperforms the simulated annealing algorithm and it is close to the PSO algorithm. (C) 2007 Elsevier Inc. All rights reserved.
In this paper, a multi-moderesource-con strained projectschedulingproblem with schedule-dependent setup times is considered. A schedule-dependent setup time is defined as a setup time dependent on the assignment of...
详细信息
In this paper, a multi-moderesource-con strained projectschedulingproblem with schedule-dependent setup times is considered. A schedule-dependent setup time is defined as a setup time dependent on the assignment of resources to activities over time, when resources are, e.g., placed in different locations. In such a case, the time necessary to prepare the required resource for processing an activity depends not only on the sequence of activities but, more generally, on the locations in which successive activities are executed. Activities are non-preemptable, resources are renewable, and the objective is to minimize the project duration. A local search metaheuristic-tabu search is proposed to solve this strongly NP-hard problem, and it is compared with the multi-start iterative improvement method as well as with random sampling. A computational experiment is described, performed on a set of instances based on standard test problems constructed by the ProGen project generator. The algorithms are computationally compared, the results are analyzed and discussed, and some conclusions are given. (C) 2006 Elsevier B.V. All rights reserved.
暂无评论