In this paper we propose an estimation of distribution algorithm (EDA) to solve the stochastic resource-constrained project scheduling problem. The algorithm employs a novel probability model as well as a permutation-...
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In this paper we propose an estimation of distribution algorithm (EDA) to solve the stochastic resource-constrained project scheduling problem. The algorithm employs a novel probability model as well as a permutation-based local search. In a comprehensive computational study, we scrutinize the performance of EDA on a set of widely used benchmark instances. Thereby, we analyze the impact of different problem parameters as well as the variance of activity durations. By benchmarking EDA with state-of-the-art algorithms, we can show that its performance compares very favorably to the latter, with a clear dominance in instances with medium to high variance of activity duration.
An ordinal chemical reaction optimization(OCRO) is proposed in this paper to solve the stochastic resource-constrained project scheduling problem(SRCPSP).First,the main elements of chemical reaction optimization(CRO) ...
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ISBN:
(纸本)9781479900305
An ordinal chemical reaction optimization(OCRO) is proposed in this paper to solve the stochastic resource-constrained project scheduling problem(SRCPSP).First,the main elements of chemical reaction optimization(CRO) are modified for searching good scheduling solutions ***,optimal computing budget allocation(OCBA) is employed to perform evaluation *** testing results are provided based on the well-known PSPLIB considering certain probability distributions,and the comparisons with some state-of-the-art algorithms are given as *** effectiveness of using OCBA in CRO is *** it also shows that the OCRO is effective in solving the problems with high variance.
Aiming to minimize the average project duration, a discrete-event simulation (DES) approach with multiple-comparison procedure is presented to solve the stochastic resource-constrained project scheduling problem (SRCP...
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Aiming to minimize the average project duration, a discrete-event simulation (DES) approach with multiple-comparison procedure is presented to solve the stochastic resource-constrained project scheduling problem (SRCPSP). The simulation model of SRCPSP is composed of a resource management model and a project process model, where the resource management model is used to administrate resources of the project, and the project process model based on an extended-directed-graph is proposed to describe the precedence constraints and resource constraints in SRCPSP. A simplified simulation strategy based on activity scanning method is used in the simulation model to generate feasible schedules of the problem. A multiple-comparison procedure based on the common random numbers is adopted to compare the multiple scheduling alternatives obtained from the stochastic simulation model and provide more information to select the optimal scheduling alternative. The cases are given to compare with other methods for the same SRCPSP from literature and show that the simulation tool by utilizing DES with a statistical method improves the efficiency of simulation in stochasticproject planning.
projectscheduling problems under both resource constraints and uncertainty have been widely studied due to their real world relevance. In this paper, we design and implement a new integrated proactive-reactive soluti...
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projectscheduling problems under both resource constraints and uncertainty have been widely studied due to their real world relevance. In this paper, we design and implement a new integrated proactive-reactive solution approach based on the critical chain method (CCM) to proactively generate a robust and reliable baseline schedule for the class of resource-constrainedprojectscheduling problem (RCPSP) under uncertainty. A discretetime Markov decision process model is applied for the reactive scheduling phase, which embeds the look-up table method in reinforcement learning to dynamically schedule and adjust schedule reactively using the baseline schedule during project execution. The cost values in the look-up table are calculated based on the occupation of a project buffer and feeding buffers in the baseline schedule generated by the CCM. We conduct computation experiments on the benchmark instances to test our algorithm. The results show that our approach is able to obtain quality solutions efficiently, and competitive with the benchmark algorithms for small- and medium-sized instances.
We study the stochastic resource-constrained project scheduling problem with uncertain resource availability, called SRCPSP-URA, and model it as a sequential decision problem. A new Markov decision process (MDP) model...
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We study the stochastic resource-constrained project scheduling problem with uncertain resource availability, called SRCPSP-URA, and model it as a sequential decision problem. A new Markov decision process (MDP) model is developed for the SRCPSP-URA. It dynamically and adaptively determines not only which activity to start at a stage, but also which to interrupt and delay when there is not sufficient resource capacity. To tackle the curse-of-dimensionality of an exact solution approach, we devise and implement a rollout-based approximate dynamic programming (ADP) algorithm with priority-rule heuristic as the base policy, for which theoretical sequential improvement property is proved. Computational results show that with moderately more computational time, our ADP algorithm significantly outperforms the priority-rule heuristics for test instances up to 120 activities. (C) 2021 Elsevier Inc. All rights reserved.
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