The resourceconstrainedprojectscheduling problem (RCPSP) is a complex and combinatorial optimization problem mostly relates with project management, construction industries, production planning and manufacturing do...
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The resourceconstrainedprojectscheduling problem (RCPSP) is a complex and combinatorial optimization problem mostly relates with project management, construction industries, production planning and manufacturing domains. Although several solution methods have been proposed, no single method has been shown to be the best. Further, optimal solution of this type of problem requires different requirements of the exploration and exploitation at different stages of the optimization process. Considering these requirements, in this paper, a two-stage multi-operator differential evolution (DE) algorithm, called TS-MODE, has been developed to solve RCPSP. TS-MODE starts with the exploration stage, and based on the diversity of population and the quality of solutions, this approach dynamically place more importance on the most-suitable DE, and then repeats the same process during the exploitation phase. A complete evaluation of the components and parameters of the algorithms by a Design of Experiments technique is also presented. A number of single-mode RCPSP data sets from the projectscheduling library (PSPLIB) have been considered to test the effectiveness and performance of the proposed TS-MODE against selected recent well-known state-of-the-art algorithms. Those results reveal the efficiency and competitiveness of the proposed TS-MODE approach. (C) 2020 Published by Elsevier B.V.
作者：
Cai, BingqiLiu, JingXidian Univ
Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China
Four representations for resource constrained project scheduling problems (RCPSPs) are studied by making use of the fitness landscape analysis technique. The fitness distance correlation (FDC) measure is used to analy...
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ISBN:
(纸本)9783642412783;9783642412776
Four representations for resource constrained project scheduling problems (RCPSPs) are studied by making use of the fitness landscape analysis technique. The fitness distance correlation (FDC) measure is used to analyze the landscapes. In the experiments, the study on the benchmark problems J30 is first presented to investigate which distance metric is more suitable for calculating FDC for RCPSPs. Then, the benchmark problems Patterson, J30, and J60 are used to evaluate the effect of the four encodings on the performance of evolutionary algorithms. Finally, a standard genetic algorithm is applied to verify the predictions made by the FDC. To the best of our knowledge, this is the first work on using FDC to study different encodings for RCPSPs.
The resource-constrainedprojectscheduling Problem (RCPSP) has been widely accepted as a challenging research topic due to its NP-hard nature. Because of the dynamic nature of real-world problems, stochastic-RCPSPs (...
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The resource-constrainedprojectscheduling Problem (RCPSP) has been widely accepted as a challenging research topic due to its NP-hard nature. Because of the dynamic nature of real-world problems, stochastic-RCPSPs (SRCPSPs) are also receiving greater attention among researchers. To solve these extended RCPSPs (i. e., SRCPSPs), this paper proposes an reinforcement learning based meta-heuristic switching approach that utilizes the powers of both multi-operator differential evolution (MODE) and discrete cuckoo search (DCS) algorithms in single algorithmic framework. Reinforcement learning (RL) is introduced as a technique to select either MODE or DCS based on the diversity of population and quality of solutions. To deal with uncertain durations, a chance-constrained based approach with some belief degrees is also considered and solved by this proposed RL based multi-method approach (i.e., DECSwRL-CC). Extensive experimentation with benchmark data from the projectscheduling library (PSPLIB) demonstrates the efficacy of this proposed multi-method approach. Numerous state of the art chance constrained approaches are taken from the literature to compare the proposed approach and to validate the efficacy of this multi-method approach. This particular strategy is applicable to the risk-averse decision-makers who want to realize the project schedule with a high degree of certainty.
Presented in this work is the development of a simulation modelling and solution approach for the scheduling problem of a case study company which can be classified as a project based flow shop. The problem is formula...
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ISBN:
(纸本)9781510810594
Presented in this work is the development of a simulation modelling and solution approach for the scheduling problem of a case study company which can be classified as a project based flow shop. The problem is formulated as a resourceconstrainedprojectscheduling Problem (RCPSP) and solved with a variable intensity approach. Binary decision variables are used in determining the projects to be executed on a periodic basis at the operations involved, followed by the application of a capacity allocation algorithm for determining the proportion of operational capacity to dedicate to each project. Also, a feeding precedence relation is applied for modelling the overlap that exists between two of the operations involved in the system. The work is part of the development of the various modules of a simulation based decision support system. The simulation modelling and solution methodology developed here are for implementation in its simulation module, which is an open-source simulation modelling package.
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