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Natural Computing
Erschienen in: Natural Computing 4/2020

18.12.2018

Joint optimization of the high-end equipment development task process and resource allocation

verfasst von: Xilin Zhang, Yuejin Tan, Zhiwei Yang

Erschienen in: Natural Computing | Ausgabe 4/2020

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Abstract

Considering the various uncertainties in the implementation process of high-end equipment development tasks, the Monte Carlo simulation method is used to simulate the execution process of these tasks. Parameters such as the average duration and cost of the simulation output are used to evaluate the fitness of individuals, and consequently, the development process is optimized using the NSGA-III algorithm. By comparing the optimization results of the development task process under different quantities of resources, the impact of the quantity of resources on the optimization results of the development task process is analyzed. With a view to obtain a more satisfactory development task process, the PSO algorithm is nested into NSGA-III. The Pareto front solution set is obtained from the optimization of the task process. The PSO algorithm is applied to optimize the resource allocation for the development task process. Joint optimization of the high-end equipment development task process and resource allocation is carried out. Finally, the effectiveness of the proposed method is verified by an example.
Vorheriger Artikel Hierarchies and undecidability results for iterative arrays with sparse communication
Nächster Artikel A binary monkey search algorithm variation for solving the set covering problem

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Metadaten
Titel
Joint optimization of the high-end equipment development task process and resource allocation
verfasst von
Xilin Zhang
Yuejin Tan
Zhiwei Yang
Publikationsdatum
18.12.2018
Verlag
Springer Netherlands
Erschienen in
Natural Computing / Ausgabe 4/2020
Print ISSN: 1567-7818
Elektronische ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-018-9722-x

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Preface

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