Multiproject Resources Allocation Model under Fuzzy Random Environment and Its Application to Industrial Equipment Installation Engineering

Journal of Applied Mathematics. 2013;2013 DOI 10.1155/2013/818731


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Journal Title: Journal of Applied Mathematics

ISSN: 1110-757X (Print); 1687-0042 (Online)

Publisher: Hindawi Limited

LCC Subject Category: Science: Mathematics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML



Jun Gang (Business School, Sichuan University, Chengdu 610064, China)

Jiuping Xu (Business School, Sichuan University, Chengdu 610064, China)

Yinfeng Xu (Business School, Sichuan University, Chengdu 610064, China)


Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 18 weeks


Abstract | Full Text

This paper focuses on a multiproject resource allocation problem in a bilevel organization. To solve this problem, a bilevel multiproject resource allocation model under a fuzzy random environment is proposed. Two levels of decision makers are considered in the model. On the upper level, the company manager aims to allocate the company's resources to multiple projects to achieve the lowest cost, which include resource costs and a tardiness penalty. On the lower level, each project manager attempts to schedule their resource-constrained project, with minimization of project duration as the main objective. In contrast to prior studies, uncertainty in resource allocation has been explicitly considered. Specifically, our research uses fuzzy random variables to model uncertain activity durations and resource costs. To search for the optimal solution of the bilevel model, a hybrid algorithm made up of an adaptive particle swarm optimization, an adaptive hybrid genetic algorithm, and a fuzzy random simulation algorithm is also proposed. Finally, the efficiency of the proposed model and algorithm is evaluated through a practical case from an industrial equipment installation company. The results show that the proposed model is efficient in dealing with practical resource allocation problems in a bilevel organization.