A community detection approach for the resource leveling problem in a multi-project scheduling environment

Sayyadi, Abbas; Esmaeeli, Hamid; Hosseinian, Amir Hossein

doi:10.1016/j.cie.2022.108202

Cited by 12 publications

(1 citation statement)

References 59 publications

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“…Resource levelling attempts to schedule noncritical activities within their available floats without changing total project time to attain a good resource histogram [10]. The problems of resource leveling can be classified into four categories: (1) considering a single resource in a project [11], (2) handling multiple types of resources in a project [12], (3) dealing with a single resource in many projects [13], and (4) leveling multiple resources in different projects [14][15][16]. However, real construction projects are still facing challenging when dealing with sharing resources, and multiple mode activities due to the lack of a regular guiding process.…”

Section: Introductionmentioning

confidence: 99%

Robust forensic-based investigation algorithm for resource leveling in multiple projects

Tran¹,

Le²,

Nguyen³

et al. 2022

Scientia Iranica

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The project managers often face challenging due to a scarcity of resources in construction management. Levelling the used resources in multiple projects is a frequently encountered problem in construction areas and manufacturing sectors. This study proposes a robust forensic-based investigation (FBI) algorithm for resource leveling in multiple projects with considerations of different objective functions of resource graphs. The fuzzy c-means clustering approach is fused into the main operation of the FBI to enhance the rate of convergence by utilizing population information. The scheduling examines different objective functions for optimizing resource profile selection. Two application case studies are used to demonstrate the performance of the improved optimization algorithm in dealing with the resource-leveling problem in multiple projects. Experimental findings and statistical comparisons demonstrated that the developed FFBI could acquire high quality solutions and surpass those of compared optimization algorithms.

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