A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty. (1st December 2019)

• Record Type: Journal Article
• Authors: Hematian, Milad; Seyyed Esfahani, Mir Mehdi; Mahdavi, Iraj; Mahdavi‐Amiri, Nezam; Rezaeian, Javad
• Is Part Of: Computational intelligence. Volume 36:Number 1(2020)
• Page Start: 276
• Page End: 296
• Publication Date: 2019-12-01
• Subjects: fuzzy mathematical model -- learning effect -- multi‐project scheduling -- multi‐skilled human resource assignment -- uncertainty; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12260
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Today, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.