Multi-skill resource-constrained project scheduling problem considering overlapping: fuzzy multi-objective programming approach to a case study

Abstract

AbstractResource-constrained project scheduling problem (RCPSP) is a broadly researched issue in the literature. The purpose of the classic form of the problem is scheduling a set of activities considering resource and precedence constraints for minimizing the project completion time. Companies mostly deal with the issue of properly assigning multi-skilled workforces and maintaining the needed skill levels while implementing projects. In this study, a novel MILP model with three objectives is presented to tackle multi-skill RCPSP (MS-RCPSP). This study concentrates on minimizing project makespan, minimizing resource costs as well as tardiness costs, and maximizing quality under uncertainty. However, the standard MS-RCPSP is not able to consider several practical engineering requirements owing to its narrow assumptions. Therefore, key assumptions including overlap between activities, tardiness penalties of activities and the rework duration concept for activities in this model are considered. Due to the complexity of the real world, interval valued fuzzy numbers are taken into account for some of the problem’s parameters. The efficiency of the proposed mathematical framework is represented using both a real case study to construct a railway bridge with 34 activities and large-size problem instances from MMLIB (MM50 and MM100). Since this model is multi-objective, a new extended IVF-ABS approach is presented in this study. Finally, the proposed approach is compared with two methods, namely SO and ABS, from the literature.Keywords: Multi-objectivemulti-skillproject schedulingoverlappingmixed integer linear programming AcknowledgmentsThe authors thank the Editor-in-Chief, Associate Editor and anonymous reviewers for their valuable comments on this presentation for remarkable improvement. The authors would also like to express their gratitude to Mr. Farzad Azimi and Ms. Fateme Nazeri for collaboration in provision of data.Ethics approvalThe authors certify that they have no affiliation with or involvement with human participants or animals performed by any of the authors in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this paper.Authors’ contributionsAll authors contributed to all parts of this research including Conceptualization; Formal analysis; Resources; Methodology; Supervision; Data collection and investigation; Software; Validation; and Writing – review & editing.Disclosure statementNo potential conflict of interest was reported by the authors.Availability of data and materialAll data generated or analyzed during this research are included in this published article.