TY - JOUR
T1 - A robust chance-constrained programming approach for a bi-objective pre-emptive multi-mode resource-constrained project scheduling problem with time crashing
AU - Shahabi-Shahmiri, Reza
AU - Kyriakidis, Thomas S.
AU - Ghasemi, Mohammad
AU - Mirnezami, Seyed Ali
AU - Mirjalili, Seyedali
N1 - Funding Information:
The authors would like to 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 Ms. Fateme Nazeri and Ms. Fateme Zarei for their provision of data, as well as Dr. Hasan Shirzadi for the final validation of the obtained results.
Publisher Copyright:
© 2023 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2023
Y1 - 2023
N2 - The presented study proposes a novel bi-objective mixed integer linear programming (MILP) framework for the multi-mode resource-constrained project scheduling problem (MRCPSP) with pre-emptive and non-preemptive activities splitting under uncertain conditions. Minimising the project makespan and resource costs are the considered objectives. Renewable and non-renewable resources along with different modes are taken into account for activities implementation. Additionally, some activities can be crashed by consuming additional renewable and non-renewable resources. Model uncertainty is efficiently addressed by utilising a fuzzy chance constrained programming (CPP) method as well as extending two robust possibilistic programming models. The capability of the presented mathematical framework is validated using problem instances from PSPLIB (j10, j14, j20, and j30) and MMLIB (MM50 and MM100). Finally, a detailed computational comparison is presented to assess the performance of the two robust possibilistic programming models.
AB - The presented study proposes a novel bi-objective mixed integer linear programming (MILP) framework for the multi-mode resource-constrained project scheduling problem (MRCPSP) with pre-emptive and non-preemptive activities splitting under uncertain conditions. Minimising the project makespan and resource costs are the considered objectives. Renewable and non-renewable resources along with different modes are taken into account for activities implementation. Additionally, some activities can be crashed by consuming additional renewable and non-renewable resources. Model uncertainty is efficiently addressed by utilising a fuzzy chance constrained programming (CPP) method as well as extending two robust possibilistic programming models. The capability of the presented mathematical framework is validated using problem instances from PSPLIB (j10, j14, j20, and j30) and MMLIB (MM50 and MM100). Finally, a detailed computational comparison is presented to assess the performance of the two robust possibilistic programming models.
KW - activity preemption
KW - MRCPSP
KW - robust chance constrained programming
KW - time crashing
KW - time–cost trade-off
UR - http://www.scopus.com/inward/record.url?scp=85170838191&partnerID=8YFLogxK
U2 - 10.1080/23302674.2023.2253147
DO - 10.1080/23302674.2023.2253147
M3 - Article
AN - SCOPUS:85170838191
SN - 2330-2674
VL - 10
JO - International Journal of Systems Science: Operations and Logistics
JF - International Journal of Systems Science: Operations and Logistics
IS - 1
M1 - 2253147
ER -
