A mixed-integer linear programming model for solving fuzzy stochastic resource constrained project scheduling problem

Yagub Alipouri (), Mohammad Hassan Sebt (), Abdollah Ardeshir () and Mohammad Hossein Fazel Zarandi ()
Additional contact information
Yagub Alipouri: Amirkabir University of Technology (Tehran Polytechnic)
Mohammad Hassan Sebt: Amirkabir University of Technology (Tehran Polytechnic)
Abdollah Ardeshir: Amirkabir University of Technology (Tehran Polytechnic)
Mohammad Hossein Fazel Zarandi: Amirkabir University of Technology (Tehran Polytechnic)

Operational Research, 2020, vol. 20, issue 1, No 8, 197-217

Abstract: Abstract This paper addresses resource-constrained project scheduling problem with mixed uncertainty of randomness and fuzziness (FS-RCPSP). The activity durations are considered to be fuzzy random variables. A resource flow network based mathematical model with fuzzy random variables is presented. Then, this model is transformed into a mixed-integer linear programming model with crisp variables. The CPLEX 12.6.0.1 solver in AIMMS (2014) is employed for applying the proposed model to solve 960 benchmark instances generated from the well-known sets J30 and J60 in PSPLIB. The computational results are encouraging and indicate the ability of the proposed model to handle the FS-RCPSP.

Keywords: Combinatorial optimization; Fuzzy stochastic resource-constrained project scheduling problem; Fuzzy random variables; Mixed-integer linear programming (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s12351-017-0321-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:operea:v:20:y:2020:i:1:d:10.1007_s12351-017-0321-x

Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351

DOI: 10.1007/s12351-017-0321-x

Access Statistics for this article

Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis

More articles in Operational Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().