A multi-objective multi-period mathematical programming model for integrated project portfolio optimization and contractor selection

This paper addresses the challenges of project portfolio optimization and contractor selection through two proposed scenarios. In the first scenario, two separate mixed-integer mathematical programming models are presented: one for project portfolio optimization and the other for contractor selectio...

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Vydané v:MethodsX Ročník 15; s. 103522
Hlavní autori: Zahedirad, Mostafa, Khalili-Damghani, Kaveh, Ghezavati, Vahidreza, Komijan, Alireza Rashidi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Netherlands Elsevier B.V 01.12.2025
Elsevier
Predmet:
case studies
Contractor ability planning
Contractor selection
Engineering
inflation
Integrated planning
Project portfolio optimization
Project risk planning
risk
Project risk planning
Project portfolio optimization
Integrated planning
Contractor selection
Contractor ability planning
ISSN:2215-0161, 2215-0161
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Shrnutí:This paper addresses the challenges of project portfolio optimization and contractor selection through two proposed scenarios. In the first scenario, two separate mixed-integer mathematical programming models are presented: one for project portfolio optimization and the other for contractor selection. In this approach, the decision variables from the project portfolio optimization model are treated as parameters in the contractor selection model. In the second scenario, an integrated mixed-integer mathematical programming model is introduced to simultaneously address both project portfolio optimization and contractor selection. Both scenarios consider multiple objectives, such as profit, risk, technical capability, and costs, along with numerous constraints, including relationships, inflation rates, and resources. The multi-objective optimization models are solved using goal programming (GP). A practical case study is conducted, comparing the two scenarios and demonstrating that the second scenario outperforms the first in terms of results. Additionally, the time complexity of both scenarios is analyzed, taking into account different numbers of variables and constraints. The analysis reveals that the second scenario exhibits superior performance in terms of CPU time.•This method applies goal programming (GP) to solve the mixed-integer mathematical programming models for project portfolio optimization and contractor selection, thoroughly comparing the two scenarios using a practical example. [Display omitted]
Bibliografia:ObjectType-Article-1
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ISSN:2215-0161
2215-0161
DOI:10.1016/j.mex.2025.103522