An integrated fuzzy multi-criteria decision making and mixed-integer linear programming optimization framework for project risk assessment and response strategy selection: A case study of a liquefied petroleum gas pipeline construction project
- 1Department of Energy Economics and Management, Tehran Faculty of Petroleum, Petroleum University of Technology, Tehran, Iran
Abstract
Effective project risk management helps to reduce negative outcomes, especially in construction projects. Because project risk management involves many different parts, an integrated approach is needed to cover all phases. This study proposes an integrated framework for risk planning, identification, assessment, and response strategy selection, addressing major weaknesses in traditional risk management methods. The framework systematically identifies risks, their characteristics, and their impact zones. These elements help to evaluate each risk thoroughly. Risk assessment is performed using fuzzy multi-criteria decision-making, fuzzy SWARA (Step-wise Weight Assessment Ratio Analysis) and fuzzy WSM (Weighted Sum Model). The selection of risk response strategies is then formulated as a mixed integer linear programming (MILP) optimization model. This produces an optimal risk response strategy that reduces threats and takes advantage of opportunities. To provide a proof-of-concept illustration of the proposed integrated framework, we present a case study utilizing empirical data from a real‑world liquefied petroleum gas (LPG) pipeline construction project. The findings identify 20 distinct risks and 12 viable risk response strategies. The optimization component successfully selects six out of twelve response strategies. This achieves a 38.9% reduction in total risk value while meeting the project’s constraints.
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BibTeX
@article{bachari2026integrated,
title = {{An integrated fuzzy multi-criteria decision making and mixed-integer linear programming optimization framework for project risk assessment and response strategy selection: A case study of a liquefied petroleum gas pipeline construction project}},
author = {Mohammad Senisel Bachari and Asgar Khademvatani and Mahdi Iranfar},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0357560},
url = {https://doi.org/10.1371/journal.pone.0357560},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - An integrated fuzzy multi-criteria decision making and mixed-integer linear programming optimization framework for project risk assessment and response strategy selection: A case study of a liquefied petroleum gas pipeline construction project
AU - Mohammad Senisel Bachari
AU - Asgar Khademvatani
AU - Mahdi Iranfar
PY - 2026
DA - 2026/09/24
JO - PLOS One
DO - 10.1371/journal.pone.0357560
UR - https://doi.org/10.1371/journal.pone.0357560
AB - Effective project risk management helps to reduce negative outcomes, especially in construction projects. Because project risk management involves many different parts, an integrated approach is needed to cover all phases. This study proposes an integrated framework for risk planning, identification, assessment, and response strategy selection, addressing major weaknesses in traditional risk management methods. The framework systematically identifies risks, their characteristics, and their impact zones. These elements help to evaluate each risk thoroughly. Risk assessment is performed using fuzzy multi-criteria decision-making, fuzzy SWARA (Step-wise Weight Assessment Ratio Analysis) and fuzzy WSM (Weighted Sum Model). The selection of risk response strategies is then formulated as a mixed integer linear programming (MILP) optimization model. This produces an optimal risk response strategy that reduces threats and takes advantage of opportunities. To provide a proof-of-concept illustration of the proposed integrated framework, we present a case study utilizing empirical data from a real‑world liquefied petroleum gas (LPG) pipeline construction project. The findings identify 20 distinct risks and 12 viable risk response strategies. The optimization component successfully selects six out of twelve response strategies. This achieves a 38.9% reduction in total risk value while meeting the project’s constraints.
SN - 1932-6203
ER -