2022
DOI: 10.3934/jimo.2020158
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A robust time-cost-quality-energy-environment trade-off with resource-constrained in project management: A case study for a bridge construction project

Abstract: <p style='text-indent:20px;'>Sustainable development requires scheduling and implementation of projects by considering cost, environment, energy, and quality factors. Using a robust approach, this study investigates the time-cost-quality-energy-environment problem in executing projects and practically indicates its implementation capability in the form of a case study of a bridge construction project in Tehran, Iran. This study aims to take into account the sustainability pillars in scheduling projects a… Show more

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Cited by 101 publications
(58 citation statements)
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“…Application of uncertainty and forecasting techniques such as robust optimization (Golpîra and Tirkolaee, 2019; Kara et al, 2019; Khalilpourazari et al, 2020a; Lotfi et al, 2020; Özmen et al, 2017), fuzzy programming (Goli et al, 2021; Maity et al, 2019; Roy et al, 2019; Tirkolaee et al, 2021), stochastic optimal control (Kalaycı et al, 2020; Kropat et al, 2020; Savku and Weber, 2018), time series (Weber et al, 2011), regression models (Kuter et al, 2018), and grey systems (Ergün et al, 2020) to address the uncertain nature of the problem.…”
Section: Discussionmentioning
confidence: 99%
“…Application of uncertainty and forecasting techniques such as robust optimization (Golpîra and Tirkolaee, 2019; Kara et al, 2019; Khalilpourazari et al, 2020a; Lotfi et al, 2020; Özmen et al, 2017), fuzzy programming (Goli et al, 2021; Maity et al, 2019; Roy et al, 2019; Tirkolaee et al, 2021), stochastic optimal control (Kalaycı et al, 2020; Kropat et al, 2020; Savku and Weber, 2018), time series (Weber et al, 2011), regression models (Kuter et al, 2018), and grey systems (Ergün et al, 2020) to address the uncertain nature of the problem.…”
Section: Discussionmentioning
confidence: 99%
“…RO methods offer a risk-averse approach to dealing with uncertainties in optimization problems. They have attracted a lot of attention as an efficient tool to cope with real-world uncertainty [27,34]. Based on Pishvaeeet al [42], a solution is called robust if it is feasible and optimally robust simultaneously.Feasibility means that the proposed solution must get feasible values of the uncertain parameters, and optimally robust means that the value of the objective function for (almost) all values of the uncertain parameters remains close to the optimal value or minimum or, at least, has the less deviation from the optimal value.…”
Section: 2mentioning
confidence: 99%
“…Likewise, conservatism level (uncertainty budget) of Constraint ( 42) is equal to Γ rt ∈ [0, |R||T |], which has a similar definition to the Bertsimas's and Sim's modeland it is used jointly with Constraint (33) due to the existence of a similar parameter of uncertainty. Ultimately, the robust mathematical model is obtained by replacing Constraint (34) with Constraint (10), Constraints ( 34)-( 37) with Constraint ( 14), Constraints ( 38)-( 41) with Constraint (15), and Constraints ( 42) to (45) with Constraint (16).…”
Section: 2mentioning
confidence: 99%
“…The main limitation of the study is to solve the model on large scale for many scenarios. For future studies, it is suggested to use and apply uncertainty and address the robust convex method of the model 35‐37 . Furthermore, incorporating transportation aspects in the model and robustness in the manufacturing process can be beneficial.…”
Section: Conclusion and Managerial Insightsmentioning
confidence: 99%