Financial Optimization of the Resource-Constrained Project Scheduling Problem with Milestones Payments

Klimek, Marcin

doi:10.3390/app11020661

Cited by 8 publications

(6 citation statements)

References 43 publications

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Zhiwu Li . execution. By increasing resource allocations, the duration of project activities can be shortened, but this may have negative effects on other objectives, such as cost and environmental implications [3]. Therefore, optimal trade-offs between the numerous project goals are essential.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model for Optimizing NPV and Greenhouse Gases for Construction Projects Under Carbon Emissions Constraints

Hussain,

Salman Khalid,

Alkahtani

et al. 2024

IEEE Access

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Traditionally, projects have been scheduled with a primary focus on optimizing economic benefits, often at the expense of environmental considerations. However, in today's context, there is an increasing societal demand for organizations to incorporate sustainability practices into their project management policies. This research aims to address this pressing issue by developing a comprehensive mathematical model that takes into account two crucial objectives: project net present value (NPV) and greenhouse gas (GHG) emissions. This model introduces carbon emission constraints on a per-period basis, alongside the usual precedence and resource constraints. the problem represented by this model is proven to be NP-hard, which adds complexity to its solution. To tackle this challenge, two multi-objective metaheuristic algorithms, specifically the Modified Grey Wolf Optimizer (MGWO) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), have been employed to solve the model. Extensive comparative analyses of these me-ta heuristics across various multi-objective performance measures indicate that MGWO consistently outperforms SPEA2 in most scenarios. To illustrate the practical applicability of the pro-posed model, a real-world case study has been utilized. The results of this case study offer compelling insights, showing that project NPV can be significantly improved with only a marginal increase in GHG emissions. This finding highlights the model's potential to provide pragmatic solutions and valuable trade-off options for project planners and practitioners striving to balance economic objectives with environmental sustainability.

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Section: Introductionmentioning

confidence: 99%

A Mathematical Model for Optimizing NPV and Greenhouse Gases for Construction Projects Under Carbon Emissions Constraints

Hussain,

Salman Khalid,

Alkahtani

et al. 2024

IEEE Access

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“…RCPSPDC is a crucial problem to consider when dealing with long-term projects with significant financial implications [1]. These projects usually involve two parties: the contractor, who is responsible for executing the project, and the client, who outsources the project to the contractor and provides the financing [2]. The details of the financial transactions are usually negotiated when the project's contract is signed [3].…”

Section: Introductionmentioning

confidence: 99%

Maximizing Net Present Value for Resource Constraint Project Scheduling Problems with Payments at Event Occurrences Using Approximate Dynamic Programming

Phuntsho,

Gonsalves

2024

Algorithms

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Resource Constraint Project Scheduling Problems with Discounted Cash Flows (RCPSPDC) focuses on maximizing the net present value by summing the discounted cash flows of project activities. An extension of this problem is the Payment at Event Occurrences (PEO) scheme, where the client makes multiple payments to the contractor upon completion of predefined activities, with additional final settlement at project completion. Numerous approximation methods such as metaheuristics have been proposed to solve this NP-hard problem. However, these methods suffer from parameter control and/or the computational cost of correcting infeasible solutions. Alternatively, approximate dynamic programming (ADP) sequentially generates a schedule based on strategies computed via Monte Carlo (MC) simulations. This saves the computations required for solution corrections, but its performance is highly dependent on its strategy. In this study, we propose the hybridization of ADP with three different metaheuristics to take advantage of their combined strengths, resulting in six different models. The Estimation of Distribution Algorithm (EDA) and Ant Colony Optimization (ACO) were used to recommend policies for ADP. A Discrete cCuckoo Search (DCS) further improved the schedules generated by ADP. Our experimental analysis performed on the j30, j60, and j90 datasets of PSPLIB has shown that ADP–DCS is better than ADP alone. Implementing the EDA and ACO as prioritization strategies for Monte Carlo simulations greatly improved the solutions with high statistical significance. In addition, models with the EDA showed better performance than those with ACO and random priority, especially when the number of events increased.

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“…The evolution of computational resources has allowed the emergence of a set of applications in the most diverse platforms, such as web, desktop, mobile, and application contexts, such as finance management [1,2], privacy and safety [3], health [4,5], education [6], smart cities [7,8], smart homes [9], and smart things [10].…”

Section: Introductionmentioning

confidence: 99%

Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes

Machado¹,

Pires²,

Caldeira³

et al. 2021

Applied Sciences

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Data centers are widely recognized for demanding many energy resources. The greater the computational demand, the greater the use of resources operating together. Consequently, the greater the heat, the greater the need for cooling power, and the greater the energy consumption. In this context, this article aims to report an industrial experience of achieving energy efficiency in a data center through a new layout proposal, reuse of previously existing resources, and air conditioning. We used the primary resource to adopt a cold corridor confinement, the increase of the raised floor’s height, and a better direction of the cold airflow for the aspiration at the servers’ entrance. We reused the three legacy refrigeration machines from the old data center, and no new ones were purchased. In addition to 346 existing devices, 80 new pieces of equipment were added (between servers and network assets) as a load to be cooled. Even with the increase in the amount of equipment, the implementations contributed to energy efficiency compared to the old data center, still reducing approximately 41% of the temperature and, consequently, energy-saving.

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Financial Optimization of the Resource-Constrained Project Scheduling Problem with Milestones Payments

Cited by 8 publications

References 43 publications

A Mathematical Model for Optimizing NPV and Greenhouse Gases for Construction Projects Under Carbon Emissions Constraints

A Mathematical Model for Optimizing NPV and Greenhouse Gases for Construction Projects Under Carbon Emissions Constraints

Maximizing Net Present Value for Resource Constraint Project Scheduling Problems with Payments at Event Occurrences Using Approximate Dynamic Programming

Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes

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