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Earned Value Management (EVM) is a project monitoring and control technique that enables the forecasting of a project's duration. Many EVM metrics and project duration forecasting methods have been proposed. However, very few studies have compared their accuracy and stability.

This paper presents an exhaustive stability and accuracy analysis of 27 deterministic EVM project duration forecasting methods. Stability is measured via Pearson's, Spearman's and Kendall's correlation coefficients while accuracy is measured by Mean Squared and Mean Absolute Percentage Errors. These parameters are determined at ten percentile intervals to track a given project's progress across 4,100 artificial project networks with varied topologies.

Findings support that stability and accuracy are inversely correlated for most forecasting methods, and also suggest that both significantly worsen as project networks become increasingly parallel. However, the AT + PD-ESmin forecasting method stands out as being the most accurate and reliable.

Implications of this study will allow construction project managers to resort to the simplest, most accurate and most stable EVM metrics when forecasting project duration. They will also be able to anticipate how the project topology (i.e., the network of activity predecessors) and the stage of project progress can condition their accuracy and stability.

Unlike previous research comparing EVM forecasting methods, this one includes all deterministic methods (classical and recent alike) and measures their performance in accordance with several parameters. Activity durations and costs are also modelled akin to those of construction projects.

The purpose of this paper is to introduce a reconfigurable model that is a combination of a schedule model and a queuing system M/M/m/K to reduce the duration of the wind turbine construction project closure phase and reduce the project documentation waiting time in the queue.

This research was implemented in a wind farm project. The schedule model deals with reducing the duration of the turbines closure phase by an activity overlapping technique, and the queuing system deals with reducing the turbine documentation waiting time in the queue, as well as reducing the probability of server idleness during the closure phase.

After the implementation of the model, the obtained results were compared to those of similar previously conducted projects in terms of duration, and the model was found effective.

Project closure is an important and mandatory process in all projects. More often than not, this process is faced with problems including prolonged project duration, disputes, lawsuits, and also in projects like the implementation of wind farms, a queue of documents at closing stage may also cause difficulties in project closure phase.

The contributions of this research are twofold: first, a combination of project management and queuing system is presented, and second, a reconfigurable model is introduced to enhance the performance and productivity of the closure phase of the project through reducing the implementation time and reducing the turbine documentation waiting time in the queue, as well as reducing the probability of server idleness during the closure phase of the wind farm project.

This study aims to introduce an efficient project buffer and resource management (PBRM) model for project resource leveling and project buffer sizing and controlling of project buffer consumption of a wind power plant project to achieve a more realistic project duration.

The methodology of this research consists of three main phases. In the first phase of the research methodology, resource leveling is done in the project and resource conflicts of activities are identified. In the second phase, the project critical chain is determined, and the appropriate size of the project buffer is specified. In the third phase of the methodology, buffer consumption is controlled and monitored during the project implementation. After using the PBRM method, the results of this project were compared with those of the previous projects.

According to the obtained results, it can be concluded that using PBRM model in this wind turbine project construction, the project duration became 25 per cent shorter than the scheduled duration and also 29 per cent shorter than average duration of previous similar projects.

One of the major problems with projects is that they are not completed according to schedule, and this creates time delays and losses in the implementation of projects. Today, as projects in the energy sector, especially renewable projects, are on the increase and also we are facing resource constraint in the implementation of projects, using scheduling techniques to minimize delays and obtain more realistic project duration is necessary.

This research was carried out in a wind farm project. In spite of the initial plan duration of 142 days and average duration of previous similar projects of 146 days, the project was completed in 113 days.

This paper introduces a practical project buffer and resource management model for project resource leveling, project buffer sizing and buffer consumption monitoring to reach a more realistic schedule in energy sector. This study adds to the literature by proposing the PBRM model in renewable energy sector.

This research presents a multiproject scheduling and resource management (MPSRM) model that includes an M/M/c/n queue system, a p-hub median model, a parallel machine scheduling and a hub location problem solution method. This research aims to design a project network and then sequence raw materials delivery to hub factories.

This research is implemented as a case study in construction and industrial company. It considers several mines to supply raw materials for production in hub factories. In the sequencing phase, a parallel mine scheduling problem specifies the delivery of raw materials to hub factories. Furthermore, a multisource project scheduling model is studied and designed for building project-oriented companies.

The results suggest that the proposed MPSRM model in this study significantly reduces project transportation costs. Therefore, creating accurate planning in projects, especially in construction projects where transportation plays an important role, can help reduce time and costs. Researchers, project managers and those dealing with projects can use this model to exploit their projects.

When the number of construction projects in a construction company increases in a region, choosing an appropriate strategy to supply resources and raw materials becomes very important in terms of profit and loss and project completion as scheduled. An increase in transportation demands alongside the economic development on the one hand and providing competitive transportation services, on the other hand, have increasingly spotlighted the significance of hub networks in transportation systems. In addition, in this research, there is no suitable access road from the mines to the project sites, and considering the workload, if any of the projects become a hub, a proper road should be built.

To the best of the knowledge, there is no outstanding research in which a p-hub median location problem by considering queuing model and different transportation modes and a parallel machine scheduling problem are studied simultaneously.

Project managers bear the responsibility of selecting and developing resource scheduling methods that align with project requirements and organizational circumstances. This study focuses on resource-constrained project scheduling in multi-project environments. The research simplifies the problem by adopting a single-project perspective using gain coefficients.

It employs uncertainty theory and multi-objective programming to construct a model. The optimal solution is identified using Matlab, while LINGO determines satisfactory alternatives. By combining these methods and considering actual construction project situations, a compromise solution closely approximating the optimal one is derived.

The study provides fresh insights into modeling and resolving resource-constrained project scheduling issues, supported by real-world examples that effectively illustrate its practical significance.

The research highlights three main contributions: effective resource utilization, project prioritization and conflict management, and addressing uncertainty. It offers decision support for project managers to balance resource allocation, resolve conflicts, and adapt to changing project demands.

The aim of this research is to propose a buffer sizing and buffer controlling algorithm (BSCA) as a heuristic algorithm for calculating project buffer and feeding buffers as well as dynamic controlling of buffer consumption in different phases of a wind power plant project in order to achieve a more realistic project duration.

The BSCA algorithm has two main phases of planning and buffer sizing and construction and buffer consumption. Project buffer and feeding buffers are determined in the planning and buffer sizing phase, and their consumption is controlled in the construction and buffer consumption phase. The heuristic algorithm was coded and run in MATLAB software. The sensitivity analysis was conducted to show the BSCA influence on project implementation. Then, to evaluate the BSCA algorithm, inputs from this project were run through several algorithms recently presented by researchers. Finally, the data of 20 projects previously accomplished by the company were applied to compare the proposed algorithm.

The results show that BSCA heuristic algorithm outperformed the other algorithms as it shortened the projects' durations. The average project completion time using the BSCA algorithm was reduced by about 15% compared to the previous average project completion time.

The proposed BSCA algorithm determines both the project buffer and feeding buffers and simultaneously controls their consumption in a dynamic way.

This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources.

The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time.

The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon.

There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners.