Showing papers by "Mario Vanhoucke published in 2021"

Automatic detection of the best performing priority rule for the resource-constrained project scheduling problem

Abstract: Priority rules are applied in many commercial software tools for scheduling projects under limited resources because of their known advantages such as the ease of implementation, their intuitive working, and their fast speed. Moreover, while numerous research papers present comparison studies between different priority rules, managers often do not know which rules should be used for their specific project, and therefore have no other choice than selecting a priority rule at random and hope for the best. This paper introduces a decision tree approach to classify and detect the best performing priority rule for the resource-constrained project scheduling problem (RCPSP). The research relies on two classification models to map project indicators onto the performance of the priority rule. Using such models, the performance of each priority rule can be predicted, and these predictions are then used to automatically select the best performing priority rule for a specific project with known network and resource indicator values. A set of computational experiment is set up to evaluate the performance of the newly proposed classification models using the most well-known priority rules from the literature. The experiments compare the performance of multi-label classification models with multi-class classification models, and show that these models can outperform the average performance of using any single priority rule. It will be argued that this approach can be easily extended to any extension of the RCPSP without changing the methodology used in this study.

Stability and accuracy of deterministic project duration forecasting methods in earned value management

Abstract: PurposeEarned 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.Design/methodology/approachThis 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.FindingsFindings 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.Practical implicationsImplications 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.Originality/valueUnlike 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.

Various extensions in resource-constrained project scheduling with alternative subgraphs

Abstract: In this research, we present several extensions for the resource-constrained project scheduling problem with alternative subgraphs (RCPSP-AS). First of all, we investigate more complex variants of ...

Generalized Multi-Scale Stochastic Reservoir Opportunity Index for enhanced well placement optimization under uncertainty in green and brownfields

Abstract: Well placement planning is one of the challenging issues in any field development plan. Reservoir engineers always confront the problem that which point of the field should be drilled to achieve the highest recovery factor and/or maximum sweep efficiency. In this paper, we use Reservoir Opportunity Index (ROI) as a spatial measure of productivity potential for greenfields, which hybridizes the reservoir static properties, and for brownfields, ROI is replaced by Dynamic Measure (DM), which takes into account the current dynamic properties in addition to static properties. The purpose of using these criteria is to diminish the search region of optimization algorithms and as a consequence, reduce the computational time and cost of optimization, which are the main challenges in well placement optimization problems. However, considering the significant subsurface uncertainty, a probabilistic definition of ROI (SROI) or DM (SDM) is needed, since there exists an infinite number of possible distribution maps of static and/or dynamic properties. To build SROI or SDM maps, the k -means clustering technique is used to extract a limited number of characteristic realizations that can reasonably span the uncertainties. In addition, to determine the optimum number of clustered realizations, Higher-Order Singular Value Decomposition (HOSVD) method is applied which can also compress the data for large models in a lower-dimensional space. Additionally, we introduce the multiscale spatial density of ROI or DM (D2 ROI and D2 DM), which can distinguish between regions of high SROI (or SDM) in arbitrary neighborhood windows from the local SROI (or SDM) maxima with low values in the vicinity. Generally, we develop and implement a new systematic approach for well placement optimization for both green and brownfields on a synthetic reservoir model. This approach relies on the utilization of multi-scale maps of SROI and SDM to improve the initial guess for optimization algorithm. Narrowing down the search region for optimization algorithm can substantially speed up the convergence and hence the computational cost would be reduced by a factor of 4.