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About

José Coelho holds a PhD in Systems Engineering from the Technical University of Lisbon in 2004. It is an Assistant Professor at the Open University in the Department of Science and Technology. Published 12 papers in international journals and more than 35 varied nature of resources in the open repository. In their professional activities interacted with 36 employees in co-authorships of scientific papers.

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Publications

2022

An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem

Authors
Luo, JY; Vanhoucke, M; Coelho, J; Guo, WK;

Publication
EXPERT SYSTEMS WITH APPLICATIONS

Abstract
In recent years, machine learning techniques, especially genetic programming (GP), have been a powerful approach for automated design of the priority rule-heuristics for the resource-constrained project scheduling problem (RCPSP). However, it requires intensive computing effort, carefully selected training data and appropriate assessment criteria. This research proposes a GP hyper-heuristic method with a duplicate removal technique to create new priority rules that outperform the traditional rules. The experiments have verified the efficiency of the proposed algorithm as compared to the standard GP approach. Furthermore, the impact of the training data selection and fitness evaluation have also been investigated. The results show that a compact training set can provide good output and existing evaluation methods are all usable for evolving efficient priority rules. The priority rules designed by the proposed approach are tested on extensive existing datasets and newly generated large projects with more than 1,000 activities. In order to achieve better performance on small-sized projects, we also develop a method to combine rules as efficient ensembles. Computational comparisons between GP-designed rules and traditional priority rules indicate the superiority and generalization capability of the proposed GP algorithm in solving the RCPSP.

2022

A prediction model for ranking branch-and-bound procedures for the resource-constrained project scheduling problem

Authors
Guo, W; Vanhoucke, M; Coelho, J;

Publication
European Journal of Operational Research

Abstract

2021

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

Authors
Guo, WK; Vanhoucke, M; Coelho, J; Luo, JY;

Publication
EXPERT SYSTEMS WITH APPLICATIONS

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. © 2020 Elsevier Ltd

2021

An analysis of network and resource indicators for resource-constrained project scheduling problem instances

Authors
Vanhoucke, M; Coelho, J;

Publication
COMPUTERS & OPERATIONS RESEARCH

Abstract
In the past decades, the resource on the resource-constrained project scheduling problem (RCPSP) has grown rapidly, resulting in an overwhelming amount of solution procedures that provide (near)-optimal solutions in a reasonable time. Despite the rapid progress, little is still known what makes a project instance hard to solve. Inspired by a previous research study that has shown that even small instances with only up to 30 activities is sometimes hard to solve, the current study provides an analysis of the project data used in the academic literature. More precisely, it investigates the ability of four well-known resource indicators to predict the hardness of an RCPSP instance. The study introduces a new instance equivalence concept to show that instances might have very different values for their resource indicators without changing any possible solution for this instance. The concept is based on four theorems and a search algorithm that transforms existing instances into new equivalent instances with more compact resources. This algorithm illustrates that the use of resource indicators to predict the hardness of an instance is sometimes misleading. In a set of computational experiment on more than 10,000 instances, it is shown that the newly constructed equivalent instances have values for the resource indicators that are not only different than the values of the original instances, but also often are better in predicting the hardness the project instances. It is suggested that the new equivalent instances are used for further research to compare results on the new instances with results obtained from the original dataset. © 2021 Elsevier Ltd

2020

Going to the core of hard resource-constrained project scheduling instances

Authors
Coelho, J; Vanhoucke, M;

Publication
COMPUTERS & OPERATIONS RESEARCH

Abstract