Ensembles of priority rules for resource constrained project scheduling problem

Đumić, Mateja; Jakobović, Domagoj

doi:10.1016/j.asoc.2021.107606

Cited by 35 publications

(8 citation statements)

References 33 publications

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“…Alternatively, "ensembles methods" is a ML technique that combines different models or approaches to improve the accuracy of the results. Đumić et al, [48] developed an approach based on producing ensembles of priority rules for the RCPSP and a voting algorithm to select the best one for a specific context. In their work, four different approaches were considered to combine priority rules, and the results showed that ensembles of priority rules achieve much better results as opposed to using a single priority rule.…”

Section: Supervised ML Algorithmsmentioning

confidence: 99%

Artificial Intelligence Applications in Project Scheduling: A Systematic Review, Bibliometric Analysis, and Prospects for Future Research

Bahroun¹,

Tanash²,

As’ad³

et al. 2023

Management Systems in Production Engineering

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The availability of digital infrastructures and the fast-paced development of accompanying revolutionary technologies have triggered an unprecedented reliance on Artificial intelligence (AI) techniques both in theory and practice. Within the AI domain, Machine Learning (ML) techniques stand out as essential facilitator largely enabling machines to possess human-like cognitive and decision making capabilities. This paper provides a focused review of the literature addressing applications of emerging ML tools to solve various Project Scheduling Problems (PSPs). In particular, it employs bibliometric and network analysis tools along with a systematic literature review to analyze a pool of 104 papers published between 1985 and August 2021. The conducted analysis unveiled the top contributing authors, the most influential papers as well as the existing research tendencies and thematic research topics within this field of study. A noticeable growth in the number of relevant studies is seen recently with a steady increase as of the year 2018. Most of the studies adopted Artificial Neural Networks, Bayesian Network and Reinforcement Learning techniques to tackle PSPs under a stochastic environment, where these techniques are frequently hybridized with classical metaheuristics. The majority of works (57%) addressed basic Resource Constrained PSPs and only 15% are devoted to the project portfolio management problem. Furthermore, this study clearly indicates that the application of AI techniques to efficiently handle PSPs is still in its infancy stage bringing out the need for further research in this area. This work also identifies current research gaps and highlights a multitude of promising avenues for future research.

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Section: Supervised ML Algorithmsmentioning

confidence: 99%

Artificial Intelligence Applications in Project Scheduling: A Systematic Review, Bibliometric Analysis, and Prospects for Future Research

Bahroun¹,

Tanash²,

As’ad³

et al. 2023

Management Systems in Production Engineering

View full text Add to dashboard Cite

show abstract

“…In another study, Ðumić and Jakobović [42] analyzed two adaptations (iterative priority rules and rollout approach) for evolving priority rules in a static environment for the RCPSP and have shown that these approaches achieve better results than the priority rules evolved and used without these adaptations. Moreover, Ðumić and Jakobović [43] created ensembles of priority rules to improve the performance of priority rules designed by GP for the RCPSP. The experimental results show that ensembles of priority rules achieve significantly better results than those achieved when using a single rule.…”

Section: Genetic Programming Hyper-heuristicmentioning

confidence: 99%

“…More precisely, if a priority rule is trained using the serial SGS, its performance in the test process is also based on the serial SGS, while its performance on test data using the parallel SGS is not considered. In some cases [43], only the results of the parallel SGS are reported. However, it is well-known that neither of these two schedule generation schemes can be generally considered superior to the other [65].…”

Section: Single Modelsmentioning

confidence: 99%

Automated design of priority rules for resource-constrained project scheduling problem using surrogate-assisted genetic programming

Luo

Vanhoucke

Coelho

2023

Swarm and Evolutionary Computation

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“…This study also showed that, even when using different ensemble construction methods, it was possible to obtain good ensembles in a short time. The ensemble approaches from [30] were also applied to the resource constrained project scheduling problem (RCPSP) [24]. In this work, it was shown that using ensembles it was again possible to improve the results compared to single DRs.…”

Section: Related Workmentioning

confidence: 99%

Novel ensemble collaboration method for dynamic scheduling problems

Đurasević¹,

Planinić²,

Gil-Gala³

et al. 2022

Preprint

Self Cite

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Dynamic scheduling problems are important optimisation problems with many real-world applications. Since in dynamic scheduling not all information is available at the start, such problems are usually solved by dispatching rules (DRs), which create the schedule as the system executes. Recently, DRs have been successfully developed using genetic programming. However, a single DR may not efficiently solve different problem instances. Therefore, much research has focused on using DRs collaboratively by forming ensembles. In this paper, a novel ensemble collaboration method for dynamic scheduling is proposed. In this method, DRs are applied independently at each decision point to create a simulation of the schedule for all currently released jobs. Based on these simulations, it is determined which DR makes the best decision and that decision is applied. The results show that the ensembles easily outperform individual DRs for different ensemble sizes. Moreover, the results suggest that it is relatively easy to create good ensembles from a set of independently evolved DRs. CCS CONCEPTS• Computing methodologies → Planning and scheduling.

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Ensembles of priority rules for resource constrained project scheduling problem

Cited by 35 publications

References 33 publications

Artificial Intelligence Applications in Project Scheduling: A Systematic Review, Bibliometric Analysis, and Prospects for Future Research

Artificial Intelligence Applications in Project Scheduling: A Systematic Review, Bibliometric Analysis, and Prospects for Future Research

Automated design of priority rules for resource-constrained project scheduling problem using surrogate-assisted genetic programming

Novel ensemble collaboration method for dynamic scheduling problems

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