A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Hematian, Milad; Esfahani, Mir Mehdi Seyyed; Mahdavi, Iraj; Mahdavi–Amiri, Nezam; Rezaeian, Javad

doi:10.1111/coin.12260

Cited by 16 publications

(4 citation statements)

References 52 publications

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“…They can ensemble multiple weak classifiers to form a more powerful classification model. 46,47 Since the resampling method is a preprocessing method independent of the classifier, this type of method only performs preprocessing on the training data set. Therefore, they can be combined with base classifiers.…”

Section: Ensemble Learning Methodsmentioning

confidence: 99%

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Dai¹,

Liu²,

Yang

2022

Computational Intelligence

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One of the most widely used approaches to the class‐imbalanced issue is ensemble learning. The base classifier is trained using an unbalanced training set in the conventional ensemble learning approach. We are unable to select the best suitable resampling method or base classifier for the training set, despite the fact that researchers have examined employing resampling strategies to balance the training set. A multi‐armed bandit heterogeneous ensemble framework was developed as a solution to these issues. This framework employs the multi‐armed bandit technique to pick the best base classifier and resampling techniques to build a heterogeneous ensemble model. To obtain training sets, we first employ the bagging technique. Then, we use the instances from the out‐of‐bag set as the validation set. In general, we consider the basic classifier combination with the highest validation set score to be the best model on the bagging subset and add it to the pool of model. The classification performance of the multi‐armed bandit heterogeneous ensemble model is then assessed using 30 real‐world imbalanced data sets that were gathered from UCI, KEEL, and HDDT. The experimental results demonstrate that, under the two assessment metrics of AUC and Kappa, the proposed heterogeneous ensemble model performs competitively with other nine state‐of‐the‐art ensemble learning methods. At the same time, the findings of the experiment are confirmed by the statistical findings of the Friedman test and Holm's post‐hoc test.

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Section: Ensemble Learning Methodsmentioning

confidence: 99%

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Dai¹,

Liu²,

Yang

2022

Computational Intelligence

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“…Some of these studies have also introduced new dimensions to measure the skills of multi-skilled resources [96,97]. Another study developed an integrated multi-project scheduling and multi-skill human resource assignment model for R&D projects under uncertainty, taking into account the learning effect to reduce costs and increase project efficiency [98]. One such study proposed a four-stage model that includes a weighted compatibility indicator to measure auditor competency and multi-choice goal programming to find trade-offs between project efficiency and competency improvement for internal audit managers [97].…”

Section: Application Areasmentioning

confidence: 99%

The Multi-Skilled Resource-Constrained Project Scheduling Problem: A Systematic Review and an Exploration of Future Landscapes

Bahroun,

As’ad,

Tanash

et al. 2024

Management Systems in Production Engineering

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The Multi-skilled Resource Constrained Project Scheduling Problem (MS-RCPSP) is a complex and multi-faceted problem that involves scheduling activities whilst considering various resource constraints. These constraints include limited availability of workers, equipment, and materials, with each activity requiring a minimal set of skills to be executed. Furthermore, for a better resemblance to reality, workers/machines are assumed to be multi-skilled/multi-purpose posing another dimension of complexity to the problem. The objective is to minimize project duration, cost, or other relevant criteria while accounting for the inherent resources flexibility. This paper provides a systematic review of the literature pertaining to MS-RCPSP, and an in-depth analysis of 171 papers published between 2000 and 2021 inclusive. The conducted bibliometric analysis identifies the top contributing authors, most influential papers, existing research tendencies, and thematic research topics within the field. In addition, this review highlights different aspects of the MS-RCPSP, spanning the significance of performance measures, solution approaches, application areas, and the incorporation of time constraints. While project completion time, cost, and tardiness are common performance indicators, other measures such as multi-skilled staff assignment and schedule robustness are also deemed important. Although various methods have been employed to solve the MS-RCPSP including exact and approximate approaches, the selection of the most-suited approach depends on the problem’s scale, complexity, and constraints, necessitating careful consideration of each method’s strengths and weaknesses. Interestingly, several studies have jointly addressed resource and time constraints in the context of MS-RCPSP, often considering tardiness, and have proposed different algorithms, models, and metaheuristics to tackle these challenges. This paper clearly highlights research gaps and promising avenues for future research. This work provides valuable insights for project managers to effectively schedule tasks in the presence of multiple flexible resources.

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“…The sub-fields in this quadrant are uncertain programming and uncertain differential equations, which are highly thought of, have good development trends, and are seeing steadily rising amounts of literature. In recent years, the research content of uncertain programming has tended towards the applications related to the sustainable facility location problem (Wang et al, 2019 ), multiobjective integrated multiproject scheduling problem (Hematian et al, 2019 ), parallel-machine scheduling problem (Shen & Zhu, 2019a ) and optimal pricing decision for supply chains (Ma et al, 2019 ). As the only sub-field with consistently strong performance, both theoretical and applied research of uncertain differential equations have been highly valued by researchers and have made remarkable achievements.…”

Section: Measurement Of Research Potentialmentioning

confidence: 99%

A systematic review of uncertainty theory with the use of scientometrical method

Zhou

Jiang

Pantelous

et al. 2022

Fuzzy Optim Decis Making

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Uncertainty theory is an area in axiomatic mathematics recently proposed by Professor Baoding Liu and aiming to deal with belief degrees. Retrieving 1004 journal articles from the Web of Science database between 2008 and 2019, and utilizing CiteSpace and Pajek software, we analyze the publications per year and by geographical distribution, productive scholars and their cooperation, key journals, highly cited articles and main paths of the field. In this way, seven key sub-fields of uncertainty theory and their research potential are derived. The results show the following: (1) The literature on uncertainty theory follows a linear growth trend, involves an extensive network of 1000 scholars worldwide and is published in 300 journals, indicating thus that uncertainty theory has become increasingly attractive, and its academic influence is gradually expanding. (2) Seven key sub-fields of uncertainty theory have clearly been identified, including the axiomatic system, uncertain programming, uncertain sets, uncertain logic, uncertain differential equations, uncertain risk analysis, and uncertain processes. Among them, uncertain differential equations and programming are the two main sub-fields with the largest numbers of published papers. Furthermore, for evaluating the research potential of sub-fields, maturity and recent attention indicators are calculated using the citations, total number of publications, quantity of most cited literature and half-life. Based on these indicators, uncertain processes shows the greatest development potential, and has remained a hot topic in recent years, being mainly concentrated on the uncertain renewal reward process, optimal control of discrete-time uncertain systems, and uncertain linear quadratic optimal control. Additionally, uncertain risk analysis is ranked second, and focuses on the analysis of expected losses, investment risk, and structural reliability of uncertain systems.

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A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Cited by 16 publications

References 52 publications

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

The Multi-Skilled Resource-Constrained Project Scheduling Problem: A Systematic Review and an Exploration of Future Landscapes

A systematic review of uncertainty theory with the use of scientometrical method

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