A decomposition-based heuristic for large employee scheduling problems with inter-department transfers

Attia, Dalia; Bürgy, Reinhard; Desaulniers, Guy; Soumis, François

doi:10.1007/s13675-019-00119-3

Cited by 3 publications

(5 citation statements)

References 21 publications

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“…For hospitals and clinics, optimisation can be used to create efficient staff schedules that take into account factors such as staff availability [56], skill levels and patient needs. This ensures that the right people are on duty at the right time to provide quality care while maintaining a reasonable workload [57]. (3) Patient triage.…”

Section: Optimization In Resource Allocationmentioning

confidence: 99%

<strong>Recent Optimization Methods and Techniques for Medical Image Analysis</strong>

wang

2023

Preprint

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Medical image analysis is an important branch in the field of medicine, which mainly uses image processing and analysis techniques to interpret and diagnose medical image data. Medical image data helps doctors to effectively observe and diagnose patients' body structures, tissues and lesions. Medical image analysis has been an important research area in the medical field, and it is important for disease diagnosis, treatment planning, and condition monitoring. In recent years, the rapid development of deep learning and computer vision technologies has contributed greatly to the automation, multimodal data fusion, real-time application, and accuracy improvement of medical image analysis. In addition, the development of deep learning has given rise to some new research areas in medical image analysis, such as Generative Adversarial Networks (GANs) for synthetic medical images, self-supervised learning for unsupervised feature learning, and neural network interpretability. In this paper, we will introduce some optimisation methods for medical images which are effective in improving the accuracy, efficiency and reliability of medical image analysis.

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Section: Optimization In Resource Allocationmentioning

confidence: 99%

<strong>Recent Optimization Methods and Techniques for Medical Image Analysis</strong>

wang

2023

Preprint

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“…In their problem definition, no restrictions are imposed on these transfers. In later studies, different authors incorporated resource transfers into their model via transfer costs (Dodin and Elimam, 1997;Bard and Wan, 2006;Batta et al, 2007;Nearchou et al, 2018;Attia et al, 2019;Dahmen et al, 2019). Dodin and Elimam (1997) recognise the relevance of travel times and costs but assume that travel times occur during non-working hours and only incorporate travel costs in their model formulation.…”

Section: Personnel Scheduling With Resource Transfersmentioning

confidence: 99%

“…idle times (Bard and Wan, 2006), resource transfer flexibility constraints (Bard and Wan, 2008)) or reduce the solution space (e.g. by solving the problem for a single entity (Attia et al, 2019), a cluster of multiple entities (Al-Yakoob and Sherali, 2007), a subset of employees (Al-Yakoob and Sherali, 2007) and/or a subset of tasks (Belien and Demeulemeester, 2006)). In later stages, improvements are accomplished by increasing the problem complexity and/or the solution space (Belien and Demeulemeester, 2006;Al-Yakoob and Sherali, 2007;Attia et al, 2019).…”

Section: Personnel Scheduling With Resource Transfersmentioning

confidence: 99%

“…by solving the problem for a single entity (Attia et al, 2019), a cluster of multiple entities (Al-Yakoob and Sherali, 2007), a subset of employees (Al-Yakoob and Sherali, 2007) and/or a subset of tasks (Belien and Demeulemeester, 2006)). In later stages, improvements are accomplished by increasing the problem complexity and/or the solution space (Belien and Demeulemeester, 2006;Al-Yakoob and Sherali, 2007;Attia et al, 2019). In some studies, these stages are organised in a hierarchical manner in the sense that decisions taken in earlier stages are imposed on later stages (Bard and Wan, 2008;Dahmen et al, 2019).…”

Section: Personnel Scheduling With Resource Transfersmentioning

confidence: 99%

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A column generation-based diving heuristic to solve the multi-project personnel staffing problem with calendar constraints and resource sharing

Eeckhout

Vanhoucke

Maenhout

2021

Computers & Operations Research

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“…In the related literature, the assignment of resources and their service tours in SAR problems and the other variants of resource-constrained project scheduling or routing problems are modeled and solved following simultaneous or two-stages decomposition approaches (Paraskevopoulus et al 2013;Yalçındağ et al, 2016). However, the intrinsic complexity of the problem, which contradicts the need for decision-making agility, especially in disaster response, has made the decomposition approaches efficient and interesting methods being applied in numerous studies (Schmid et al 2013;Rolland et al, 2010;Nickle et al, 2012;Allaoua et al, 2013;Yalçındağ et al, 2016;Can and Ulusoy, 2014;Misir et al, 2015;Yalçındağ and Matta, 2017;Attia et al, 2019;Dahmen et al, 2020, Memari et al, 2020.…”

Section: Scheduling and Routingmentioning

confidence: 99%

A decision support model for robust allocation and routing of search and rescue resources after earthquake: a case study

et al. 2020

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The efficient planning of search and rescue (SAR) operations is highly impactful in the disaster response phase, which offers a limited time window with a declining chance for saving trapped people. The present paper introduces a new robust decision support framework for planning SAR resource deployment in post-disaster districts. A two-stage decomposition approach is applied to formulate the problem as iterative interrelated stages of mixed-integer programming (MIP) models. The first stage presents a robust multi-period allocation model for maximizing fair and effective demand coverage in the affected districts during the entire planning horizon. It takes into account the time-sensitiveness of the operations via a time-dependent demand satisfaction measure and incorporates resource transshipment optimization. The second stage optimizes the routing of the resources allocated in the first stage for each district during the upcoming period. It aims to minimize the weighted sum of SAR demand fulfillment times under consideration of secondary destruction risk, resource collaboration, and rest time requirements. At the end of each period, the proposed framework can be re-executed to capture updated resource, demand, and travel time parameters. To tackle the environment's inherent uncertainty, an interval-based robust optimization approach is adopted. The proposed framework is solved and analyzed for an urban zone in Iran under an earthquake scenario. Results show that the proposed robust models have superior performance compared to a deterministic approach for adaptation to an uncertain disaster environment. More importantly, they prove to be a strong analysis tool for providing helpful managerial insights for the mitigation and preparedness phases.

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A decomposition-based heuristic for large employee scheduling problems with inter-department transfers

Cited by 3 publications

References 21 publications

<strong>Recent Optimization Methods and Techniques for Medical Image Analysis</strong>

<strong>Recent Optimization Methods and Techniques for Medical Image Analysis</strong>

A column generation-based diving heuristic to solve the multi-project personnel staffing problem with calendar constraints and resource sharing

A decision support model for robust allocation and routing of search and rescue resources after earthquake: a case study

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