Partitioning methods for pruning the Pareto set with application to multiobjective allocation of a cross-trained workforce

Brusco, Michael J.

doi:10.1016/j.cie.2017.06.035

Cited by 14 publications

(6 citation statements)

References 52 publications

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“…e nursing workforce planning has many characteristics, such as noninventoriable services and timevarying demands, thus making it far more complicated than traditional workforce planning issues. To meet peak and fluctuating demands, some researchers have proposed floating nurse pools, on-the-job training, and crosstraining to increase staff flexibility [15,20]. Nasiri and Rahvar [21] developed a nurse scheduling model which considered nurse preferences and consecutive shifts.…”

Section: Factors Affecting Nursing Staffing Optimisationmentioning

confidence: 99%

“…Specifically, when a temporary nursing staff is selected for any shift within twenty-one consecutive shifts (i.e., seven consecutive days), the temporary staff is counted in the workforce in that particular week. Equation (20) defines the relationship between T t ijm and E t ijw and expresses the limitation imposed on temporary staff concerning the maximum number of working hours that a temporary nursing staff can work per week. Hence, the weekly number of temporary nursing staff, for each category, to be hired is given by equation (21).…”

Section: Model Of the Nursing Staffing Optimisation Problemmentioning

confidence: 99%

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Blockchain-IoT-Driven Nursing Workforce Planning for Effective Long-Term Care Management in Nursing Homes

Tsang

Leung

et al. 2021

Journal of Healthcare Engineering

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Due to the global ageing population, the increasing demand for long-term care services for the elderly has directed considerable attention towards the renovation of nursing homes. Although nursing homes play an essential role within residential elderly care, professional shortages have created serious pressure on the elderly service sector. Effective workforce planning is vital for improving the efficacy and workload balance of existing nursing staff in today’s complex and volatile long-term care service market. Currently, there is lack of an integrated solution to monitor care services and determine the optimal nursing staffing strategy in nursing homes. This study addresses the above challenge through the formulation of nursing staffing optimisation under the blockchain-internet of things (BIoT) environment. Embedding a blockchain into IoT establishes the long-term care platform for the elderly and care workers, thereby decentralising long-term care information in the nursing home network to achieve effective care service monitoring. Moreover, such information is further utilised to optimise nursing staffing by using a genetic algorithm. A case study of a Hong Kong nursing home was conducted to illustrate the effectiveness of the proposed system. We found that the total monthly staffing cost after using the proposed model was significantly lower than the existing practice with a change of −13.48%, which considers the use of heterogeneous workforce and temporary staff. Besides, the care monitoring and staffing flexibility are further enhanced, in which the concept of skill substitution is integrated in nursing staffing optimisation.

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Section: Factors Affecting Nursing Staffing Optimisationmentioning

confidence: 99%

Section: Model Of the Nursing Staffing Optimisation Problemmentioning

confidence: 99%

Blockchain-IoT-Driven Nursing Workforce Planning for Effective Long-Term Care Management in Nursing Homes

Tsang

Leung

et al. 2021

Journal of Healthcare Engineering

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“…For bicriterion combinatorial optimization problems that are small or have particular structures that can be exploited, it is sometimes possible to generate the Pareto efficient set of solutions using mathematical programming solutions or specially designed algorithms (see, for example, Brusco, 2015, 2017; Delattre & Hansen, 1980). Such examples, however, are the exceptions, not the rule.…”

Section: Diversity and Dispersion Criteria For Anticlusteringmentioning

confidence: 99%

“…There is commonly a small subset of partitions that produce a convex hull for the plot and the partitions that define the hull can help to narrow the set of candidates when there are dozens or hundreds of partitions in the Pareto efficient set. Another option in such circumstances is to prune the Pareto set to a more manageable set of solutions for the decision analyst to consider (see Brusco, 2017).…”

Section: Bicriterion Local Search Heuristicmentioning

confidence: 99%

Combining diversity and dispersion criteria for anticlustering: A bicriterion approach

Brusco

Cradit

Steinley

2019

Brit J Math & Statis

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Most partitioning methods used in psychological research seek to produce homogeneous groups (i.e., groups with low intra‐group dissimilarity). However, there are also applications where the goal is to provide heterogeneous groups (i.e., groups with high intra‐group dissimilarity). Examples of these anticlustering contexts include construction of stimulus sets, formation of student groups, assignment of employees to project work teams, and assembly of test forms from a bank of items. Unfortunately, most commercial software packages are not equipped to accommodate the objective criteria and constraints that commonly arise for anticlustering problems. Two important objective criteria for anticlustering based on information in a dissimilarity matrix are: a diversity measure based on within‐cluster sums of dissimilarities; and a dispersion measure based on the within‐cluster minimum dissimilarities. In many instances, it is possible to find a partition that provides a large improvement in one of these two criteria with little (or no) sacrifice in the other criterion. For this reason, it is of significant value to explore the trade‐offs that arise between these two criteria. Accordingly, the key contribution of this paper is the formulation of a bicriterion optimization problem for anticlustering based on the diversity and dispersion criteria, along with heuristics to approximate the Pareto efficient set of partitions. A motivating example and computational study are provided within the framework of test assembly.

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“…Zio and Bazzo (2011) aimed to partition the Pareto fronts into clusters and then find a representative solution for each of them for decision‐making. Brusco (2017) applied maximum split and p‐median partitioning to normalized objectives to obtain a unified partition of the Pareto front. Distance‐based methods select the solution that has the shortest distance to a reference or ideal point.…”

Section: Introductionmentioning

confidence: 99%

Shrinking Pareto Fronts to Guide Reservoir Operations by Quantifying Competition Among Multiple Objectives

Wang

Qiu

Fang

et al. 2022

Water Resources Research

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Reservoirs store and regulate natural water resources, playing a vital role in water supply, power generation, flood control, and shipping (Biemans et al., 2011;Chen et al., 2019;Kibler & Tullos, 2013). Consequently, the construction and operation of reservoirs affect the ecosystem, mainly manifesting as changes in hydrological conditions downstream. In most cases, the human-controlled discharge process is more stable, altering the flow regime formed by long-term natural evolution that is required by downstream aquatic ecosystems, thus endangering them (Jiang et al., 2019).Fish have a high trophic level in aquatic ecosystems, and their population size and species richness reflect the changes in the aquatic ecosystems (Mišetić et al., 2003;Wu et al., 2014;Zhao et al., 2015). In some systems, fish spawning coincides with the onset of flooding, and they gather at the spawning grounds to lay eggs when the flood increases and leave after it finishes (Tao et al., 2017). The key ecological requirements for the hydro-environment in the upper reach of the Yellow River in China during the spawning season include the flow regime, as the development of the fish gonads requires stimulation of the flow rising process; the water depth, as the hatching of benthic eggs requires a gentle water flow with little amplitude variation; and water temperature, with the appropriate temperature for fish spawning and egg incubation being approximately 14°C-15°C (Li et al., 2020). Table 2 in Bejarano et al. ( 2017) describes the changes in the environmental and biological responses caused by short-term flow regime changes, including the destruction of the habitats of fish and other species, impact of migration activities, and reduction of species diversity. With increased attention paid to the ecological environment, improving the current reservoir operation schemes and restoring the river's ecological flow processes, especially during critical physiological processes, such as fish spawning and migration, have become key objectives in reservoir operations.The objectives of reservoir operation are often incommensurable or even conflicting. Multiobjective optimization models, which simultaneously consider different objectives of reservoirs, such as power generation and ecological protection, are required to address these problems (Bai et al., 2019;W. He et al., 2020). Multiobjective optimization models are solved to determine the quantitative relationship among objectives and formulate a scheme balancing them (Tang et al., 2019).The existing multiobjective optimization models of reservoir operations, which consider resource exploitation and ecology, mostly use the weighting of each objective to reduce dimensionality or obtain a set of Pareto optimal

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Partitioning methods for pruning the Pareto set with application to multiobjective allocation of a cross-trained workforce

Cited by 14 publications

References 52 publications

Blockchain-IoT-Driven Nursing Workforce Planning for Effective Long-Term Care Management in Nursing Homes

Blockchain-IoT-Driven Nursing Workforce Planning for Effective Long-Term Care Management in Nursing Homes

Combining diversity and dispersion criteria for anticlustering: A bicriterion approach

Shrinking Pareto Fronts to Guide Reservoir Operations by Quantifying Competition Among Multiple Objectives

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