A Variable Neighbourhood Search for Nurse Scheduling with Balanced Preference Satisfaction

Constantino, Ademir Aparecido; Tozzo, Everton; Pinheiro, Rodrigo Lankaites; Landa-Silva, Dario; Romão, Wesley

doi:10.5220/0005364404620470

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…The scheduling algorithm should ideally support the participants in their decision-making, but not decide on their behalf. Notably, we found this effect despite the fact that both the algorithm-based and collaborative decisions were briefly explained to the healthcare workers in each vignette, thus providing higher explainability than existing systems [11,15].…”

Section: Summary and Discussionmentioning

confidence: 70%

“…Three in-depth interviews and an experimental vignette study helped us draw a detailed picture of subjective fairness in shift scheduling. The oversimplified concept of fairness as equality of outcomes underlying the majority of shift scheduling systems (e.g., [11,36]), does not capture healthcare workers' understanding of fairness. While equality was the preferred norm on an abstract level, it is not deemed appropriate for resolving concrete scheduling conflicts.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…One particular group are digital scheduling systems, which provide algorithmic support for solving shift planning problems (e.g., [21,26]). New algorithms have been developed to increase flexibility for the healthcare workers during planning and to better satisfy their preferences [10,11,13,37,36]. However, this line of research approaches the shift scheduling problem mainly from a technical perspective and the healthcare workers' perspective is considered only secondarily.…”

Section: Hospital Managementmentioning

confidence: 99%

“…Besides many studies mainly considering technical implementations, some user studies have been carried out e.g., for computer-supported rent division [20,22,33], court decisions [25], a dispatching system [35], and university course allocation [42]. However, with a few exceptions ( [25,33,35]), these studies assume a model of "objective fairness", similar to the planning algorithms [11,36]. This line of thought envisions a mathematically determined distribution of resources between groups or individuals, to provide "provably fair" solutions [22].…”

Section: Fairnessmentioning

confidence: 99%

“…Current technical solutions (i.e., shift scheduling algorithms) neglect these facets of fairness and the majority is implicitly based on the equality norm [11,13,36]. In one case an equity norm [37] was applied.…”

Section: Fairnessmentioning

confidence: 99%

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Fairness and Decision-making in Collaborative Shift Scheduling Systems

Uhde

Schlicker²,

Wallach³

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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The strains associated with shift work decrease healthcare workers' well-being. However, shift schedules adapted to their individual needs can partially mitigate these problems. From a computing perspective, shift scheduling was so far mainly treated as an optimization problem with little attention given to the preferences, thoughts, and feelings of the healthcare workers involved. In the present study, we explore fairness as a central, human-oriented attribute of shift schedules as well as the scheduling process. Three in-depth qualitative interviews and a validating vignette study revealed that while on an abstract level healthcare workers agree on equality as the guiding norm for a fair schedule, specific scheduling conflicts should foremost be resolved by negotiating the importance of individual needs. We discuss elements of organizational fairness, including transparency and team spirit. Finally, we present a sketch for fair scheduling systems, summarizing key findings for designers in a readily usable way.

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Section: Summary and Discussionmentioning

confidence: 70%

Section: Summary and Discussionmentioning

confidence: 99%

Section: Hospital Managementmentioning

confidence: 99%

Section: Fairnessmentioning

confidence: 99%

Section: Fairnessmentioning

confidence: 99%

See 3 more Smart Citations

Fairness and Decision-making in Collaborative Shift Scheduling Systems

Uhde

Schlicker²,

Wallach³

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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An Implicit Learning Approach for Solving the Nurse Scheduling Problem

Said

Mohammed

Mouhoub

2021

Lecture Notes in Computer Science

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Solving combinatorial optimization problems involve satisfying a set of hard constraints while optimizing some objectives. In this context, exact or approximate methods can be used. While exact methods guarantee the optimal solution, they often come with an exponential running time as opposed to approximate methods that trade the solution's quality for a better running time. In this context, we tackle the Nurse Scheduling Problem (NSP). The NSP consist in assigning nurses to daily shifts within a planning horizon such that workload constraints are satisfied while hospital's costs and nurses' preferences are optimized. To solve the NSP, we propose implicit and explicit approaches. In the implicit solving approach, we rely on Machine Learning methods using historical data to learn and generate new solutions through the constraints and objectives that may be embedded in the learned patterns. To quantify the quality of using our implicit approach in capturing the embedded constraints and objectives, we rely on the Frobenius Norm, a quality measure used to compute the average error between the generated solutions and historical data. To compensate for the uncertainty related to the implicit approach given that the constraints and objectives may not be concretely visible in the produced solutions, we propose an alternative explicit approach where we first model the NSP using the Constraint Satisfaction Problem (CSP) framework. Then we develop Stochastic Local Search methods and a new Branch and Bound algorithm enhanced with constraint propagation techniques and variables/values ordering heuristics. Since our implicit approach may not guarantee the feasibility or optimality of the generated solution, we propose a data-driven approach to passively learn the NSP as a constraint network. The learned constraint network, formulated as a CSP, will then be solved using the methods we listed earlier.

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