Modeling and solving staff scheduling with partial weighted maxSAT

Demirović, Emir; Musliu, Nysret; Winter, Felix

doi:10.1007/s10479-017-2693-y

Cited by 24 publications

(14 citation statements)

References 12 publications

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“…Existing results on this dataset for comparison are shown in Table IV, including the work of [24] who model the NRP using Partial Weighted maxSAT and solve it using the WPM3 algorithm of [27] for 4 hours runtime, both an ejection chain heuristic method (reported after 10 and 60 minutes, solutions after 60 minutes shown) and a branch-and-price method implemented by [22] as in [23], and finally results for a complete integer programming implementation of the problem instances from [22], run on Gurobi 7.0 with a 1 hour runtime limit. The best results for these methods are compared with the average result of 10 runs of our ACO-IP approach in Table IV.…”

Section: Comparison Of Resultsmentioning

confidence: 99%

“…Three methods are applied to the NRP dataset in [22], the ejection chain and branch-and-price from [23], and solving the formulation provided with the integer programming software Gurobi 5.6.3. The instances of the NRP dataset have also been solved as a partially weighted maxSAT problem [24] The objective is to minimise the weighted sum of undercover, overcover, and not satisfied nurse shift preferences. This is subject to 10 requirements (with their respective category: sequence, schedule, or roster):…”

Section: Nurse Rostering Problemmentioning

confidence: 99%

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An Integer Programming based Ant Colony Optimisation Method for Nurse Rostering

Bunton¹,

Ernst²,

Krishnamoorthy³

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-Nurse rostering problems are typically too large and hard to be solved exactly. In order to achieve quality solutions to these difficult problems, meta-heuristics are often employed. One such meta-heuristic is Ant Colony Optimisation (ACO), inspired by the pheromone trails left by ants. ACO works by guiding a heuristic solution construction by using these pheromones to direct weighted random choices. When the problem to be solved is highly constrained, finding feasible solutions is difficult, which can result in poor performance for ACO. To address this, we propose an ACO algorithm using an integer programming based solution construction method to ensure feasibility and select from a collection of schedules. The approach also uses a novel solution merging step that combines the information from multiple ants to generate a better final roster. We discuss several challenges inherent in this approach, and how they may be overcome. Computational results on highly constrained nurse rostering problem instances from the literature demonstrate the effectiveness of our proposed new hybrid metaheuristic.

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Section: Comparison Of Resultsmentioning

confidence: 99%

Section: Nurse Rostering Problemmentioning

confidence: 99%

An Integer Programming based Ant Colony Optimisation Method for Nurse Rostering

Bunton¹,

Ernst²,

Krishnamoorthy³

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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“…[13] There is extensive literature on staffing and scheduling. [14][15][16][17][18][19] However, there is less attention in the literature regarding how managers can keep track of providers' time allotment or how a tool could assist in managing physician schedules, and equitably assigning clinical and non-clinical time as well as vacation and other time away. This is essential to help manage burnout and balance workload.…”

Section: Introductionmentioning

confidence: 99%

Provider time allotment tracking tool to effectively manage assignment commitments

Huang

Shahraki

Wallin

et al. 2021

JHA

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Due to the rising demand with limited health service capacity, managing available resources effectively becomes an important task to reduce patient care delays and avoid unnecessary and costly capacity expansions. At the same time, staff satisfaction and/or burnout is a complementary consideration when designing optimal schedules. Deviation from the scheduled plan can cause delays in patient access and may lead to unsatisfaction among providers. Balancing demand management, staff satisfaction and generating optimized schedules quickly reveals the need for a tool that tracks provider time allotment over time, especially for the academic healthcare organization where providers are committed to multiple assignments, clinical and non-clinical. This tracking tool should allow management to proactively adjust allotment to unplanned changes in the schedule and increase participation. In this study, a tool is developed to track monthly provider assignments for the Department of Cardiovascular Medicine at Mayo Clinic, Rochester. The proposed tool produces two key outputs for each provider and assignment: 1) the recommended target workdays and 2) workday upper and lower bounds to accommodate for variability. This tracking tool is successfully implemented with implementation criteria, and the feedback is positive. The tool pulls the data systematically from the Mayo data platform and performs the necessary analysis on the data. It also automatically updates the values for the recommended target as well as upper and lower bounds for the remaining months in a year based on changes in the schedule so that provider commitment can be met at the end of year.

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“…There is a remarkable number of algorithms for (W)PMS including complete solvers (Davies and Bacchus 2011;Ansótegui, Bonet, and Levy 2013;Narodytska and Bacchus 2014;Martins, Manquinho, and Lynce 2014;Ansótegui and Gabàs 2017) and incomplete solvers (Cai et al 2014;2016;Luo et al 2017;Lei and Cai 2018). Due to the success of these works, there has been much interest in solving combinatorial optimization problems by this kind of logical language (Demirovic and Musliu 2017;Jiang et al 2018;Demirovic, Musliu, and Winter 2019).…”

Section: Introductionmentioning

confidence: 99%

Solving Set Cover and Dominating Set via Maximum Satisfiability

Lei

2020

AAAI

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The Set Covering Problem (SCP) and Dominating Set Problem (DSP) are NP-hard and have many real world applications. SCP and DSP can be encoded into Maximum Satisfiability (MaxSAT) naturally and the resulting instances share a special structure. In this paper, we develop an efficient local search solver for MaxSAT instances of this kind. Our algorithm contains three phrase: construction, local search and recovery. In construction phrase, we simplify the instance by three reduction rules and construct an initial solution by a greedy heuristic. The initial solution is improved during the local search phrase, which exploits the feature of such instances in the scoring function and the variable selection heuristic. Finally, the corresponding solution of original instance is recovered in the recovery phrase. Experiment results on a broad range of large scale instances of SCP and DSP show that our algorithm significantly outperforms state of the art solvers for SCP, DSP and MaxSAT.

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Modeling and solving staff scheduling with partial weighted maxSAT

Cited by 24 publications

References 12 publications

An Integer Programming based Ant Colony Optimisation Method for Nurse Rostering

An Integer Programming based Ant Colony Optimisation Method for Nurse Rostering

Provider time allotment tracking tool to effectively manage assignment commitments

Solving Set Cover and Dominating Set via Maximum Satisfiability

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