ArticleScience progress2021
A heuristic algorithm for medical staff's scheduling problems with multiskills and vacation control.
Article in Science progress, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 5 citations in OpenAlex.
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe main issue related to the duty schedule is to allocate medical staff to each medical department by considering personnel skills and personal vocation preferences. However, how to effectively use staff's multiskill characteristics and how to execute vocation control have not been well investigated.
objectivesThis article aims to develop duty scheduling and vacation permission decisions to minimize the sum of customers' waiting costs, the overtime cost of medical staff, the cost of failing to meet medical staff' vacation requirements, and the cost of mutual support between departments.
methodsThis study formulated the problem as a multiperiod mixed integer nonlinear programming model and developed a hybrid heuristic based on evolutionary mechanism of genetic algorithm and linear programming to efficiently solve the proposed model.
resultsFive types of problems were solved through Lingo optimization and the proposed approach. For small-scale problems, both methods can find the optimal solutions. For a slightly larger problem, the solutions found by the proposed approach are superior those of Lingo.
conclusionThis research discusses the complex decision-making problem of on-duty arrangement and vacation control of medical staff in a multidepartmental medical center. This research formulates the medical staff's scheduling and vacation control problems as constrained mixed integer quadratic programming problems. Computational results indicate that the proposed approach can efficiently produce compromise solutions that outperform the solutions of the Lingo optimization software.
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Registered trials
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