Solving a real-world nurse rostering problem by Simulated Annealing

IF 1.5 Q3 HEALTH CARE SCIENCES & SERVICES Operations Research for Health Care Pub Date : 2023-03-01 DOI:10.1016/j.orhc.2023.100379

Sara Ceschia , Luca Di Gaspero , Vincenzo Mazzaracchio , Giuseppe Policante , Andrea Schaerf

{"title":"Solving a real-world nurse rostering problem by Simulated Annealing","authors":"Sara Ceschia , Luca Di Gaspero , Vincenzo Mazzaracchio , Giuseppe Policante , Andrea Schaerf","doi":"10.1016/j.orhc.2023.100379","DOIUrl":null,"url":null,"abstract":"<div><p>Designing high quality nurse rostering plans is essential for health care facilities in order to guarantee efficiency, safety and quality-of-care balanced with staff well-being.</p><p>We introduce a new real-world formulation for the nurse rostering problem, arising in many Italian healthcare institutions, which has been developed in collaboration with a primary software company in the field. It considers nurses with different skills, special shifts depending on the skills, time work-load limits, and different types of days-off. In addition, preferences and incompatibilities between nurses are taken into account.</p><p>We propose a MIP model and a local search method, driven by a Simulated Annealing metaheuristic, based on a combination of two neighborhoods. The solution method was tested on 34 real-world instances coming from various healthcare institutions in North Italy. The dataset is available at <span>https://bitbucket.org/satt/nrp-instances</span><svg><path></path></svg>, along with our best solutions.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":"36 ","pages":"Article 100379"},"PeriodicalIF":1.5000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Operations Research for Health Care","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211692323000024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}

引用次数: 2

Abstract

Designing high quality nurse rostering plans is essential for health care facilities in order to guarantee efficiency, safety and quality-of-care balanced with staff well-being.

We introduce a new real-world formulation for the nurse rostering problem, arising in many Italian healthcare institutions, which has been developed in collaboration with a primary software company in the field. It considers nurses with different skills, special shifts depending on the skills, time work-load limits, and different types of days-off. In addition, preferences and incompatibilities between nurses are taken into account.

We propose a MIP model and a local search method, driven by a Simulated Annealing metaheuristic, based on a combination of two neighborhoods. The solution method was tested on 34 real-world instances coming from various healthcare institutions in North Italy. The dataset is available at https://bitbucket.org/satt/nrp-instances, along with our best solutions.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

用模拟退火法解决现实世界的护士名册问题

设计高质量的护士名册计划对于保健设施来说至关重要，以便保证效率、安全和护理质量与工作人员的福祉相平衡。我们为许多意大利医疗保健机构中出现的护士名册问题引入了一个新的现实世界公式，该公式是与该领域的主要软件公司合作开发的。它考虑了不同技能的护士，根据技能的特殊班次，时间工作量限制和不同类型的休息日。此外，还考虑到护士之间的偏好和不相容。我们提出了一种基于两个邻域组合的MIP模型和一种由模拟退火元启发式驱动的局部搜索方法。解决方案方法在来自意大利北部不同医疗机构的34个实际实例上进行了测试。该数据集可在https://bitbucket.org/satt/nrp-instances上获得，以及我们的最佳解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊