{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T03:38:33Z","timestamp":1764733113433,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2021YFC3300402","GWVI-11.2-XD39"],"award-info":[{"award-number":["2021YFC3300402","GWVI-11.2-XD39"]}]},{"name":"three-year action plan for strengthening the construction of the public health system in Shanghai","award":["2021YFC3300402","GWVI-11.2-XD39"],"award-info":[{"award-number":["2021YFC3300402","GWVI-11.2-XD39"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>The layout of emergency medical services (EMS) is of vital importance. A well-planned layout significantly impacts the timeliness of response and operational efficiency, which are crucial for saving lives and mitigating injury severity. This paper presents a novel decision support framework for optimizing EMS station layout. Employing the k-means clustering algorithm in combination with the elbow method and silhouette coefficient method, we conduct a clustering analysis on a patient call record dataset. Comprising 166,161 emergency center call records in the Shanghai area over one year, this dataset serves as the basis for our analysis. The analysis results are applied to determine EMS station locations, with the average ambulance patient pickup time as the evaluation criterion. A simulation model is utilized to validate the effectiveness and reliability of the decision-making framework. An experimental analysis reveals that compared with the existing EMS station layout, the proposed framework reduces the average patient pickup time from 11.033 min to 9.661 min, marking a 12.441% decrease. Furthermore, a robustness test of the proposed scheme is carried out. The results indicate that even when some first-aid sites fail, the average response time can still be effectively controlled within 9.9 min. Through this robustness analysis, the effectiveness and reliability of the decision framework are demonstrated, offering more efficient and reliable support for the EMS system.<\/jats:p>","DOI":"10.3390\/systems13020092","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T05:13:43Z","timestamp":1738646023000},"page":"92","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Optimization of EMS Station Layout Based on a New Decision Support Framework"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4516-5605","authenticated-orcid":false,"given":"Peng","family":"Yang","sequence":"first","affiliation":[{"name":"Computer Science and Technology, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bozheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9858-1682","authenticated-orcid":false,"given":"Jingrong","family":"Yang","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1016\/j.promfg.2020.01.396","article-title":"Operations Management in Emergency Medical Services: Response Time in a Brazilian Mobile Emergency Care Service","volume":"39","author":"Colla","year":"2019","journal-title":"Procedia Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1111\/j.1553-2712.2011.01005.x","article-title":"The influence of emergency medical services transport on Emergency Severity Index triage level for patients with abdominal pain","volume":"18","author":"Hiestand","year":"2011","journal-title":"Acad. Emerg. Med. Off. J. Soc. Acad. Emerg. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1177\/0361198118791363","article-title":"Service Location Optimization Model for Improving Rural Emergency Medical Services","volume":"2672","author":"He","year":"2018","journal-title":"Transp. Res. Rec."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.2147\/RMHP.S304475","article-title":"Optimization for locating emergency medical service facilities: A case study for health planning from China","volume":"14","author":"Deng","year":"2021","journal-title":"Risk Manag. Healthc. Policy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/00375497211006175","article-title":"A simulation-optimization algorithm for return strategies in emergency medical systems","volume":"97","author":"Golabian","year":"2021","journal-title":"Simulation"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1177\/00375497221139870","article-title":"A discrete simulation-based optimization approach for multi-period redeployment in emergency medical services","volume":"99","author":"Aboueljinane","year":"2023","journal-title":"Simulation"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103794","DOI":"10.1016\/j.ijdrr.2023.103794","article-title":"Location selection of field hospitals amid COVID-19 considering effectiveness and fairness: A case study of Florida","volume":"93","author":"Alisan","year":"2023","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_8","first-page":"1095","article-title":"Location selection for a Covid-19 field hospital using fuzzy choquet integral method","volume":"12","year":"2021","journal-title":"G\u00fcm\u00fc\u015fhane \u00dcniversitesi Sos. Bilim. Derg."},{"key":"ref_9","first-page":"1","article-title":"Spatial accessibility analysis and location optimization of emergency shelters in Deyang","volume":"14","author":"Zhang","year":"2023","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_10","first-page":"1183","article-title":"Optimum Location of Field Hospitals for COVID-19: A Nonlinear Binary Metaheuristic Algorithm","volume":"68","author":"Hassan","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103036","DOI":"10.1016\/j.omega.2024.103036","article-title":"Decision support framework for tactical emergency medical service location planning","volume":"125","author":"Grot","year":"2024","journal-title":"Omega"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"23069","DOI":"10.1109\/ACCESS.2020.2967797","article-title":"Study on the Location of Private Clinics Based on K-Means Clustering Method and an Integrated Evaluation Model","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Han, B., Hu, M., and Wang, J. (2020). Site selection for pre-hospital emergency stations based on the actual spatiotemporal demand: A case study of Nanjing City, China. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9100559"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Z. (2023, January 24\u201326). Base station planning problem based on genetic algorithm and K-Means clustering algorithm. Proceedings of the 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), Changchun, China.","DOI":"10.1109\/EEBDA56825.2023.10090812"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Luo, R. (2023, January 24\u201326). A study of base station establishment site selection based on cluster analysis. Proceedings of the 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), Changchun, China.","DOI":"10.1109\/EEBDA56825.2023.10090850"},{"key":"ref_16","first-page":"16","article-title":"Spatial clustering mining method for site selection problem of emergency response center","volume":"11","author":"Fan","year":"2008","journal-title":"J. Manag. Sci. China"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110815","DOI":"10.1016\/j.enbuild.2021.110815","article-title":"Data-driven optimization of building layouts for energy efficiency","volume":"238","author":"Sonta","year":"2021","journal-title":"Energy Build."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1049\/cps2.12021","article-title":"Hierarchical clustering framework for facility location selection with practical constraints","volume":"6","author":"Lin","year":"2021","journal-title":"IET Cyber-Phys. Syst. Theory Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"207","DOI":"10.2147\/PPA.S155603","article-title":"Research of an emergency medical system for mass casualty incidents in Shanghai, China: A system dynamics model","volume":"12","author":"Yu","year":"2018","journal-title":"Patient Prefer. Adherence"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wu, I.C., Lin, Y.C., Yien, H.W., and Shih, F.Y. (2020). Constructing constraint-based simulation system for creating emergency evacuation plans: A case of an outpatient chemotherapy area at a cancer medical center. Healthcare, 8.","DOI":"10.3390\/healthcare8020137"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1177\/0037549718776765","article-title":"Modeling and simulation framework for value-based healthcare systems","volume":"95","author":"Zacharewicz","year":"2019","journal-title":"Simulation"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s10729-014-9280-4","article-title":"Reducing Emergency Medical Service response time via the reallocation of ambulance bases","volume":"19","author":"Nogueira","year":"2016","journal-title":"Health Care Manag. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104174","DOI":"10.1016\/j.ijmedinf.2020.104174","article-title":"A Simulation-Optimisation approach for hospital beds allocation","volume":"141","author":"Oliveira","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s40537-020-00313-w","article-title":"SICE: An improved missing data imputation technique","volume":"7","author":"Khan","year":"2020","journal-title":"J. Big Data"},{"key":"ref_25","first-page":"323","article-title":"Missing values in big data research: Some basic skills","volume":"3","author":"Zhang","year":"2015","journal-title":"Ann. Transl. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","article-title":"Data clustering: 50 years beyond K-Means","volume":"31","author":"Anil","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1007\/s40745-022-00428-2","article-title":"An improved K-means clustering algorithm towards an efficient data-driven modeling","volume":"11","author":"Zubair","year":"2022","journal-title":"Ann. Data Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Seraj, R., and Islam SM, S. (2020). The k-means algorithm: A comprehensive survey and performance evaluation. Electronics, 9.","DOI":"10.3390\/electronics9081295"},{"key":"ref_29","first-page":"6041484","article-title":"Design and Implementation of an Improved K-Means Clustering Algorithm","volume":"2022","author":"Huiling","year":"2022","journal-title":"Mob. Inf. Syst."},{"key":"ref_30","first-page":"9","article-title":"Research on the number of clusters in K-means clustering algorithm","volume":"25","author":"Liu","year":"2017","journal-title":"Electron. Des. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lei, T., and Li, S. (2021, January 23\u201325). Improved K-means clustering algorithm by combining with multiple factors. Proceedings of the 2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC), Shanghai, China.","DOI":"10.1109\/CTISC52352.2021.00054"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109255","DOI":"10.1016\/j.ress.2023.109255","article-title":"Probabilistic framework for evaluating the seismic resilience of transportation systems during emergency medical response","volume":"236","author":"Taghizadeh","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1016\/S0731-7085(00)00529-X","article-title":"Guidance for robustness\/ruggedness tests in method validation","volume":"24","author":"Nijhuis","year":"2001","journal-title":"J. Pharm. Biomed. Anal."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/2\/92\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:25:05Z","timestamp":1760027105000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/2\/92"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,31]]},"references-count":33,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["systems13020092"],"URL":"https:\/\/doi.org\/10.3390\/systems13020092","relation":{},"ISSN":["2079-8954"],"issn-type":[{"type":"electronic","value":"2079-8954"}],"subject":[],"published":{"date-parts":[[2025,1,31]]}}}