{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T01:51:47Z","timestamp":1781574707593,"version":"3.54.5"},"reference-count":19,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hunan Provincial Natural Science Foundation Project","award":["2025JJ70160"],"award-info":[{"award-number":["2025JJ70160"]}]},{"name":"Scientific Fund of Hunan Provincial Education Department","award":["23A0629"],"award-info":[{"award-number":["23A0629"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The selection of locations for logistics distribution centers poses a significant challenge in logistics network planning. Traditional methods often demonstrate limited accuracy in solutions and a tendency to become trapped in local optima when addressing large-scale, multi-constraint location models. To address these shortcomings, this study introduces a firefly algorithm enhanced by genetic mutation strategies (GVFA) to optimize the location of distribution centers. Within the framework of the standard firefly algorithm, we incorporate an adaptive step-size decay mechanism and a mutation operator. The movement step size adjusts dynamically based on iteration counts, while a mutation probability of 5% is implemented to maintain population diversity, effectively reducing the risk of premature convergence. A specialized boundary-handling strategy ensures that the search process remains within the feasible solution space, guiding the population toward the global optimum. Experiments were conducted using latitude\u2013longitude coordinates and logistics demand data from 159 Cainiao Post stations in Hengyang City, resulting in the construction of a location model aimed at minimizing total costs. The findings confirm the efficiency and stability of our method in optimizing distribution center locations, thereby providing a novel intelligent optimization approach for the siting of logistics distribution centers.<\/jats:p>","DOI":"10.3390\/a19060481","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:49:01Z","timestamp":1781570941000},"page":"481","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Logistics Distribution Center Location Problem Based on Genetic Variation Firefly Algorithm"],"prefix":"10.3390","volume":"19","author":[{"given":"Lang","family":"Yang","sequence":"first","affiliation":[{"name":"School of Humanity, Shanghai University of Finance and Economics, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2885-0694","authenticated-orcid":false,"given":"Changan","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang 421002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhangwei","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang 421002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengya","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang 421002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"647","DOI":"10.5198\/jtlu.2024.2438","article-title":"Assessing the spatial footprint of e-commerce logistics differentiating the types of warehouses","volume":"17","author":"Schorung","year":"2024","journal-title":"J. Transp. Land Use"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jayarathna, C.P., Agdas, D., Dawes, L., and Yigitcanlar, T. (2021). Multi-objective optimization for sustainable supply chain and logistics: A review. Sustainability, 13.","DOI":"10.3390\/su132413617"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"G\u00fcven G\u00fcney, B., and Y\u00fczer, M.A. (2025). Location Criteria for E-Commerce Logistics Facilities: A Scale-Sensitive Analysis. Sustainability, 17.","DOI":"10.3390\/su172210115"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"\u00d6zder, E.H. (2025). A Sustainable Multi-Criteria Decision-Making Framework for Online Grocery Distribution Hub Location Selection. Processes, 13.","DOI":"10.3390\/pr13061653"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Huang, Q., Zheng, G., Pan, S., Liao, H., and Jiang, Z. (2025). Layout optimization of multi-level cold chain storage facilities in agricultural producing areas considering type and capacity constraints. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0313062"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"88769","DOI":"10.1109\/ACCESS.2020.2990988","article-title":"A new hybrid algorithm for cold chain logistics distribution center location problem","volume":"8","author":"Dou","year":"2020","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, X.-S. (2009). Firefly algorithms for multimodal optimization. International Symposium on Stochastic Algorithms, Springer.","DOI":"10.1007\/978-3-642-04944-6_14"},{"key":"ref_8","first-page":"933","article-title":"Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing","volume":"3","author":"Priya","year":"2024","journal-title":"Salud Cienc.-Tecnol.-Ser. Conf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"572","DOI":"10.62411\/jcta.12618","article-title":"A Multilevel Digital Image Thresholding Technique Based on an Enhanced Firefly Algorithm with Neighborhood Attraction","volume":"2","author":"Suleiman","year":"2025","journal-title":"J. Comput. Theor. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"23","DOI":"10.5604\/01.3001.0015.9925","article-title":"Research on the site selection and path layout of the logistics distribution center of marine ships based on a mathematical model","volume":"63","author":"Tong","year":"2022","journal-title":"Arch. Transp."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Huang, Y., Wang, X., and Chen, H. (2022). Location selection for regional logistics center based on particle swarm optimization. Sustainability, 14.","DOI":"10.3390\/su142416409"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8541","DOI":"10.1007\/s00500-023-08132-w","article-title":"TODIM-VIKOR method based on hybrid weighted distance under probabilistic uncertain linguistic information and its application in medical logistics center site selection","volume":"27","author":"Lei","year":"2023","journal-title":"Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, L., Tian, Y., Xiang, X., and Zhang, X. (2023). Optimizing Large-Scale Distribution Center Locations During the COVID-19 Quarantine. 2023 IEEE Congress on Evolutionary Computation (CEC), IEEE.","DOI":"10.1109\/CEC53210.2023.10254157"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, P., and Fan, X. (2023). The application of the improved jellyfish search algorithm in a site selection model of an emergency logistics distribution center considering time satisfaction. Biomimetics, 8.","DOI":"10.3390\/biomimetics8040349"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"012005","DOI":"10.1088\/1742-6596\/2665\/1\/012005","article-title":"An improved genetic algorithm with density-guiding operator based on the location selection model of emergency logistics center","volume":"2665","author":"Cao","year":"2023","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3138","DOI":"10.1108\/IMDS-08-2023-0558","article-title":"A cold chain logistics distribution optimization model: Beijing-Tianjin-Hebei region low-carbon site selection","volume":"124","author":"Zhang","year":"2024","journal-title":"Ind. Manag. Data Syst."},{"key":"ref_17","first-page":"1","article-title":"Enhancing logistics optimization: A double-layer site-selection model approach","volume":"36","author":"Wang","year":"2024","journal-title":"J. Organ. End User Comput. (JOEUC)"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhang, J., Zhou, Z., Dai, Y., and Qin, L. (2024). A deep reinforcement learning-based algorithm for multi-objective agricultural site selection and logistics optimization problem. Appl. Sci., 14.","DOI":"10.3390\/app14188479"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TAI.2022.3214181","article-title":"Adaptive neuroevolution with genetic operator control and two-way complexity variation","volume":"4","author":"Behjat","year":"2022","journal-title":"IEEE Trans. Artif. 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