135491 20260410135822.0 doi 10.1007/s10479-024-06017-1 sideral 138653 ART-2025-138653 eng Calvete, Herminia I. Universidad de Zaragoza (orcid)0000-0001-7603-9380 Balancing the cardinality of clusters with a distance constraint: a fast algorithm 2025 Access copy available to the general public Unrestricted Logistics companies partition the customers they serve into delivery zones as a tactical decision and manage the customers assigned to each zone as a cluster for the purpose of routing, workload allocation, etc. Frequently, this partition is made in accordance with customers’ geographical location, which can result in very unbalanced clusters in terms of the number of customers they include. In addition, in the day-to-day operations, not necessarily all customers need to be served every day so, even if the clusters originally created are balanced, daily needs may lead to unbalanced clusters. Given an a priori assignment of customers to clusters, improving the balance between clusters in advance of workload management is therefore a key issue. This paper addresses the problem of balancing clusters, when there is a distance constraint that prevents reassigning customers to clusters far away from their original pre-assignment. This problem is formulated as a lexicographic biobjective optimization model. The highest priority objective function minimizes the variance of the number of customers in the clusters. The second ranked objective function minimizes the total distance resulting from all reassignments. A fast and effective heuristic algorithm is developed, based on exploring customer reassignments, either by comparing clusters two by two or by extending the search to allow for sequential customer swaps among clusters. Both the quality of the solution and the computational time required encourage the use of this algorithm by logistics companies to balance clusters in real scenarios. info:eu-repo/grantAgreement/ES/DGA/E41-20R info:eu-repo/grantAgreement/ES/DGA/E41-23R info:eu-repo/grantAgreement/ES/MICINN/PID2019-104263RB-C43 info:eu-repo/grantAgreement/ES/MICINN/PID2022-139543OB-C43 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 4.5 2024 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 23 / 106 = 0.217 2024 Q1 T1 1.092 2024 Management Science and Operations Research 2024 Q1 Decision Sciences (miscellaneous) 2024 Q1 9.8 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Galé, Carmen Universidad de Zaragoza (orcid)0000-0002-5630-3719 Iranzo, José A. Universidad de Zaragoza (orcid)0000-0001-9993-9816 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 351 (2025), 629-651 Ann. oper. res. ANNALS OF OPERATIONS RESEARCH 0254-5330 1798414 http://zaguan.unizar.es/record/135491/files/texto_completo.pdf Versión publicada 1510305 http://zaguan.unizar.es/record/135491/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:135491 articulos driver 2026-04-10-13:53:49 ARTICLE