000135491 001__ 135491
000135491 005__ 20250923084429.0
000135491 0247_ $$2doi$$a10.1007/s10479-024-06017-1
000135491 0248_ $$2sideral$$a138653
000135491 037__ $$aART-2024-138653
000135491 041__ $$aeng
000135491 100__ $$0(orcid)0000-0001-7603-9380$$aCalvete, Herminia I.$$uUniversidad de Zaragoza
000135491 245__ $$aBalancing the cardinality of clusters with a distance constraint: a fast algorithm
000135491 260__ $$c2024
000135491 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135491 5203_ $$aLogistics 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.
000135491 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E41-20R$$9info:eu-repo/grantAgreement/ES/DGA/E41-23R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-104263RB-C43$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-139543OB-C43
000135491 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000135491 590__ $$a4.5$$b2024
000135491 592__ $$a1.092$$b2024
000135491 591__ $$aOPERATIONS RESEARCH & MANAGEMENT SCIENCE$$b23 / 106 = 0.217$$c2024$$dQ1$$eT1
000135491 593__ $$aManagement Science and Operations Research$$c2024$$dQ1
000135491 593__ $$aDecision Sciences (miscellaneous)$$c2024$$dQ1
000135491 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135491 700__ $$0(orcid)0000-0002-5630-3719$$aGalé, Carmen$$uUniversidad de Zaragoza
000135491 700__ $$0(orcid)0000-0001-9993-9816$$aIranzo, José A.$$uUniversidad de Zaragoza
000135491 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000135491 773__ $$g(2024), [22 pp.]$$pAnn. oper. res.$$tANNALS OF OPERATIONS RESEARCH$$x0254-5330
000135491 8564_ $$s1798414$$uhttps://zaguan.unizar.es/record/135491/files/texto_completo.pdf$$yVersión publicada
000135491 8564_ $$s1510305$$uhttps://zaguan.unizar.es/record/135491/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135491 909CO $$ooai:zaguan.unizar.es:135491$$particulos$$pdriver
000135491 951__ $$a2025-09-22-14:42:11
000135491 980__ $$aARTICLE