TY - JOUR
T1 - Heuristic and exact algorithms for minimum-weight non-spanning arborescences
AU - Ritt, Marcus
AU - Pereira, Jordi
N1 - Funding Information:
Our research has been supported by CNPq (grant 420348/2016-6 ), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil ( CAPES ) - Finance Code 001, by Google Research Latin America (grant 25111 ) and by the Chilean Council of Scientific and Technological Research ( CONICYT ) through Fondecyt (grant 1180670 ).
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - We address the problem of finding an arborescence of minimum total edge weight rooted at a given vertex in a directed, edge-weighted graph. If the arborescence must span all vertices the problem is solvable in polynomial time, but the non-spanning version is NP-hard. We propose reduction rules which determine vertices that are required or can be excluded from optimal solutions, a modification of Edmonds algorithm to construct arborescences that span a given set of selected vertices, and embed this procedure into an iterated local search for good vertex selections. Moreover, we propose a cutset-based integer linear programming formulation, provide different linear relaxations to reduce the number of variables in the model and solve the reduced model using a branch-and-cut approach. We give extensive computational results showing that both the heuristic and the exact methods are effective and obtain better solutions on instances from the literature than existing approaches, often in much less time.
AB - We address the problem of finding an arborescence of minimum total edge weight rooted at a given vertex in a directed, edge-weighted graph. If the arborescence must span all vertices the problem is solvable in polynomial time, but the non-spanning version is NP-hard. We propose reduction rules which determine vertices that are required or can be excluded from optimal solutions, a modification of Edmonds algorithm to construct arborescences that span a given set of selected vertices, and embed this procedure into an iterated local search for good vertex selections. Moreover, we propose a cutset-based integer linear programming formulation, provide different linear relaxations to reduce the number of variables in the model and solve the reduced model using a branch-and-cut approach. We give extensive computational results showing that both the heuristic and the exact methods are effective and obtain better solutions on instances from the literature than existing approaches, often in much less time.
KW - Branch-and-cut
KW - Heuristic
KW - Iterated Local Search
KW - Minimum-weight non-spanning arborescence
UR - http://www.scopus.com/inward/record.url?scp=85085769279&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2020.03.073
DO - 10.1016/j.ejor.2020.03.073
M3 - Article
AN - SCOPUS:85085769279
SN - 0377-2217
VL - 287
SP - 61
EP - 75
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 1
ER -