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Author Ritt, M.; Pereira, J. doi  openurl
  Title Heuristic and exact algorithms for minimum-weight non-spanning arborescences Type
  Year 2020 Publication European Journal Of Operational Research Abbreviated Journal Eur. J. Oper. Res.  
  Volume 287 Issue 1 Pages 61-75  
  Keywords Minimum-weight non-spanning arborescence; Heuristic; Iterated Local Search; Branch-and-cut  
  Abstract 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. (C) 2020 Elsevier B.V. All rights reserved.  
  Address [Ritt, Marcus] Univ Fed Rio Grande do Sul, Inst Informat, Av Bento Goncalves 9500, Porto Alegre, RS, Brazil, Email: marcus.ritt@inf.ufrgs.br;  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0377-2217 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000541072800005 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1187  
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Author Pereira, J.; Ritt, M.; Vasquez, O.C. pdf  doi
openurl 
  Title A memetic algorithm for the cost-oriented robotic assembly line balancing problem Type
  Year 2018 Publication Computers & Operations Research Abbreviated Journal Comput. Oper. Res.  
  Volume 99 Issue Pages 249-261  
  Keywords Line balancing; Cost-oriented line balancing; Robotic assembly line; Hybrid algorithms  
  Abstract In order to minimize costs, manufacturing companies have been relying on assembly lines for the mass production of commodity goods. Among other issues, the successful operation of an assembly line requires balancing work among the stations of the line in order to maximize its efficiency, a problem known in the literature as the assembly line balancing problem, ALBP. In this work, we consider an ALBP in which task assignment and equipment decisions are jointly considered, a problem that has been denoted as the robotic ALBP. Moreover, we focus on the case in which equipment has different costs, leading to a cost-oriented formulation. In order to solve the problem, which we denote as the cost-oriented robotic assembly line balancing problem, cRALBP, a hybrid metaheuristic is proposed. The metaheuristic embeds results obtained for two special cases of the problem within a genetic algorithm in order to obtain a memetic algorithm, applicable to the general problem. An extensive computational experiment shows the advantages of the hybrid approach and how each of the components of the algorithm contributes to the overall ability of the method to obtain good solutions. (C) 2018 Elsevier Ltd. All rights reserved.  
  Address [Pereira, Jordi] Univ Adolfo Ibanez, Fac Engn & Sci, Av Padre Hurtado 750,Off C216, Vina Del Mar, Chile, Email: jorge.pereira@uai.cl;  
  Corporate Author Thesis  
  Publisher Pergamon-Elsevier Science Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0305-0548 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000442059400019 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 907  
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