METSlib

The METSlib QAP solver is a Tabu Search solver for the quadratic assignment problem, a combinatorial optimization problem that arises in many applicative cases. It can be used to find optimal locations for a set of facilities while minimizing the cost of moving commodities between them, to optimize the placement of components on a circuit board, and for many other applications. This software is based on the METSlib framework. (Source: http://freecode.com/)


References in zbMATH (referenced in 30 articles , 1 standard article )

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  1. Schryen, Guido: Parallel computational optimization in operations research: a new integrative framework, literature review and research directions (2020)
  2. Kramer, Arthur; Subramanian, Anand: A unified heuristic and an annotated bibliography for a large class of earliness-tardiness scheduling problems (2019)
  3. Penna, Puca Huachi Vaz; Subramanian, Anand; Ochi, Luiz Satoru; Vidal, Thibaut; Prins, Christian: A hybrid heuristic for a broad class of vehicle routing problems with heterogeneous fleet (2019)
  4. Pessoa, Artur; Sadykov, Ruslan; Uchoa, Eduardo: Enhanced branch-cut-and-price algorithm for heterogeneous fleet vehicle routing problems (2018)
  5. Stodola, Petr: Using metaheuristics on the multi-depot vehicle routing problem with modified optimization criterion (2018)
  6. Chen, Yujie; Cowling, Peter; Polack, Fiona; Remde, Stephen; Mourdjis, Philip: Dynamic optimisation of preventative and corrective maintenance schedules for a large scale urban drainage system (2017)
  7. Galligari, Alessandro; Maischberger, Mirko; Schoen, Fabio: Local search heuristics for the zone planning problem (2017)
  8. Ma, Fuda; Hao, Jin-Kao; Wang, Yang: An effective iterated tabu search for the maximum bisection problem (2017)
  9. Paraskevopoulos, Dimitris C.; Laporte, Gilbert; Repoussis, Panagiotis P.; Tarantilis, Christos D.: Resource constrained routing and scheduling: review and research prospects (2017)
  10. Schneider, Michael; Schwahn, Fabian; Vigo, Daniele: Designing granular solution methods for routing problems with time windows (2017)
  11. Silvestrin, Paulo Vitor; Ritt, Marcus: An iterated tabu search for the multi-compartment vehicle routing problem (2017)
  12. Bae, Heechul; Moon, Ilkyeong: Multi-depot vehicle routing problem with time windows considering delivery and installation vehicles (2016)
  13. Doppstadt, C.; Koberstein, A.; Vigo, D.: The hybrid electric vehicle-traveling salesman problem (2016)
  14. Li, Jian; Li, Yang; Pardalos, Panos M.: Multi-depot vehicle routing problem with time windows under shared depot resources (2016)
  15. Cherif-Khettaf, Wahiba Ramdane; Rachid, Mais Haj; Bloch, Christelle; Chatonnay, Pascal: New notation and classification scheme for vehicle routing problems (2015)
  16. Hemmelmayr, Vera C.: Sequential and parallel large neighborhood search algorithms for the periodic location routing problem (2015)
  17. Juan, Angel A.; Pascual, IƱaki; Guimarans, Daniel; Barrios, Barry: Combining biased randomization with iterated local search for solving the multidepot vehicle routing problem (2015)
  18. Wang, Yong; Ma, Xiaolei; Xu, Maozeng; Wang, Yinhai; Liu, Yong: Vehicle routing problem based on a fuzzy customer clustering approach for logistics network optimization (2015)
  19. Campbell, Ann Melissa; Wilson, Jill Hardin: Forty years of periodic vehicle routing (2014)
  20. Contardo, Claudio; Martinelli, Rafael: A new exact algorithm for the multi-depot vehicle routing problem under capacity and route length constraints (2014)

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