author = "Mauri, Regis Mauri and Ribeiro, Glaydston Mattos and Lorena, Luiz 
                         Antonio Nogueira and Laporte, Gilbert",
          affiliation = "{Universidade Federal do Esp{\'{\i}}rito Santo (UFES)} and 
                         {Universidade Federal do Rio de Janeiro (UFRJ)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {CIRRELT and HEC 
                title = "An adaptive large neighborhood search for the discrete and 
                         continuous Berth allocation problem",
              journal = "Computers \& Operations Research",
                 year = "2016",
               volume = "70",
                pages = "140--154",
                month = "June",
             keywords = "Berth allocation problem, Metaheuristic, Adaptive large 
                         neighborhood search.",
             abstract = "The Berth Allocation Problem (BAP) consists of assigning ships to 
                         berthing positions along a quay in a port. The choice of where and 
                         when the ships should move is the main. decision to be made in 
                         this problem. Considering the berthing positions, there are 
                         restrictions related to the water depth and the size of the ships 
                         among others. There are also restrictions related to the berthing 
                         time of the ships which are modeled as time windows. In this work 
                         the ships are represented as rectangles to be placed into a space 
                         x time area, avoiding overlaps and satisfying time window 
                         constraints. We consider discrete and continuous models for the 
                         BAP and we propose an Adaptive Large Neighborhood Search heuristic 
                         to solve the problem. Computational experiments indicate that the 
                         proposed algorithm is capable of generating high-quality solutions 
                         and outperforms competing algorithms for the same problem. In most 
                         cases the improvements are statistically significant.",
                  doi = "10.1016/j.cor.2016.01.002",
                  url = "http://dx.doi.org/10.1016/j.cor.2016.01.002",
                 issn = "0305-0548",
             language = "en",
           targetfile = "mauri_adaptive.pdf",
        urlaccessdate = "27 nov. 2020"