Clustering Search for the Berth Allocation Problem

Oliveira, Rudinei Martins de; Mauri, Geraldo Regis; Lorena, Luiz Antônio Nogueira

doi:10.1016/j.eswa.2011.11.072

Cited by 69 publications

(37 citation statements)

References 14 publications

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“…However, as highlighted in [11], GSPP can require large amounts of memory and computational time, depending on the complexity of the instances. In this regard, a Clustering Search with Simulated Annealing (CS-SA) that is able to provide the optimal solutions in all the cases and outperforms the GSPP time behaviour is presented in [6]. The results shown in the table related to this algorithm correspond to the best objective function values obtained and the average computational time required for 5 tests.…”

Section: Computational Resultsmentioning

confidence: 99%

“…− Clustering Search with Simulated Annealing for generating initial solutions (CS-SA) [6] − Particle Swarm Optimization (PSO) [13] − Decentralized Cooperative Metaheuristic (DCM) Table 2 shows the computational results obtained by applying these solution approaches. The mathematical formulation GSPP implemented by using CPLEX 2 in [2] provides the optimal solution in 17.92 seconds in the worst case.…”

Section: Computational Resultsmentioning

confidence: 99%

“…The authors also proposed a benchmark suite of instances for which the model from [3] does not provide feasible solutions within a time limit. [6] presents a Clustering Search (CS-SA) with Simulated Annealing for solving the DBAP. This algorithm provides the optimal solutions for all the largest instances proposed in [5].…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Decentralized Cooperative Metaheuristic for the Dynamic Berth Allocation Problem

Lalla-Ruiz¹,

Expósito‐Izquierdo²,

Batista³

et al. 2015

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The increasing demand of maritime transport and the great competition among port terminals force their managers to reduce costs by exploiting its resources accurately. In this environment, the Berth Allocation Problem, which aims to allocate and schedule incoming vessels along the quay, plays a relevant role in improving the overall terminal productivity. In order to address this problem, we propose Decentralized Cooperative Metaheuristic (DCM), which is a population-based approach that exploits the concepts of communication and grouping. In DCM, the individuals are organized into groups, where each individual shares information with its group partners. This grouping strategy allows to diversify as well as intensify the search in some regions by means of information shared among the individuals of each group. Moreover, the constrained relation for sharing information among individuals through the proposed grouping strategy allows to reduce computational resources in comparison to the 'all to all' communication strategy. The computational experiments for this problem reveal that DCM reports high-quality solutions and identifies promising regions within the search space in short computational times.

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Section: Computational Resultsmentioning

confidence: 99%

Section: Computational Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Decentralized Cooperative Metaheuristic for the Dynamic Berth Allocation Problem

Lalla-Ruiz¹,

Expósito‐Izquierdo²,

Batista³

et al. 2015

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“…Tal problema tem sido objeto de estudo de muitos pesquisadores nosúltimos anos, como em [1], [2] e [4], tendo em vista o forte impacto do setor de transporte marítimo no comércio mundial.…”

Section: Introductionunclassified

Heurística GRASP para o Problema de Alocação de Navios em Berços

Romagnome

Gomes-Ruggiero

Moretti

2015

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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O GRASP consiste de duas fases, a primeira, construtiva e a segunda de busca local. Para a primeira fase, foi elaborada uma estratégia para obtenção de uma solução viável para o problema original. A cada etapa,é construída uma lista de navios candidatos a serem alocados em um dos berços. A seleção de navios para compor a lista de candidatosé feita através de uma função de mérito, introduzida neste trabalho, que visa quantificar a urgência de atendimento de cada navio, ponderando tempo de atendimento e de espera, custo associado ao navio, horários de funcionamento dos berços e a janela de tempo de cada navio.Obtido um conjunto de soluções viáveis, são então selecionadas as de menor custo e acionado um processo de intensificação, visando obter, a partir desta solução, umótimo local.A heurística para a fase construtiva foi já testada computacionalmente. Realizamos testes com 7, 10, 20, 40, 60 navios para serem alocados em, respectivamente, 2, 3, 5, 8, 13 berços. Em todos os * Aluno de Mestrado/CAPES

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“…The problem is solved by a heuristic algorithm which combines Tabu search methods and mathematical programming techniques. To solve the discrete BAP more efficiently, de Oliveira et al (2012) propose a hybrid Clustering Search method, which is found faster than the existing methods in the literature.…”

Section: Integrating Truck Arrival Management Into Tactical Operationmentioning

confidence: 99%

Integrating truck arrival management into tactical operation planning at container terminals

Yang¹,

Chen²,

Song³

2013

Polish Maritime Research

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As an intermodal interface, marine container terminals serve vessels on the sea side and trucks/trains on the land side. Operating a container terminal involves many different decisions and they often interact with each other. Due to the multi-criteria nature, the complexity of operations, and the size of the operations management problem, it is extremely difficult to make the optimal decisions for the entire terminal system (Zhang et al., 2003). Traditionally, the whole system is decomposed into a set of sub-planning problems of manageable complexity. The sub-problems may be solved in a sequential fashion, in which the output of one sub-problem is treated as the input of another sub-problem. This sequential solution enables a clear hierarchy of decision making, but on the other hand, ignores the interrelations between the sub-problems and often leads to plans of poor overall quality (Bierwirth and Meisel, 2010). In order to find better planning decisions, it is necessary to integrate some of the sub-planning problems and optimize them simultaneously at a reasonable planning level, as mentioned by Stahlbock and Voss (2008) that "improved terminal performance cannot necessarily be obtained by solving isolated problems but by an integration of various operations connected to each other."Many container terminals in Asia have a typical layout as shown in Fig. 1, which consists of three parts: the seaside area, the yard storage area and the landside area. The seaside area is the place where vessels are berthed and operated by quay cranes. The landside area, also called gate house, is the entrance and exit place for external trucks (XTs). Between the seaside and the landside areas is the storage yard, which stores inbound (I/B) and outbound (O/B) containers temporarily because there are time differences between vessel arrivals and land-carrier arrivals (Meisel, 2009

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Clustering Search for the Berth Allocation Problem

Cited by 69 publications

References 14 publications

Decentralized Cooperative Metaheuristic for the Dynamic Berth Allocation Problem

Decentralized Cooperative Metaheuristic for the Dynamic Berth Allocation Problem

Heurística GRASP para o Problema de Alocação de Navios em Berços

Integrating truck arrival management into tactical operation planning at container terminals

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