The continuous Berth Allocation Problem in a container terminal with multiple quays

Frojan, Pablo; Correcher, Juan Francisco; Álvarez-Valdés, Ramón; Koulouris, Gerasimos; Tamarit, José Manuel

doi:10.1016/j.eswa.2015.05.018

Cited by 56 publications

(49 citation statements)

References 23 publications

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“…The fitness of the initial population chromosomes is assessed in step 7. After that, SAEA starts an iterative procedure (steps [8][9][10][11][12][13][14][15][16][17][18], where the generation count is updated in step 9. A copy of the fittest individual is stored in step 10 before applying any algorithmic operators (i.e., the "elitism" strategy).…”

Section: Complexity Analysismentioning

confidence: 99%

“…A significant amount of previously conducted studies primarily focused on the berth scheduling problem (BSP), aiming to improve the seaside operations at MCTs [6]. In BSP, the MCT operator aims to assign arriving vessels for service at available MCT berthing positions and determine the service order of vessels at each berthing position.The BSP is a challenging decision problem, which can be reduced to the unrelated machine scheduling problem [6][7][8][9][10][11][12]. As underlined by Pinedo [13], the unrelated machine scheduling problem (therefore, the BSP as well) has NP-hard complexity.…”

mentioning

confidence: 99%

“…For a detailed review of the BSP mathematical models and solution algorithms this study refers to the literature survey, conducted by Bierwirth and Meisel [6]. Many BSP studies applied Evolutionary Algorithms (EAs) and demonstrated their efficiency based on the numerical experiments [6][7][8][9][10][11][12][14][15][16][17][18][19]. EAs are biologically inspired metaheuristics, where the candidate solutions to the problem of interest are encoded in the chromosomes [20,21].…”

mentioning

confidence: 99%

“…The BSP is a challenging decision problem, which can be reduced to the unrelated machine scheduling problem [6][7][8][9][10][11][12]. As underlined by Pinedo [13], the unrelated machine scheduling problem (therefore, the BSP as well) has NP-hard complexity.…”

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confidence: 99%

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A Self-Adaptive Evolutionary Algorithm for the Berth Scheduling Problem: Towards Efficient Parameter Control

et al. 2018

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Since ancient times, maritime transportation has played a very important role for the global trade and economy of many countries. The volumes of all major types of cargo, which are transported by vessels, has substantially increased in recent years. Considering a rapid growth of waterborne trade, marine container terminal operators should focus on upgrading the existing terminal infrastructure and improving operations planning. This study aims to assist marine container terminal operators with improving the seaside operations and primarily focuses on the berth scheduling problem. The problem is formulated as a mixed integer linear programming model, minimizing the total weighted vessel turnaround time and the total weighted vessel late departures. A self-adaptive Evolutionary Algorithm is proposed to solve the problem, where the crossover and mutation probabilities are encoded in the chromosomes. Numerical experiments are conducted to evaluate performance of the developed solution algorithm against the alternative Evolutionary Algorithms, which rely on the deterministic parameter control, adaptive parameter control, and parameter tuning strategies, respectively. Results indicate that all the considered solution algorithms demonstrate a relatively low variability in terms of the objective function values at termination from one replication to another and can maintain the adequate population diversity. However, application of the self-adaptive parameter control strategy substantially improves the objective function values at termination without a significant impact on the computational time.Algorithms 2018, 11, 100 2 of 35 of waterborne trade increased by 33.6% from 7.7 billion tons in 2006 to 10.3 billion tons in 2016 [5]. Moreover, the volumes of all major types of cargo, transported by vessels, have significantly increased over the last ten years. Specifically, containerized trade increased by 59.9%, while major bulk cargo, dry bulk cargo, and oil/gas increased by 74.9%, 10.8%, and 13.2%, respectively, from 2006 to 2016. The general consumption goods and high-value cargo are typically transported in containerized form. Containers are transferred among different continents by vessels, which are served at marine container terminals (MCTs). Rapid growth of waterborne trade requires MCT operators upgrading the existing terminal infrastructure and improving operations planning.MCT operations can be categorized in the following three types [1,6]: (1) seaside operations, which focus on service of arriving vessels (i.e., loading containers on vessels and unloading containers from vessels); (2) marshaling yard operations, which focus on temporary storage of containers in yard blocks of the MCT marshaling yard; and (3) landside operations, which focus on pick-up and delivery of containers by the inland transportation modes (generally, on-dock rail and/or drayage trucks). Efficient seaside operations are critical for MCT performance, as disruptions in the seaside operations may significantly delay service of the arriving v...

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Section: Complexity Analysismentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Self-Adaptive Evolutionary Algorithm for the Berth Scheduling Problem: Towards Efficient Parameter Control

et al. 2018

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“…Ting et al (2014) proposed a PSO algorithm for the BSP, minimizing the total turnaround time of vessels. Frojan et al (2015) formulated a BSP at the MCT with multiple quays, minimizing the total vessel service cost. An EA with a local search heuristic was developed to solve the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel memetic algorithm with a deterministic parameter control for efficient berth scheduling at marine container terminals

Dulebenets

2017

MABR

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Purpose The volumes of international containerized trade substantially increased over the past years. In the meantime, marine container terminal (MCT) operators are facing congestion issues at their terminals because of the increasing number of large-size vessels, the lack of innovative technologies and advanced handling equipment and the inability of proper scheduling of the available resources. This study aims to propose a novel memetic algorithm with a deterministic parameter control to facilitate the berth scheduling at MCTs and minimize the total vessel service cost. Design/methodology/approach A local search heuristic, which is based on the first-come-first-served policy, is applied at the chromosomes and population initialization stage within the developed memetic algorithm (MA). The deterministic parameter control strategy is implemented for a custom mutation operator, which alters the mutation rate values based on the piecewise function throughout the evolution of the algorithm. Performance of the proposed MA is compared with that of the alternative solution algorithms widely used in the berth scheduling literature, including a MA that does not apply the deterministic parameter control strategy, typical evolutionary algorithm, simulated annealing and variable neighborhood search. Findings Results demonstrate that the developed MA with a deterministic parameter control can obtain superior berth schedules in terms of the total vessel service cost within a reasonable computational time. Furthermore, greater cost savings are observed for the cases with high demand and low berthing capacity at the terminal. A comprehensive analysis of the convergence patterns indicates that introduction of the custom mutation operator with a deterministic control for the mutation rate value would provide more efficient exploration and exploitation of the search space. Research limitations/implications This study does not account for uncertainty in vessel arrivals. Furthermore, potential changes in the vessel handling times owing to terminal disruptions are not captured. Practical implications The developed solution algorithm can serve as an efficient planning tool for MCT operators and assist with efficient berth scheduling for both discrete and continuous berthing layout cases. Originality/value The majority of studies on berth scheduling rely on the stochastic search algorithms without considering the specific problem properties and applying the guided search heuristics. Unlike canonical evolutionary algorithms, the developed algorithm uses a local search heuristic for the chromosomes and population initialization and adjusts the mutation rate values based on a deterministic parameter control strategy for more efficient exploration and exploitation of the search space.

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Fully Fuzzy Linear Programming Model for the Berth Allocation Problem with Two Quays

Gutierrez

Lujan

Asmat

et al. 2019

Studies in Fuzziness and Soft Computing

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The continuous Berth Allocation Problem in a container terminal with multiple quays

Cited by 56 publications

References 23 publications

A Self-Adaptive Evolutionary Algorithm for the Berth Scheduling Problem: Towards Efficient Parameter Control

A Self-Adaptive Evolutionary Algorithm for the Berth Scheduling Problem: Towards Efficient Parameter Control

A novel memetic algorithm with a deterministic parameter control for efficient berth scheduling at marine container terminals

Fully Fuzzy Linear Programming Model for the Berth Allocation Problem with Two Quays

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