Berth allocation with time-dependent physical limitations on vessels

Xu, Dan; Li, Chung‐Lun; Leung, Joseph Y.-T.

doi:10.1016/j.ejor.2011.07.012

Cited by 98 publications

(76 citation statements)

References 41 publications

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“…The principle showing how the model works can be seen in the graphical presentation (Fig.7). Let us look at the group 3 (vessels [11][12][13][14][15]). An optimal berthing schedule without draft limitation requires that vessel no.14 should wait 3 time windows to be berthed at 29 th immediately after vessel no.13 has left berth segments 1-3.…”

Section: Optimization Results and Analysismentioning

confidence: 99%

“…The relation between vessel's time of arrival, waiting, berthing and departure is set up by (11) and (12). Berthing time should fall after the time of arrival, as it is defined by (13). Finally, a lower bound of waiting time is defined by (14).…”

Section: Figure 5: the Relation Of Decision Variables Y Ij And Z Ij Dmentioning

confidence: 99%

“…In that case, such ships could be redirected to berths where depth is not a limiting factor, if available. A similar approach to this one can be found in [13], where water depth and tidal fluctuations are used as attributes. However, the authors have only considered BAP with fixed berths layout.…”

Section: Introductionmentioning

confidence: 99%

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A Contribution to Berth Allocation Problem Solution with Draft Restrictions

Grubišić

Vilke

Barić

2015

Journal of Maritime & Transportation Science

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Models for berth assignments help to solve logistic problems in container terminals and are important decision making tools. This is particularly so where, because of the system complexity and vessels arrival rate, problems cannot be solved empirically. Another issue concerns potential infrastructure and equipment shortcomings, where high utilization of the existing system components is required. In this paper, the authors focus on draft or depth restrictions that may occur alongside a quay. The existing researches in this field are surveyed and the model for berth allocation optimization is upgraded to fulfil draft restrictions. The model is tested in the environment both with and without sea-depth limitation alongside a quay. For validation purposes, random vessels arrivals are generated together with their technical particulars.

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Section: Optimization Results and Analysismentioning

confidence: 99%

Section: Figure 5: the Relation Of Decision Variables Y Ij And Z Ij Dmentioning

confidence: 99%

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A Contribution to Berth Allocation Problem Solution with Draft Restrictions

Grubišić

Vilke

Barić

2015

Journal of Maritime & Transportation Science

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“…Having investigated three relevant models for solving the BAP, they improved one of the three models. Regarding a parallel-machine scheduling problem, Xu et al [24] dealt with the BAP with constraints that included taking into account the water depth and tidal condition. That study considered both static and dynamic versions of BAP and a heuristic was proposed to deal with them.…”

Section: Studies Focusing On Bap or Qcapmentioning

confidence: 99%

“…Simulation has been used to solve BAP [14], QCSP [11,15], and as an evaluation tool for terminal operations [16]. Genetic algorithms (GAs) have been used to solve dynamic QCAP [17], QCSP [1,18], simultaneous BAP and QCAP [19][20][21], and simultaneous BAP and QCSP [7,[22][23][24]. Heuristic rules have been employed to solve QCSP [25].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Solving the Three Seaside Operational Problems Using an Object-Oriented and Timed Predicate/Transition Net

et al. 2017

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Container terminals (CTs) play an essential role in the global transportation system. To deal with growing container shipments, a CT needs to better solve the three essential seaside operational problems; berth allocation problem (BAP), quay crane assignment problem (QCAP), and quay crane scheduling problem (QCSP), which affect the performance of a CT considerably. In past studies, the three seaside operational problems have often been solved individually or partially, which is likely to result in poor overall system performance. However, solving the three seaside operational problems simultaneously is in fact a very complicated task. In this research, we dealt with the three seaside operational problems at the same time by using a novel high-level Petri net, termed an Object-Oriented and Timed Predicate/Transition Net (OOTPr/Tr net). After defining the three seaside operational problems formally, we integrated them as a three-level framework that was further transformed into an OOTPr/Tr net model. Then, using the Prolog programming language, we implemented this model as a simulation tool to find the best solution based on the various combinations of heuristic rules used.

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2018

Metaheuristics for Maritime Operations

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Berth allocation with time-dependent physical limitations on vessels

Cited by 98 publications

References 41 publications

A Contribution to Berth Allocation Problem Solution with Draft Restrictions

A Contribution to Berth Allocation Problem Solution with Draft Restrictions

Modeling and Solving the Three Seaside Operational Problems Using an Object-Oriented and Timed Predicate/Transition Net

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