The integrated berth allocation, quay crane assignment and scheduling problem: mathematical formulations and a case study
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This paper considers the integration of three essential seaport terminal operations: the berth allocation problem, the quay crane assignment problem (QCAP), and the quay crane scheduling problem (QCSP). The paper presents a new mathematical formulation that captures all associated operations and constraints. Different quay crane operational policies are considered, namely permitting versus not permitting bay task preemption in QCSP and static versus dynamic crane allocations in QCAP. Thus, variants of the mathematical formulation are introduced to capture the different combinations of these scenarios. Due to the preemption consideration, the models include disaggregated quay crane (QC) tasks. Specifically, QC tasks are identified by single container movements as opposed to bay or stack task allocations that are commonly used in the literature. A case study based on Abu Dhabi’s container terminal is presented where the use of the proposed mathematical models are compared against the current existing operational approach. Results show that the service times can be significantly decreased by the use of the proposed models. Moreover, the policy choice effect on the total schedule is compared through simulated examples and Abu Dhabi’s container terminal case study. The results show that the policy improvements can depend on the problem’s attributes and thus a better policy cannot be generalized.
KeywordsBerth allocation problem Quay crane scheduling Quay crane assignment Maritime logistics Seaport operations Optimization modeling
The first two authors were supported by Abu Dhabi Ports and Maqta Gateway in Abu Dhabi, United Arab Emirates. The authors would like to acknowledge their invaluable contributions and extend their warm appreciation to the CEO of Abu Dhabi Ports, Capt. Mohamed Al Shamisi, and the CEO of Maqta Gateway, Dr. Noura Al Dhaheri. Professor Cheng was supported in part by the NSF of China under Grant Number NSF#71390334.
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