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Integrated continuous berth allocation and quay crane assignment and scheduling problem with time-dependent physical constraints in container terminals

机译:集成的连续泊位分配和Quay起重机分配和调度问题在容器终端中的时间依赖性物理约束

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摘要

This paper presents an integer programming model for integrated continuous berth allocation, quay crane assignment and quay crane scheduling problem (BACASP) in container terminals where berthing possibility depends on water depth and tide conditions. The proposed model also considers the safe distance between quay cranes and the fact that they cannot cross each other on the rails. Given the NP-Hard complexity of the proposed model, a particle swarm optimization (PSO) based meta-heuristic called the random topology particle swarm optimization algorithm (RTPSO) is developed for solving its large-size instances. To evaluate the performance of the developed RTPSO, its results are compared with the results of the exact solution and the basic PSO. The results illustrate the better performance of the proposed random topology particle swarm optimization algorithm in terms of accuracy and computational time.
机译:本文介绍了集装箱终端中集成连续泊位分配,码头起重机分配,Quay起重机分配和码头起重机调度问题(BACASP)的整数编程模型,其中停泊可能取决于水深和潮汐条件。该拟议的模型还考虑码头起重机之间的安全距离以及它们在轨道上不能互相交叉的事实。鉴于所提出的模型的NP - 硬复杂性,开发了一种称为随机拓扑粒子群优化优化算法(RTPSO)的基于粒子群优化(PSO)的元启发式,用于解决其大尺寸实例。为了评估所发育的RTPSO的性能,将其结果与精确溶液和基本PSO的结果进行比较。结果说明了在准确性和计算时间方面提出了所提出的随机拓扑粒子群优化算法的性能。

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