基于多目标混沌云粒子群算法的泊位-岸桥分配研究  

Berth and quay-crane allocation based on multi-objective chaos cloud particle swarm optimization algorithm

作　　者：李明伟[1,2] 康海贵[2] 耿静[1,3] 周鹏飞[2] 

机构地区：[1]哈尔滨工程大学船舶工程学院,黑龙江哈尔滨150001 [2]大连理工大学海岸和近海工程国家重点实验室,辽宁大连116023 [3]大连中交理工交通技术研究院有限公司,辽宁大连116023

出　　处：《水运工程》2014年第1期90-96,共7页Port & Waterway Engineering

基　　金：国家自然科学基金(50679008);教育部博士点专项基金(200901411105);河南省交通厅科技计划项目(2010D107-4)

摘　　要：为了提高集装箱港口泊位-岸桥分配效果和优化效率,以集卡运距和船舶在港时间最小为优化目标,建立了多目标离散泊位-岸桥分配模型,利用混沌云粒子群算法对泊位-岸桥分配模型进行求解,开发了粒子可行-整数化处理模块,内嵌于混沌云粒子群算法进化中,制定了粒子编码规则,设计了多目标函数的粒子历史极值和全局极值的计算方法,提出了基于混沌云粒子群优化算法求解多目标离散泊位-岸桥分配模型的新方法,数值算例结果证明了该模型和算法的可行性和实用性。To improve allocation effectiveness and optimize efficiency of the berth and quay-crane an container terminal, a multi-objective berth and quay-crane allocation mode is established, so as to minimize the transportation distance of container truck and stay time of ships in terminal, and the Chaos Cloud Particle Swarm Optimization （CCPSO） algorithm is used to solve the new model. The feasible-integer processing module for particles is designed, and embedded in the CCPSO algorithm. Devising the rules of particles encoding and calculation method of the historical extremum and the global extremum of particles, a new multi-objective berth and quay-crane allocation mode optimized by the CCPSO algorithm is proposed. Numerical example result shows that the proposed model and algorithm has certain the practicability and effectiveness.

关 键 词：泊位-岸桥分配 粒子群算法 多目标优化 混沌理论 云模型 

分 类 号：U691.6[交通运输工程—港口、海岸及近海工程]