摘要
在对集装箱码头的核心作业过程进行分析的基础上,建立了基于船舶在港延迟时间最短和多种污染气体排放最少的泊位-岸桥-集卡的集成调度优化模型,并设计了一种基于量子局部搜索的非支配排序遗传算法(QNSGA-Ⅱ)。最后以某港口为例进行了实证分析,并将QNSGA-Ⅱ算法与非支配排序算法(NSGA-Ⅱ)和多目标粒子群优化算法(MOPSO)进行了比较。结果表明:QNSGA-Ⅱ算法在帕累托边界的完整性、分布均匀性和收敛性方面均得到了显著提高;港口区域产生的污染物排放量与延迟时间之间存在负相关关系,如果港口盲目追求高服务效率,将导致港口地区污染物排放量的大量增加。
Based on the analysis of the core operation process of the container terminal,the integrated scheduling optimization problem of berths,quay cranes and internal trucks in container terminals considering pollutant emissions was put forward,which minimized the cost of ship delay time in port and the cost of port emissions. A quantum non-dominated sorting genetic algorithm Ⅱ based on quantum behavior(QNSGA-Ⅱ) was designed to solve the problem. Finally,an empirical analysis of one port was carried out as an example,and QNSGA-Ⅱ was compared with the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) and the multi-objective particle swarm optimization algorithm(MOPSO) respectively. The results show that the QNSGA-Ⅱ has significantly improved the completeness of the Pareto boundary,the uniformity of the distribution,and the convergence of the algorithm. In addition,there is a negative correlation between the pollutant emissions and the delay time. If the port blindly pursues high service efficiency,it will lead to a great increase in pollutant emissions in the port area.
作者
王丹
李丹阳
赵利昕
徐卉
黄肖玲
范厚明
WANG Dan;LI Danyang;ZHAO Lixin;XU Hui;HUANG Xiaoling;FAN Houming(Transportation Engineering College,Dalian Maritime University,Dalian,Liaoning 116026,China)
出处
《工业工程与管理》
CSCD
北大核心
2023年第1期131-143,共13页
Industrial Engineering and Management
基金
国家社会科学基金资助项目(20VYJ024)
辽宁省社会科学基金资助项目(L18BGJ001)
大连市科技创新基金项目(2020JJ26GX033)。
关键词
集装箱港口
集成调度
污染物排放
量子局部搜索
非支配排序遗传算法
container terminal
integrated scheduling
pollutant emissions
quantum local search
non-dominated sorting genetic algorithm
