摘要
在集装箱码头岸桥调度计划中,船舶容量、到达时间、设备功能和天气条件的变化均会造成很大的不确定性。为在不同条件下保持港口的服务水平,港口运营者需要制定稳健的调度表。针对操作时间不确定性条件下的岸桥调度优化问题,提出以最小化完工时间为目标函数,同时决策岸桥调度方案的方法,建立混合整数随机规划模型。为提高计算效率并评价算法的适用性,采用粒子群算法和禁忌搜索算法对模型进行求解,并利用算例对模型与算法的有效性进行验证。算法对比数值实验表明:两种算法均适用于中小规模下的随机优化问题,而粒子群算法更适用于大规模随机优化问题。
For container terminals,the quay crane scheduling usually faces many uncertain factors such as variations in container volume,arrival time,equipment functionality and weather conditions. In order to maintain the service level of the port under different conditions,developing a robust schedule is important for port operators.To solve the quay crane scheduling problem(QCSP)under uncertain operating time,a mixed integer stochastic programming model was formulated to minimize the makespan of quay crane and to decide the quay crane scheduling scheme. The particle swarm optimization and tabu search algorithm were used to solve the model,which aimed to improve the efficiency of computation and evaluated the applicability of algorithms. Numerical experiments were provided to show the validity of the proposed model and algorithms. The result indicates that both algorithms are suitable for small and medium-sized stochastic optimization problems,and particle swarm optimization performs better than tabu search algorithm for large-scale stochastic optimization problems in the tested cases.
作者
张思
吕梦晴
代剑环
李亚萍
ZHANG Si;LV Mengqing;DAI Jianhuan;LI Yaping(School of Management,Shanghai University,Shanghai 200444,China)
出处
《工业工程与管理》
CSSCI
北大核心
2020年第5期50-58,共9页
Industrial Engineering and Management
基金
国家自然科学基金项目(71701123)
国家自然科学基金项目(71701123)。
关键词
岸桥调度
不确定性
粒子群算法
禁忌搜索算法
quay crane scheduling
uncertainty
particle swarm optimization
tabu search algorithm
