不确定环境下泊位与岸桥的协同调度研究
发布时间:2018-01-05 23:10
本文关键词:不确定环境下泊位与岸桥的协同调度研究 出处:《清华大学》2014年硕士论文 论文类型:学位论文
更多相关文章: 集装箱码头 遗传算法 泊位分配 岸桥调度 不确定性
【摘要】:作为国际物流的重要节点,集装箱码头是一个典型的离散、随机和动态的大规模系统,系统内部的复杂性和外部环境的不确定性总是给集装箱码头的规划与调度制造难题,面对这些难题,学术界已经做了大量的相关研究。对码头运营商而言,既要做好战略层面的码头规划,也要做好运作层面的码头调度,前者包括设备购买等长期决策,,后者包括泊位分配和岸桥调度等短期决策。由于涉及实时性等特点,调度问题一般而言要比规划问题复杂得多,成为学术研究突破的重点和难点。其中,集装箱码头的泊位分配与岸桥调度问题更是重中之重,国内外的不少学者都为之倾注了心血,也取得了可观的研究成果,有的研究在模型上做了创新,有的研究在算法上做了创新,有的研究用数学优化方法,有的研究用仿真优化方法,虽然已是硕果累累,但是在不断实践的过程中总会发现新的问题,成为我们进一步探索的动力。 泊位和岸桥作为集装箱码头至关重要的战略资源,其有效利用直接关系到码头的绩效和竞争力,甚至影响码头的生存问题。近年来,由于对问题认识的不断深入,以及相关理论和技术的不断成熟,越来越多的学者专注于泊位与岸桥的协同调度研究,该问题也是学术界公认的NP-hard问题。本文同样将泊位分配与岸桥调度作为一个整体进行研究,主要考虑如何在不确定环境中得到一个既稳健又低成本的调度方案。本文所关注的是连续泊位分配问题,同时还考虑了岸桥的泊位服务范围,使得模型更加贴近现实情况。为了解决该问题,本文提出了一个混合整数规划模型,并设计了基于遗传算法的启发式方法进行求解,数值实验证明了模型和算法的效率和有效性。 在已有研究的基础上,本文主要的创新点在于:针对现有研究的不足,在连续泊位分配策略下,考虑岸桥的泊位服务范围,对码头的泊位和岸桥进行协同调度,弥补了现有研究中要么忽视协同调度、要么考虑离散泊位的不足,使得调度系统更加符合现实;针对所提出的混合整数规划模型,本文还设计了综合基本遗传算法、基因调整算法和局部优化算法的启发式算法进行求解,大量数值实验证明了模型和算法的有效性,能够在成本和稳健性两个方面取得良好平衡。
[Abstract]:As an important node of international logistics, container terminal is a typical discrete, random and dynamic large-scale system. The complexity of the system and the uncertainty of the external environment always give the container terminal planning and scheduling problems. In the face of these problems, the academic community has done a lot of related research. It is necessary not only to do a good job of strategic level of terminal planning, but also to do a good job in the operational level of terminal scheduling, the former includes equipment purchase and other long-term decisions. The latter includes short-term decisions such as berth allocation and shore bridge scheduling. Due to the real-time characteristics, scheduling problems are generally more complex than planning problems, which has become the focus and difficulty of academic research breakthrough. The allocation of berths and the dispatch of quayside bridges are the most important problems in container terminals. Many scholars at home and abroad have devoted their efforts to it, and have also achieved considerable research results, some of which have made innovations in the model. Some of the research in the algorithm has been innovative, some research using mathematical optimization method, some research using simulation optimization method, although has been fruitful, but in the process of continuous practice will always find new problems. Become the motive force of our further exploration. Berth and quayside bridge are the most important strategic resources of container terminal. Their effective utilization is directly related to the performance and competitiveness of wharf, and even affects the survival of wharf in recent years. Due to the deepening of understanding of the problem and the maturation of related theories and technologies, more and more scholars focus on the cooperative scheduling of berth and shore bridge. This problem is also recognized as a NP-hard problem in academic circles. In this paper, berth allocation and shore bridge scheduling are also studied as a whole. This paper focuses on the allocation of continuous berths and the scope of berth service of the quayside. In order to solve this problem, a mixed integer programming model is proposed and a heuristic method based on genetic algorithm is designed to solve the problem. Numerical experiments show the efficiency and effectiveness of the model and algorithm. On the basis of the existing research, the main innovation of this paper is: in view of the shortcomings of the existing research, under the strategy of continuous berth allocation, the berth service scope of the quayside bridge is considered. The coordinated scheduling of berths and quayside bridges makes up for the lack of cooperative scheduling or discrete berths, which makes the scheduling system more realistic. For the proposed mixed integer programming model, this paper also designs a heuristic algorithm which integrates basic genetic algorithm, gene adjustment algorithm and local optimization algorithm to solve the problem. A large number of numerical experiments show that the model and the algorithm are effective and can achieve a good balance between cost and robustness.
【学位授予单位】:清华大学
【学位级别】:硕士
【学位授予年份】:2014
【分类号】:U691.3
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