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1.
绿色港口日渐成为港口发展的必然趋势,为了提高集装箱码头的服务水平及降低其能耗,综合分析了集装箱码头的装卸作业流程,考虑岸桥、场桥、集卡在不同作业状态下的能耗,且以总完工时间和总作业能耗最小为目标建立了多目标混合整数规划模型。使用MATLAB编码改进自适应遗传算法求解所建模型,并分别与CPLEX和原始遗传算法的求解结果作对比,证明了该算法的优秀性。更改能耗目标和作业时间目标所占权重进行求解,发现考虑各设备在不同作业状态下的能耗会影响总完工时间,且能耗与作业时间是相互冲突的目标,追求低能耗会造成作业效率的牺牲。分析结果表明,所建模型和算法在岸桥、场桥和集卡的协调调度问题中可以帮助决策者更好地权衡作业时间和能耗目标。  相似文献   

2.
在混堆模式下的集装箱港口中,场桥(YC)调度是否合理直接影响着堆场的作业效率。考虑到混堆箱区内各任务对应的内集卡或外集卡到达时刻的不同,以及内外集卡优先级别的差异,构建了一个以所有集卡的等待成本和场桥的总移动成本最小为目标的场桥调度(YCS)模型,并设计了对应的遗传算法,给出了相应遗传算子的操作规则,通过算例的求解验证了模型与算法的有效性。  相似文献   

3.
李舒仪  韩晓龙 《计算机应用》2021,41(5):1506-1513
在集装箱海铁联运港口中,铁路作业区作为连接铁路运输和水路运输的重要节点,其装卸效率将影响集装箱海铁联运的整体效率。首先,对比分析了“船舶-列车”作业模式和“船舶-堆场-列车”作业模式的特点,并结合海铁联运港口实际作业情况提出了混合作业模式。然后,以轨道吊完工时间最短为目标构建混合整数规划模型,既考虑了班列和船舶的作业时间窗约束,又考虑了轨道吊间干扰和安全距离、轨道吊和集卡接续作业和等待时间等现实约束。针对遗传算法在局部搜索能力方面的不足,将启发式规则与遗传算法相结合设计了求解轨道吊与集卡协同调度问题的混合遗传算法(HGA),并进行了数值实验。实验结果验证了所提模型和混合算法的有效性。最后通过设计实验分析集装箱数量、岸边箱占比、轨道吊数量和集卡数量对轨道吊完工时间和集卡完工时间的影响,发现同等集装箱数量下岸边箱占比提高时,应通过增加轨道吊数量来有效缩短完工时间。  相似文献   

4.
为提高集装箱码头作业效率,降低不确定干扰因素对外集卡提箱作业的影响,提出以滚动窗口策略处理干扰因素的方法,并建立以作业延误惩罚成本与场桥移动成本最小化为目标的混合整数模型,采用遗传算法(GA)进行求解。首先,利用滚动窗口策略得到在无干扰因素情况下的外集卡提箱作业调度方案;其次,当出现干扰因素时触发滚动窗口再调度机制对外集卡提箱作业顺序重新安排;最后,计算出各滚动窗口内最优的调度方案,提出总计划时间内最优作业方案。通过对不同情景下的案例求解结果进行对比分析,实验结果表明在无干扰情况下,滚动窗口策略下的最小作业成本比传统作业方式下降低了9%,而在干扰情况下滚动窗口策略优于传统作业方式15%,进而验证了算法的有效性以及滚动窗口策略对外集卡提箱作业的优越性。  相似文献   

5.
为了解决港口海铁联运转运集装箱作业规模过大的问题,采用滚动窗策略方法研究港口船舶与列车之间转运进口集装箱作业问题,在每个窗口内建立以列车在港停留时间和集装箱在堆场的堆存时间总时间最小为目标的整数规划模型,设计双层遗传算法进行求解。在此基础上,分别讨论了以固定任务数量和固定时间长度为滚动窗口的情况,对比发现以固定时间长度为窗口的滚动窗策略更适用,并将其与已有的调度策略研究成果作比较分析。最后,设置实验比较双层遗传算法和单层遗传算法,并对设备的工作能力进行灵敏度分析。结果表明,滚动调度策略可以灵活解决大规模集装箱转运问题,双层遗传算法的解优于单层遗传算法的解,增加装卸线数和轨道起重机工作能力可以提高集装箱转运效率。  相似文献   

6.
高银萍  苌道方  陈俊贤 《计算机应用》2022,42(10):3259-3267
针对外集卡到达时间的不确定性,提出自动堆垛起重机(ASC)作业序列的动态优化,从而以减少ASC作业完成时间以及ASC和外集卡等待时间为目的,提高自动化集装箱码头堆场的作业效率。首先,结合混堆模式下集装箱作业类型与外集卡动态到达的特点,提出ASC动态匹配外集卡作业任务的策略;其次,构建ASC作业时间最短与ASC和外集卡等待时间最短的多目标模型;最后,设计基于动态规则的非支配排序遗传算法Ⅱ (DRNSGA Ⅱ)作为求解算法。在小规模算例实验中,分别运用DRNSGA Ⅱ与遗传算法(GA)求解动态策略和随机策略下的ASC作业问题。实验结果表明,DRNSGA Ⅱ求解的动态策略下目标函数值优于随机策略28.2%,并且动态策略下DRNSGA Ⅱ的求解结果优于遗传算法23.3%。在大规模算例实验中,比较了DRNSGA Ⅱ与多目标粒子群优化(MOPSO)两种算法的性能。实验结果表明DRNSGA Ⅱ的求解结果优于MOPSO算法6.7%。可见DRNSGA Ⅱ能够快速生成多样化的非支配解,为混堆模式下的ASC动态作业提供决策支持。  相似文献   

7.
为解决集装箱港口岸桥和集卡资源紧张的现状,减少集装箱处理时间,针对岸桥和集卡协调调度问题,在只有进口箱的条件下,综合考虑岸桥干涉和集装箱优先级等约束,建立一个以最小化最大完工时间为目标的混合整数线性规划模型,并使用遗传算法(GA)求解该模型。其次对不同规模的问题分别使用遗传算法(GA)和粒子群算法(PSO)求解并比较。实验结果表明,对于该问题模型遗传算法(GA)算法优于粒子群算法(PSO)算法,遗传算法是有效的。  相似文献   

8.
铁路集装箱中心站主作业区资源分配与作业调度联合优化对其经营效益和运作效率有重要影响.基于“轨道吊-集卡”协同装卸方案,引入轨道吊动态配置原则,以最大化作业均衡率和最小化作业成本为目标,构建多目标非线性混合整数规划模型,综合研究作业区域动态划分、贝位分配以及多轨道吊调度多层次联合优化问题. 根据问题特点,融合启发式规则、遗传算法和模拟退火算法,设计3层混合启发式算法求解模型.通过不同规模算例,对比Cplex与所设计算法的实验结果,验证模型的正确性和算法的有效性,并借助于不同划分原则、不同优化策略与不同间隔约束下的对比实验,验证优化模型与算法普适性.结果表明,新型装卸方案、动态作业区域以及联合优化策略可大幅度均衡轨道吊作业量,避免资源过度负载、降低中心站作业成本,为中心站的实际运营管理提供决策支持.  相似文献   

9.
在集装箱码头装卸作业中,提高集卡调度对作业效率有非常大的影响。而集装箱卡车重车和空车行驶速度的不确定性,增加了集卡调度的难度。为此提出建立不确定环境下作业时间最短的集卡调度优化模型。在完成固定装卸任务的前提下,通过对集卡的合理调度,达到不确定情况下集卡总作业时间最短。针对不确定模型的特点,采用粒子群算法快速求解,同时保证了解的有效性。算例表明上述模型和算法合理解决了不确定环境下集卡调度优化问题,得到较好的计算结果,并且降低了运算复杂度,提高了调度效率。  相似文献   

10.
在港口码头作业中,龙门吊的作业与集卡的作业之间存在着非常紧密的联系,如何更好地同步龙门吊与集卡之间的作业,对减少集装箱船的在港时间非常有效。同时考虑了龙门吊与集卡的协同调度,很好地考虑了龙门吊与集卡之间的同步。在此基础之上,首次考虑了实际作业中存在的一些约束,如多台龙门吊在同一个箱区作业时由于共享一个双向轨道而存在的龙门吊间不能相互跨越的约束。另外还有龙门吊间的安全距离约束,工作优先约束等。构建了一个数学模型来描述问题,目标是使得所有作业总完工时间最短。由于问题计算的复杂性,引进了一种多层遗传算法来求解模型,最后算例验证了算法的有效性。  相似文献   

11.
黎明  翟金刚 《计算机应用研究》2012,29(10):3636-3639
为了解决集装箱堆场装船顺序问题,根据集装箱船舶和配载的实际情况,考虑集装箱的航程,结合集装箱的装载位置,以集装箱堆场的翻箱率、船舶卸载时的翻箱率以及装船后的稳性为目标,建立了装船顺序的多目标规划模型,并基于粒子群算法构造了求解算法,通过MATLAB进行仿真实验,数据结果表明,该模型具有一定的的合理性与可行性。  相似文献   

12.
This paper presents a novel approach for allocating containers to storage blocks in a marine container terminal. We model the container terminal as a network of gates, yard blocks and berths on which export and import containers are considered as bi-directional traffic. For both export and import containers, the yard blocks are the intermediate storage points between gates (landside) and berths (waterside). Our model determines the route for each individual container (i.e. assign the container to a block to be stored) based on two competing objectives: (1) balance the workload among yard blocks, and (2) minimize the distance traveled by internal trucks between yard blocks and berths. The model utilizes an ant-based control method. It exploits the trail laying behavior of ant colonies where ants deposit pheromones as a function of traveled distance and congestion at the blocks. The route of a container (i.e. selection of a yard block) is based on the pheromone distribution on the network. The results from experiments show that the proposed approach is effective in balancing the workload among yard blocks and reducing the distance traveled by internal transport vehicles during vessel loading and unloading operations.  相似文献   

13.
An optimization methodology for intermodal terminal management   总被引:13,自引:0,他引:13  
A solution to the problems of resource allocation and scheduling of loading and unloading operations in a container terminal is presented. The two problems are formulated and solved hierarchically. First, the solution of the resource allocation problem returns, over a number of work shifts, a set of quay cranes used to load and unload containers from the moored ships and the set of yard cranes to store those containers on the yard. Then, a scheduling problem is formulated to compute the loading and unloading lists of containers for each allocated crane. The feasibility of the solution is verified against a detailed, discrete-event based, simulation model of the terminal. The simulation results show that the optimized resource allocation, which reduces the costs by [frac13], can be effectively adopted in combination with the optimized loading and unloading list. Moreover, the simulation shows that the optimized lists reduce the number of crane conflicts on the yard and the average length of the truck queues in the terminal.  相似文献   

14.
基于遗传算法的集装箱后方堆场箱位分配策略*   总被引:3,自引:2,他引:1  
根据重庆港实际情况建立了以提箱时间为制约因素,以最小化翻箱率为目标的集装箱后方堆场箱位分配模型,并针对模型提出了基于遗传算法的解决方案。方案对一次卸船或者进港的一批箱进行全局优化,并考虑其分配对后续集装箱的影响。遗传算法迭代过程中采用适应度函数指数变换防早熟,采用可行解替换法处理约束,并设计最优解保存策略保证最终的优化效果。最后针对实际堆场的不同规模,对方案的优化结果同文献中的其他遗传算法方案进行比较,证明了本文优化策略的优越性和实用性。  相似文献   

15.
Optimizing collaborative operations for yard cranes (YCs) and yard trucks (YTs) is vital to the overall performance of a container terminal. This research investigates four different hybrid approaches developed for dealing with yard crane scheduling problem (YCSP) and yard truck scheduling problem (YTSP) simultaneously for export containers in the yard side area of a container terminal. First, these approaches use a load-balancing heuristic to assign containers to YCs evenly. Following this, each of them employs a specific heuristic/metaheuristic, such as genetic algorithm (GA), particle swarm optimization (PSO) or subgroups PSO (SGPSO), to generate alternative container loading sequences for each YC. Finally, a simulation model is used to simulate loading and transporting of these export containers, evaluate alternative planning results, and finally output the best planning result. Experiments have been conducted to compare these hybrid approaches. The results show Hybrid4 (SGPSO) outperforms Hybrid1 (Sort-by-bay), Hybrid2 (GA), and Hybrid3 (PSO) in terms of makespan.  相似文献   

16.
Due to increasing ships and quay cranes, container terminals operations become more and more busy. The traditional handling based on work line is converted into pool strategy, namely loading and unloading containers with multiple work lines are operating simultaneously. In the paper we discuss the yard crane scheduling problem with multiple work lines in container terminals. We develop a multi-objective 0-1 integer programming model considering the minimum total completion time of all yard cranes and the maximization balanced distribution of the completion time at the same time. With the application of adaptive weight GA approach, the problem can be solved by a multi-objective hybrid genetic algorithm and the Pareto solutions can be finally got. Using the compromised approach, the nearest feasible solution to ideal solution is chosen to be the best compromised Pareto optimal solution of the multi-objective model. The numerical example proves the applicability and effectiveness of the proposed method to the multi-objective yard crane scheduling problem.  相似文献   

17.
Facing the shortage of storage space of container terminal yard, a yard sharing strategy that uses dry port's surplus storage space to ease container congestion is proposed. This novel strategy can address the container storage space assignment problem for inbound containers. The problem is studied based on the storage yard of the combined container terminal and dry port. First, a multiple-objective mixed integer programming model that considers yard sharing strategy with the objectives of minimizing total travel distance, minimizing imbalance in number of containers, maximizing shared storage space of the dry port is formulated to obtain optimal solutions. Second, a non-dominated sorting genetic algorithm II (NSGA-II) is proposed. Next, the performance of the algorithm is verified by a set of instances. Numerical experiments are conducted to elucidate the problem with yard sharing strategy intuitively. Furthermore, the performance of the model in four aspects proclaims the advantages of yard sharing strategy and certifies the comprehensiveness. Finally, sensitivity analysis is conducted by two aspects which are weight coefficient and feasible distance to verify the efficiency of the proposed method.  相似文献   

18.
朱杰  张文怡  薛菲 《计算机应用》2020,40(1):284-291
针对自动化立体仓库储位分配问题,结合仓库运作特点和安全性要求,构建了自动化立体仓库储位优化问题的多目标模型,并提出了求解模型的基于Sigmoid曲线的改进自适应遗传模拟退火算法(SAGA)。首先,以降低货品出入库时间、同组货品距离和货架重心为目标建立储位优化模型;然后,为了克服遗传算法(GA)局部搜索能力差和易陷入局部最优的缺点,引入基于Sigmoid曲线的自适应交叉变异操作和逆转操作,同时完成与SAGA的融合;最后,对改进遗传SAGA进行算法优化性、稳定性和收敛性测试。仿真实验表明,相比模拟退火(SA)算法的求解结果,该算法对货品出入库时间的优化度提高了37.7949个百分点、对同组货品距离提高了58.4630个百分点、对货架重心优化度提高了25.9275个百分点,并且该算法具有更好的稳定性和收敛性。由此验证了改进遗传SAGA求解问题的有效性,该算法可为自动化立体仓库储位优化提供决策方法。  相似文献   

19.
集装箱翻箱问题涉及集装箱装卸工作的顺畅与否。以此为研究对象,首先建立了基于网络模型的集装箱翻箱模型,并用算例进行了计算试验。在此基础上,对约束进行了改正,建立了改正的集装箱预翻箱模型,并进行了计算验证,证明了其在计算时间上的压缩。根据实际的单贝集装箱堆存情况,进行了实际堆存状态翻箱的计算试验,取得了阶段性成果。对单贝集装箱的堆存数与翻箱次数的关系进行了研究,发现在堆存数达到17时有较好的堆场利用率和较少的翻箱次数。  相似文献   

20.
An important activity in container transshipment terminals consists in transferring containers in the yard from their current temporary positions to different positions closer to the point along the berth from which the containers will be boarded on departing vessels. This housekeeping process aims at speeding-up discharge and loading operations and, thus, relieving congestion. This paper introduces a heuristic procedure to manage the routing of multi-trailer systems and straddle carriers in a maritime terminal. A simulation model embedded in a local search heuristic allows a proper evaluation of the impact of different vehicle schedules on congestion and throughput. Computational experiments performed on test instances derived from real-life data show that important improvements in routing distance and vehicle waiting time can be obtained compared to the use of standard scheduling policies.  相似文献   

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