集卡分批到达模式下的进口箱场桥作业调度优化

在进口箱疏港过程中, 服务于相同客户的若干集卡组成集卡组, 具有相同的抵港时间, 因此, 外部集卡抵港提箱呈现分批到达的特点. 集卡组内作业指派的优劣直接影响场桥的作业效率, 存在较大的优化空间. 对此, 基于翻箱作业不能跨贝进行的现实约束, 将场桥作业调度解构为场桥作业路径优化问题和贝内翻箱作业优化问题两部分并分别建立动态优化模型. 针对场桥作业路径优化问题, 提出一种多项式时间的精确算法并给以证明; 针对贝内翻箱作业优化问题, 设计一种基于MSA的双层启发式算法进行求解. 一系列数值实验的结果显示了所提出优化模型及算法的有效性和鲁棒性.

     

基于关键资源优先的三级装卸搬运分时协调策略

研究基于关键资源优先的单元化“装卸、搬运、装卸” 三级作业链的调度问题. 在已知关键资源调度方案的条件下, 将两个非关键各作业级的调度问题分别转化为最小单位流问题, 并建立相应的0/1 整数规划模型求解. 为求解大规模问题, 提出三级装卸搬运的分时协调策略. 参照宁波港码头数据, 设计面向集装箱码头的仿真算例, 实验结果验证了所提出模型和策略的有效性.

     

分布式车间调度优化算法研究综述

在分布式制造环境下, 分布式车间调度着重研究工件在工厂间的合理分配以及各工厂内的合理加工顺序, 以实现调度指标的最优化. 分布式车间调度的研究具有重要的学术意义和应用价值, 已成为生产调度领域的热点. 对 此, 围绕分布式并行机调度、分布式流水线调度、分布式作业车间调度、分布式装配调度和分布式柔性车间调度等问题, 重点综述分布式调度优化算法方面的代表性成果, 介绍分布式调度的若干应用, 最后指出有待于进一步研究的若干方向和内容.

     

炼钢连铸生产调度的优先级策略混合遗传算法

针对炼钢连铸生产调度计划的可执行性要求, 考虑到生产中的设备选择及作业时间的不确定性问题, 提出一种利用任务可执行设备的加工权重赋值方法来量化描述现实生产中加工设备间的匹配关系, 并以设备选择优先级策略的形式引入遗传算法的交叉、变异过程, 按照生成可行解、再进行种群优化的分步决策方式形成混合遗传算法. 以某炼钢厂的实际生产调度数据为例进行仿真实验, 其结果表明了所提出算法的有效性.

     

基于动态平衡策划下的集装箱堆场箱位分配方案研究

针对集装箱在码头堆存时存在的箱位分配问题,在研究原有多场桥同时作业方案的基础上,提出了多场桥分区域动态平衡策划方案,弥补了原方案中因保证场桥间的安全距离而带来的缺陷。以合理利用堆存空间,提高场桥作业效率为目标,建立数学模型,结合遗传算法对所提方案进行验证。结果表明,分区域动态平衡策划方案能有效减少场桥的无效作业时间,提高场桥的工作效率,并能充分地利用堆场贝位,更好地利用堆场空间。     

多阶段人机协同求解动态柔性作业车间调度问题

针对柔性作业车间生产环境中机器故障的动态调度问题, 以最小最大完工时间和最小偏差为目标, 结合车间调度人员的经验建立多阶段人机协同动态调度策略. 在不同阶段该策略的调度人员可参与优化过程, 提高方案的可行性和稳定性. 设计外部精英库中最优解的更新方法, 依据海明距离保留具有相同目标值的多种调度方案. 最后通过实例仿真验证了该模型和算法的有效性、可行性和稳定性, 更便于有效地指导生产实践.

     

基于GMOGSO的多目标流水车间调度问题

针对缓冲区有限的多目标流水车间调度问题, 提出一种基于Pareto 最优的广义多目标萤火虫算法. 通过引入交换子和交换序将基本萤火虫算法离散化, 并将算法拓展为全局搜索过程和局部搜索过程. 进化初期采用全局搜索将种群推向较优区域, 进化中后期采用捕食搜索策略使算法主体在全局搜索和局部搜索间智能切换, 从而保证全局与局部的平衡. 动态变步长策略进一步增强了算法搜索能力. 通过算例测试验证了所提出算法的有效性.

     

考虑时序约束的多智能体协同任务分配

研究多智能体系统的多目标多任务分配问题, 考虑任务之间的时序关系, 建立分布式任务分配模型. 扩展了一致性包算法(CBBA), 按优先级将目标任务归入不同层级, 各智能体在构建任务包和任务路径时, 只将分配过高阶段任务的目标添加至相应的任务包和任务路径中, 从而保证目标任务时序约束的同时, 保持了CBBA算法的特性. 与多任务分配问题经典算法的比对实验表明, 所提出的改进算法求解结果稳定可靠, 运行时间优于经典算法.

     

混合分布估计算法求解随机Job shop 提前/拖期调度问题

针对加工时间具有随机特性的Job shop 调度问题, 提出基于分布估计算法的混合算法. 为增强分布估计算法的种群多样性, 定义了父代工序继承率并设计一种可保留父代个体优良结构特征的重组方法, 该方法在继承父代个体优良结构特征的同时避免了非法解的产生. 在个体选择评价阶段, 采用最优计算量分配策略为每个个体分配模拟量以提高个体评价的精确性. 仿真算例表明了所提出算法的有效性和鲁棒性.

     

多编组协同任务分配模型及DLS-QGA 算法求解

为解决多智能体编组协同任务分配问题, 定义任务、智能体编组和相关的分配过程变量, 建立以最高任务执行效率为目标的数学模型. 在问题模型中设计考虑资源损耗的编组资源能力更新机制, 提出用于求解该模型的动态列表规划和量子遗传算法的混合任务分配算法, 使用动态列表规划选择处理的任务, 利用量子遗传算法为选定任务分配最合适编组. 最后通过算例表明, 所提出的方法在解决时序逻辑任务分配时能够得到更优更稳定的方案.

     

堆场龙门吊调度问题研究

在集装箱码头作业中,龙门吊是非常重要的码头资源,如何更合理地调度龙门吊对减少船舶在港时间,提高码头效率有重要意义。在综合考虑龙门吊在时间和空间上的不可跨越性以及其他约束条件的基础上,建立了龙门吊调度问题的混合整数规划模型,目标是使得集卡的等待时间最短。由于问题计算的复杂性,引进遗传算法来求解模型;算例验证了算法的有效性,与已有的模型进行比较,证明了该模型的优越性。     

Quay crane and yard truck dual-cycle scheduling with mixed storage strategy

In order to enhance the efficiency of port operations, the scheduling problem of the quay cranes and yard trucks is crucial. Conventional port operation mode lacks optimization research on efficiency of port handling operation, yard truck scheduling, and container storage location. To make quay crane operations and horizontal transportation more efficient, this study uses a dual-cycle strategy to focus on a quay crane and yard truck scheduling problem in conjunction with a mixed storage strategy. A dispatching plan for yard trucks is considered, as well as the storage location of inbound containers. Based on the above factors, a mixed-integer programming model is formulated to minimize vessels’ berth time for completing all tasks. The proposed model is solved using a particle swarm optimization-based algorithm. Validation of the proposed model and algorithm is conducted through numerical experiments. Additionally, some managerial implications which may be potentially useful for port operators are obtained.     

Storage yard management based on flexible yard template in container terminal

With the bottleneck of port operation moving from the quay side to the yard area, storage yard management is becoming increasingly important in the container terminal. This paper studies on storage yard management in container terminal, a flexible yard template strategy is proposed instead of the fixed yard template strategy. Based on the strategy, an integrated optimization model simultaneously considering space allocation and yard crane deployment for the tactical storage yard management is formulated. Besides, Numerical experiments are conduced to verify the effectiveness of the proposed strategy and mathematical model.     

An optimization methodology for intermodal terminal management

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.     

Design of digital twin applications in automated storage yard scheduling

A digital twin-enabled automated storage yard scheduling framework for uncertain port dispatching is proposed in this paper. Digital twin technology is employed to establish the virtual yet realistic storage yard and the connection between them. In the proposed framework, disturbed scenarios during practical operation are monitored, and real-time data is visualized in the virtual space to adapt to the time-varying environment. The proposed framework focuses on the optimization of three main resources, viz. storage area, automated stacking cranes (ASCs), and automated guided vehicles (AGVs). In addition, three key technologies, the Internet of Things (IoT), virtual reality, and digital thread, are adopted to develop the proposed scheduling system. A case study of ASC rescheduling due to dynamic arrival is used to demonstrate the effectiveness of the proposed framework and the significance of obtaining uncertainties in port optimization. Sensitivity analysis is conducted to define the appropriate configuration required to handle all tasks. The results show that digital twin applications in automated storage yard scheduling help operators make optimization decisions.     

铁路集装箱中心站资源分配与作业调度联合优化

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.     

A quay crane scheduling algorithm considering the workload of yard cranes in a container yard

Quay cranes (QCs) are the most important equipment for vessel operation in container terminals. It is important to efficiently schedule the operations of QCs so that a high productivity may be achieved in terminals. Because the storage yard is shared by multiple vessels, where the congestion of yard cranes and yard trucks must be considered, the operations of QCs for multiple vessels must be simultaneously considered for QC scheduling. The objective terms include the QC make-span, the total traveling distance of QCs, the expected delay time from interference, and the uniformity of workload among blocks. This study suggests a heuristic algorithm based on a meta-heuristic search algorithm. Finally, a numerical experiment is carried out to test the performance of the algorithm.     

考虑时间窗的集装箱港口场桥全局调度优化

合理高效的场桥调度计划有助于减少场桥与集卡相互等待的时间从而提高港口的运营效率,考虑到在实际操作中会出现多箱区多场桥同时工作、互相冲突等情况,建立了以场桥移动成本和延误成本最小化为目标的数学模型,利用计划时间段和时间窗的概念对场桥作业进行约束,通过遗传算法编码进行求解,并将计算结果与实际操作及其他算法的优化结果相对比,进而验证该模型和算法的有效性和稳定性。