基于混合人工蜂群算法的多目标柔性作业车间调度问题研究

传统的优化算法在求解面对多目标柔性作业车间调度时,往往求解效率低且难以获得最优解。为了求解多目标柔性作业车间调度问题,设计了混合人工蜂群算法。种群的初始化采用了多种方法相结合的策略。在人工蜂群算法的不同阶段采用不同的搜索机制,在雇佣蜂阶段采用开发搜索,针对跟随蜂阶段蜜蜂跟随的对象的优秀解进行小幅度的更新,从而提高了搜索的表现。禁忌搜索与改进的人工蜂群算法相结合,有效的提升了获得最优解的概率。通过相关文献中的标准实例对设计的混合人工蜂群算法进行一系列求解测试,实验的结果有效的说明了算法在求解柔性作业车间调度问题时效果显著。通过求解结果对比表明人工蜂群算法的高效性和优越性。     

基于船时效率的岸桥配置优化

在集装箱码头系统中,对船舶进行有效的岸桥配置有助于缓解岸边资源紧张的现状,提高码头的运营效率。针对连续泊位下动态到港船舶的泊位分配和岸桥配置的集成优化问题,对船舶的岸桥配置进行基于船时效率的动态调整,以最小化包括船舶延迟靠泊成本、偏离偏好泊位成本、延迟离港成本和岸桥重新配置成本在内的总成本为目标建立模型,并根据基于船时效率的岸桥配置的调整规则设计了启发式算法,结合遗传算法（GA）对问题进行求解。最终通过算例分析,验证了提出的模型和算法在解决实际港口中泊位分配和岸桥配置问题上的有效性,并通过与未考虑岸桥配置进一步调整的传统GA计算的结果进行比较,证实了提出算法的优化效果。     

A hybrid approach for multi-objective combinatorial optimisation problems in ship design and shipping

Numerous real-world problems relating to ship design and shipping are characterised by combinatorially explosive alternatives as well as multiple conflicting objectives and are denoted as multi-objective combinatorial optimisation (MOCO) problems. The main problem is that the solution space is very large and therefore the set of feasible solutions cannot be enumerated one by one. Current approaches to solve these problems are multi-objective metaheuristics techniques, which fall in two categories: population-based search and trajectory-based search. This paper gives an overall view for the MOCO problems in ship design and shipping where considerable emphasis is put on evolutionary computation and the evaluation of trade-off solutions. A two-stage hybrid approach is proposed for solving a particular MOCO problem in ship design, subdivision arrangement of a ROPAX vessel. In the first stage, a multi-objective genetic algorithm method is employed to approximate the set of pareto-optimal solutions through an evolutionary optimisation process. In the subsequent stage, a higher-level decision-making approach is adopted to rank these solutions from best to worst and to determine the best solution in a deterministic environment with a single decision maker.     

三峡工程两坝联合通航调度的混合模拟退火算法

对三峡大坝和葛洲坝的一共5座船闸进行统一的船舶通航调度管理,是提高长江三峡水域航运能力的关键,然而其优化调度算法还缺乏必要的研究.本文首先提出了该问题的混合整数非线性规划模型,在实际通航调度环境中,该模型属于强NP-hard复杂度的大规模组合优化问题,因此设计了一种混合模拟退火算法来搜索次优化调度方案,该算法将解分解为闸次时间表和船舶调度计划两部分,在搜索过程中用启发式规则对闸次时日表进行调整,然后用深度优先搜索(DFS)算法根据闸次时间表求解船舶调度计划,最后根据Metropolis规则对当前解进行更新.针对实际通航数据的测试结果表明其优化效果明显优于原有的启发式算法.目前该算法已经成功地应用于实际的两坝联合通航调度系统中.     

混合离散人工蜂群算法求解含不相关并行机的分布式柔性流水线调度

研究每阶段含不相关并行机的分布式柔性流水线调度问题.考虑顺序相关准备时间和工件动态到达时间,以最小化总加权提前/拖期惩罚为目标建立整数规划模型,提出一种融合离散差分进化算法、变邻域下降算法和局域搜索的混合离散人工蜂群算法以获取近优解.该算法采用基于工厂-工件号的编码以及基于机器最早空闲时间的动态解码机制,通过随机规则和均衡分派策略生成初始工厂-工件序列群,在引领蜂阶段引入离散差分进化算法产生优质工厂-工件序列,在跟随蜂阶段利用变邻域下降算法在被选择序列附近继续搜索以得到邻域序列,在侦察蜂阶段设计基于关键/非关键工厂间插入的局域搜索提高算法搜索能力.通过仿真实验测试不同规模的算例,实验结果表明,所提出的混合离散人工蜂群算法表现出较好的求解性能.     

一种混合人工蜂群算法求解顺序相依拆卸线平衡问题

拆卸线平衡问题直接影响回收再制造成本.为此,构建了最小工作站开启数量、最短总拆卸时间、均衡工作站空闲时间、尽早拆卸有危害和高需求零部件的多目标顺序相依拆卸线平衡问题优化模型,提出一种混合人工蜂群算法.所提出算法在观察蜂跟随阶段采用分阶段选择评价法,以便更好地区分蜜源;在侦查蜂开采阶段构建基于全局学习的搜索机制,以提高开采能力.蜜蜂寻优过程中设计了简化变邻域搜索策略,提高了寻优效率.对比实验结果验证了模型的有效性和算法的优越性.     

Fuzzy decision support system for ship lock control

This paper presents the development of a Decision Support System (DSS) for the management of ship locks that relies on fuzzy logic. It contains a brief overview of the history and the construction of locks and basic information related to fuzzy logic, fuzzy linguistic variables and methods used in approximate reasoning. In reality, ship lock control is mostly based on the subjective estimations and the experience of a lock master (ship lock operator). The fuzzy set theory is the most favourable mathematical approach for consideration of indefiniteness and subjective estimates. This paper analyses the control process of a ship lock on a two-way waterway, with one chamber designed for one vessel. A control algorithm is constructed according to a set of linguistic rules that describes the operator’s control strategy. The subjective estimations are therefore implemented in the algorithm as fuzzy sets. Fuzzy rules aggregate the final fuzzy set and defuzzification produces a decision. A set of ship traffic data is generated for analysis and simulation purposes based on the annual distribution of ship arrivals at the lock. Two criteria are presented and used in parallel with the Fuzzy DSS (FDSS). These two extreme criteria reflect the interests of shippers on one side and workers and owners of the lock on the other side. These interests occur in actual systems and are used here to evaluate the results obtained using the FDSS. This paper additionally describes the design of the SCADA (Supervisory Control And Data Acquisition) software. This software relies on a PLC (Programmable Logic Controller) and provides a platform on which to implement the desired fuzzy algorithm. The software was developed with the suggestions of operators who have extensive experience in ship lock control. The presented control system can be used for support in decision making in control processes and in the training of new operators of ship locks.     

An analysis on convergence of data-driven approach to ship lock scheduling

In this paper, a ship lock scheduling problem is investigated. Ships arrive randomly over time, and the instantaneous arrival rates are allowed to vary both temporally and stochastically in an arbitrary manner. A data-driven approach is applied to a single ship lock scheduling, which is a typical optimizing and decision-making problem. The objective is to minimize the operation costs and other costs(e.g. water cost, electricity cost, and staff welfare cost) by selecting an appropriate slot number during a planned period. The convergence of data-driven approach is discussed from three aspects: the convergence of ant colony optimization algorithm, the convergence of the proposed algorithm, and the error between the historical ship data and the current arrival ship data. The research findings are beneficial for the convergence analysis of data-driven theory and the management of waterway transportation.     

港航多视角下内河集装箱船舶配载决策

为满足内河集装箱运输中船舶航线配载实际决策需求,从港方和船方多视角出发,提出港航多视角下船舶航线配载决策方法。基于问题分析与特征提取构建考虑港方和船方双方利益的港航多视角下的船舶配载决策模型。考虑到问题的多目标优化特性,设计一种带模糊关联熵的启发式算法进行多目标并行寻优。通过算例实验验证了模型与算法的可行性与有效性。     

基于ABC-SA混合算法的群控电梯优化调度

为解决电梯群控系统（Elevator group control system,EGCS）时间和能耗性能不理想的问题,提出一种基于改进人工蜂群的电梯群控多目标优化调度算法。首先,针对EGCS控制目标复杂性,建立具有多评价指标的群控电梯调度模型,依据该模型的适应度值进行合理派梯选择;其次,引入模拟退火准则优化基本人工蜂群算法结构以解决算法易陷入局部最优解的问题,使用混合改进的人工蜂群算法进行多目标优化调度。仿真结果表明,所提算法在侯梯时间、乘梯时间和停靠次数三个性能指标上对比基本人工蜂群算法均有所提高,有效说明该方法在求解柔性多目标群控电梯优化调度时具有一定的优越性。     

单船岸桥分配与调度集成优化模型

针对集装箱码头泊位确定条件下的单船岸桥(QC)分配和调度问题,建立了线性规划模型.模型以船舶在泊作业时间最短为目标,考虑多岸桥作业过程中的干扰等待时间与岸桥间的作业量均衡,并设计了嵌入解空间切割策略的改进蚁群优化(IACO)算法进行模型求解.实验结果表明:与可用岸桥全部投放使用的方法相比,所提模型与算法求得结果平均能够节省31.86%的岸桥资源;IACO算法与Lingo求得的结果相比,船舶在泊作业时间的平均偏差仅为5.23%,但CPU处理时间平均降低了78.7%,表明了所提模型与算法的可行性和有效性.     

A new global optimization strategy for coordinated multi-robot exploration: Development and comparative evaluation

This paper proposes a new multi-robot coordinated exploration algorithm that applies a global optimization strategy based on K-Means clustering to guarantee a balanced and sustained exploration of big workspaces. The algorithm optimizes the on-line assignment of robots to targets, keeps the robots working in separate areas and efficiently reduces the variance of average waiting time on those areas. The latter ensures that the different areas of the workspace are explored at a similar speed, thus avoiding that some areas are explored much later than others, something desirable for many exploration applications, such as search & rescue. The algorithm leads to the lowest variance of regional waiting time (WTV) and the lowest variance of regional exploration percentages (EPV). Both features are presented through a comparative evaluation of the proposed algorithm with different state-of-the-art approaches.     

改进人工蜂群求解多目标柔性作业车间调度问题

针对多个目标约束的柔性作业车间问题,本文采用基于Pareto解集的改进离散人工蜂群算法来求解.由于经典人工蜂群算法的选择概率不适用于多目标问题,本文对选择概率进行了重定义,将排序引入选择概率中;同时采用基于变异操作的邻域搜索方法进行局部搜索,并使用混合列交叉算子提高种群的多样性;采用Harmonic平均距离对Pareto解集进行裁剪,完成对Pareto解集的更新.最后通过实例测试及仿真实验,验证了本文算法在求解多目标柔性作业车间调度时的有效性.     

Holistic ship design optimization

Ship design is a complex endeavor requiring the successful coordination of many disciplines, of both technical and non-technical nature, and of individual experts to arrive at valuable design solutions. Inherently coupled with the design process is design optimization, namely the selection of the best solution out of many feasible ones on the basis of a criterion, or rather a set of criteria. A systemic approach to ship design may consider the ship as a complex system integrating a variety of subsystems and their components, for example, subsystems for cargo storage and handling, energy/power generation and ship propulsion, accommodation of crew/passengers and ship navigation. Independently, considering that ship design should actually address the whole ship’s life-cycle, it may be split into various stages that are traditionally composed of the concept/preliminary design, the contractual and detailed design, the ship construction/fabrication process, ship operation for an economic life and scrapping/recycling. It is evident that an optimal ship is the outcome of a holistic optimization of the entire, above-defined ship system over her whole life-cycle. But even the simplest component of the above-defined optimization problem, namely the first phase (conceptual/preliminary design), is complex enough to require to be simplified (reduced) in practice. Inherent to ship design optimization are also the conflicting requirements resulting from the design constraints and optimization criteria (merit or objective functions), reflecting the interests of the various ship design stake holders.The present paper provides a brief introduction to the holistic approach to ship design optimization, defines the generic ship design optimization problem and demonstrates its solution by use of advanced optimization techniques for the computer-aided generation, exploration and selection of optimal designs. It discusses proposed methods on the basis of some typical ship design optimization problems with multiple objectives, leading to improved and partly innovative designs with increased cargo carrying capacity, increased safety and survivability, reduced required powering and improved environmental protection. The application of the proposed methods to the integrated ship system for life-cycle optimization problem remains a challenging but straightforward task for the years to come.     

船闸人字门背拉杆预应力优化设计

为有效提高船闸闸门的扭转刚度、减小闸门的扭转变形,对某大型船闸人字门背拉杆预应力进行优化.介绍船闸人字门线性规划函数形式的优化设计方法,计算在自重和单项载荷作用下背拉杆的应力和位移;综合考虑人字门门体的扭转和下垂变形的影响,建立改进线性规划的预应力优化设计模型,获得的若干预应力优化值可供现场调试参考.     

Disruption management-based coordinated scheduling for vessels and ship loaders in bulk ports

For ensuring the orderly operation of the port, it is vital to coordinately schedule available ship loaders and vessels that plan to enter and exit the port when ship loaders are unable to work due to faults. Therefore, this paper studies the coordination between vessels and ship loaders scheduling problem affected by failed ship loaders (VSLPB), and proposes a novel disruption management-based method to address this problem. An innovative optimization model is developed to reduce the generalized cost with the constraints of disruption management strategies (DMS), aiming to minimize the impact of failed ship loaders on the coordinated scheduling and the bulk cargo handling efficiency. For solving the VSLPB, an effective two-stage row generation (TSRG) algorithm is developed. In the first stage, the disruption conditions in the model are released to find the available ship loaders and berths for vessels affected by the failure factors. In the second stage, the optimal strategy is sought among multiple DMS to minimize the objective function value. Using the proposed method in Huanghua Coal Port as a case study, the results show that our method can effectively solve the impact of ship loader failure on the efficiency of bulk cargo handling and the efficiency of vessels entering and leaving the port. These further highlights the importance of implementing DMS, and show that the proposed method can provide an efficient and reliable solution for port production and operation to deal with disruption problems. Furthermore, the proposed method in this paper can help improve the ability of the port to resist uncertain factors, thus improving the ability of the entire supply chain to resist risks.     

改进近邻人工蜂群算法求解柔性作业车间调度问题

为了更好地解决以最小化最大完工时间为目标的柔性作业车间调度问题,提出了一种改进的人工蜂群算法。首先,采用随机选择和反向学习策略来提高初始蜜源的质量。同时,设计了一种新颖的特征表示方式,用于计算蜜源之间的距离。在引领蜂阶段,通过引入交叉和变异策略来优化种群中的近距离蜜源。在探索蜂阶段,引入了六种变邻域方法,以扩大解空间的搜索范围。而在侦查蜂阶段,则根据蜜源的潜力值剔除局部最优个体。在15个数据集上进行了广泛实验,实验结果表明,该改进算法性能明显优于其他四种著名的群智能优化算法。该研究为解决柔性作业车间调度问题提供了一种新的有效方法,对于实际生产调度具有重要的实用价值。     

多目标粒子群和人工蜂群混合优化的阈值图像分割算法

在图像分割中,为了准确地把目标和背景分离出来,提出了一种基于多目标粒子群和人工蜂群混合优化的阈值图像分割算法。在多目标优化的框架下,将改进的类间方差准则和最大熵准则作为适应度函数,通过粒子群和蜂群混合优化这2个适应度函数来获得1组非支配解。同时,为了提高全局和局部搜索能力,在蜂群进化时,将粒子群的全局最优解引入到人工蜂群算法的雇佣蜂阶段蜜源的更新中,并对搜索方程进行改进。最后通过类间差异和改进的类内差异的加权比值,从一组非支配解中选取最优阈值。实验结果表明,该算法能够取得理想的分割结果。     

基于离散人工蜂群算法的云任务调度优化

针对现今云计算任务调度只考虑单目标和云计算应用对虚拟资源的服务的质量要求高等问题,综合考虑了用户最短等待时间、资源负载均衡和经济原则,提出一种离散人工蜂群(ABC)算法的云任务调度优化策略。首先,从理论上建立了云任务调度的多目标数学模型;然后,结合偏好满意度策略并引入局部搜索算子和改变侦察蜂搜索方式,提出多目标离散型人工蜂群(MDABC)算法的优化策略。通过不同的云任务调度仿真实验,显示了改进离散人工蜂群算法相对于基础离散人工蜂群算法、遗传算法以及经典贪心算法,能够得到较高的综合满意度,表明了改进离散人工蜂群算法能够更好地改善虚拟资源中云任务调度系统的性能,具有一定的普适性。     

基于多目标模糊线性规划求解方法的飞机排班问题研究

提出了一种基于多目标模糊线性规划法解决飞机排班问题的新算法。该算法将模糊理论与最优化概念相结合,根据最大隶属度原则,将以飞机飞行时间均衡优先、飞机起降次数均衡优先、飞机等待时间最少优先为目标函数的多目标模糊线性规划数学模型转化为一般的线性规划问题进行求解。实验数据表明,该算法可行、有效,步骤简捷,计算量小,能得到理想的结果。