考虑潮汐影响的班轮多船型船舶调度

针对班轮企业由于提前公布船期表，但受货运需求的波动和潮汐的影响引起的多船型船舶调度问题进行研究。首先系统分析了一家班轮企业近洋运输航线结构；然后考虑大型船舶需乘潮进出港口，以及适当条件下允许租船的实际情况，兼顾班轮船期表的限制，构建了以运输总成本最小为目标的班轮多船型船舶调度非线性规划模型；最后考虑模型的特点，设计了嵌入基因修复的改进遗传算法（IGA）用于模型求解。实验结果表明，与传统的经验调度方案相比，得到的船舶调度方案在船舶利用率上能提高25%~35%；中规模算例下与CPLEX相比，IGA的CPU处理时间平均降低77%；中、大规模算例下与蚁群算法相比，IGA计算的运输费用平均降低15%。实验结果验证了所提模型和算法的有效性，可为班轮企业船舶调度提供参考。     

基于量子遗传混合算法的泊位联合调度

为了提高集装箱港口服务效率,减少船舶服务的拖期费用,针对港口硬件（泊位、拖轮、岸桥）既定条件下的拖轮-泊位联合调度问题,新建了以最小化总体船舶在港时间和总拖期时间为目标的数学模型,设计了一种混合算法进行求解。首先,分析确定了将量子遗传算法（QGA）和禁忌搜索（TS）算法进行串行混合的策略;然后,依据该联合调度问题特点,在解决算法实施中的关键技术问题（染色体结构设计和测量、遗传操作、种群更新等）的同时,采用了动态量子旋转门更新机制;最后,用生产实例验证了算法的可行性及有效性。算法实验结果表明,与人工调度结果相比,混合算法的总体船舶在港时间和总拖期时间分别减少了24%和42.7%;与遗传算法结果相比,分别减少了10.9%和22.5%。所提模型及算法不仅能为港口船舶的入泊、离泊和装卸作业环节提供优化作业方案,而且能增强港口竞争力。     

基于遗传算法的不定期船舶调度优化模型研究

针对不定期船舶调度中客户需求信息的动态变化、船舶类型的多样化以及船舶航线的不定性,以最小化航运成本为目标函数,建立了带滚动窗口的不定期多目标船舶调度优化模型（SRPRW）,并制定了基于SRPRW模型的实时优化策略以实现需求信息变化时能及时快速地调整船舶调度航线.SRPRW模型求解时,在遗传算法（GA）中引入模拟退火机制以防止SRPRW模型的调度结果陷入局部最优解,同时设计了一种自适应交叉算子和自适应变异算子,以提高模型求解的收敛速度.实验结果表明该模型能快速地制定出船舶调度路线,及时响应客户的动态需求.     

结合遗传算法和滚动调度的多机器人任务分配算法

研究多机器人任务分配（MRTA）的目的是提高智能工厂中机器人完成任务的效率。针对现有算法在处理大规模、多约束的MRTA时存在不足的问题，提出一种结合遗传算法和滚动调度的MRTA算法（ACGARS）。首先，在遗传算法中采用基于有向无环图（DAG）的编码方式高效地处理任务之间的优先级约束；其次，在遗传算法的初始种群中加入先验知识以提高算法的搜索效率；最后，设计基于任务组的滚动调度策略用于减小求解问题的规模，从而实现对大规模问题的高效求解。在大规模问题实例上的实验结果表明，相较于构造性启发式算法（CHA）、最小化干扰算法（MIA）和基于惩罚策略的遗传算法（GAPS）生成的方案，当任务组数为20时，所提算法生成的方案的平均订单完成时间分别缩短了30.02%、16.86%和75.65%，验证了所提算法能有效地缩短订单的平均等待时间，提升多机器人任务分配效率。     

基于改进遗传算法的标签印刷生产调度技术

针对标签印刷生产过程中存在的多品种、小批量、客户定制化程度高、部分生产工序存在不确定性等问题建立了以最小化最大完工时间为目标的柔性作业车间调度模型,提出了一种改进遗传算法（GA）。首先,在标准遗传算法的基础上采用整数编码;然后,在选择操作阶段采用轮盘赌法,并通过引入精英解保留策略以确保算法收敛性;最后,提出动态自适应交叉和变异概率,从而保证算法在前期进行较大范围寻优,以避免早熟,而后期尽快收敛,以保证前期获得的优良个体不被破坏。为了验证所提改进遗传算法的可行性,首先采用Ft06基准算例把所提算法与标准遗传算法（GA）进行比较,结果显示改进遗传算法的最优解（55 s）优于标准遗传算法的最优解（56 s）,且改进遗传算法的迭代次数明显优于标准遗传算法;然后通过柔性作业车间调度问题（FJSP）的8×8、10×10和15×10标准算例进一步验证了算法的稳定性和寻优性能,在3个标准测试算例上改进遗传算法均在较短时间内取得了最优解;最后,将该算法用于求解标签印刷车间的排产问题时,使得加工效率比原来提高了50.3%。因此,提出的改进遗传算法可以有效应用于求解标签印刷车间的排产问题。     

考虑资源闲置成本的多项目调度问题研究

针对资源受限多项目整体进度优化问题,考虑资源分配过程中的资源闲置成本,提出了一种集成工期延迟和资源闲置成本的多目标调度协同优化模型。通过该模型提高了资源的利用效率,理论上实现了多项目在目标成本最小情况下的整体进度方案。综合利用模拟退火算法和遗传算法的优点,设计退火遗传算法对该模型进行求解,通过算例验证了算法和模型的有效性。     

考虑公平性的面向多灾点需求应急资源调度

针对铁路突发事件多需求点多供应点的应急资源调度问题,结合"软时间窗"的概念,以公平性最大和调度总成本最小为优化目标,设计了有多个救援目标的应急资源调度模型,并利用并列选择遗传算法求解。该算法根据目标函数的个数,将种群均等地划分为与目标函数个数相等的子种群,为划分后的各个子种群各自分配一个目标函数,并对其进行独立的选择运算,将各个子种群中适应度高的个体组成新的种群,对这个新的种群进行交叉、变异,生成下一代种群。算例表明,与粒子群优化（PSO）和两阶段启发式算法相比,利用并列选择遗传算法进行计算,目标函数中所有需求点的资源满足程度的方差分别减小了93.88%、89.88%,成本分别减少了5%、0.15%。所提算法能够有效减小所有需求点的资源满足程度的方差,即提高各需求点的公平性,同时降低成本,其在求解多目标规划问题中能够得到更优的解。     

面向差异化覆盖的异构有向传感器节点调度算法

为延长异构有向传感器网络的寿命,提出一种基于改进珊瑚礁优化算法（ECRO）的面向不同监测目标有不同监测要求的节点调度算法。利用ECRO将传感器集合划分成符合覆盖要求的多个集合,通过集合间的调度达到延长网络寿命的目的。对珊瑚礁优化算法（CRO）的改进体现在四个方面：一是在珊瑚礁的雌雄同体繁殖过程中融入生物地理学优化算法中的迁移操作,保留原有种群的优秀解;二是在雌雄同体繁殖过程中采用一种带有混沌参数的差分变异因子,增强子代的优化能力;三是通过对最差个体执行随机反向学习,增强种群的多样性;四是通过CRO与模拟退火算法的结合,增强算法的局部搜索能力。对数值基准函数和节点调度进行了大量的仿真实验。在数值测试方面的结果表明,与遗传算法、模拟退火算法、差分进化算法及其改进算法相比,ECRO的优化能力更强;在传感器网络节点调度方面的结果表明,与贪婪算法、基于学习自动机的差分进化（LADE）算法和未改进的CRO相比,ECRO使网络寿命分别提高了53.8%、19.0%和26.6%,验证了所提算法的有效性。     

基于改进多目标狼群算法的微电网调度优化

为获得微电网系统建设成本、环境成本和运行成本的多重目标优化,以构建系统独立运行模块和仿真模块为核心,设计了微电网系统的多目标调度模型。使用能量模块对微电网调度模型的建设成本、环境成本和运行成本指标进行评价,优化调度算法模块则使用基于个体密度多目标狼群算法(Multi-objective wolf colony algorithm,MOWCA)。在MOWCA算法中引入了非支配排序和个体密度多样性保持操作,有效提高了多目标优化的前沿分布多样性和收敛精度。将所提优化调度算法基于Docker容器技术,对风柴蓄光微电网系统进行调度优化模拟,验证了所提调度算法在多重目标优化上的有效性。     

面向差异化覆盖的异构有向传感器节点调度算法

为延长异构有向传感器网络的寿命，提出一种基于改进珊瑚礁优化算法（ECRO）的面向不同监测目标有不同监测要求的节点调度算法。利用ECRO将传感器集合划分成符合覆盖要求的多个集合，通过集合间的调度达到延长网络寿命的目的。对珊瑚礁优化算法（CRO）的改进体现在四个方面：一是在珊瑚礁的雌雄同体繁殖过程中融入生物地理学优化算法中的迁移操作，保留原有种群的优秀解；二是在雌雄同体繁殖过程中采用一种带有混沌参数的差分变异因子，增强子代的优化能力；三是通过对最差个体执行随机反向学习，增强种群的多样性；四是通过CRO与模拟退火算法的结合，增强算法的局部搜索能力。对数值基准函数和节点调度进行了大量的仿真实验。在数值测试方面的结果表明，与遗传算法、模拟退火算法、差分进化算法及其改进算法相比，ECRO的优化能力更强；在传感器网络节点调度方面的结果表明，与贪婪算法、基于学习自动机的差分进化（LADE）算法和未改进的CRO相比，ECRO使网络寿命分别提高了53.8%、19.0%和26.6%，验证了所提算法的有效性。     

An integrated optimization method for tactical-level planning in liner shipping with heterogeneous ship fleet and environmental considerations

The maritime transportation flows and container demand have been increasing over time, although the COVID-19 pandemic may slow down this trend for some time. One of the common strategies adopted by shipping lines to efficiently serve the existing customers is the deployment of large ships. The current practice in the liner shipping industry is to deploy a combination of ships of different types with different carrying capacities (i.e., heterogeneous fleet), especially at the routes with a significant demand. However, heterogeneous fleets of ships have been investigated by a very few studies addressing the tactical liner shipping decisions (i.e., determination of service frequency, ship fleet deployment, optimization of ship sailing speed, and design of ship schedules). Moreover, limited research efforts have been carried out to simultaneously capture all the major tactical liner shipping decisions using a single solution methodology. Therefore, this study proposes an integrated optimization model that addresses all the major tactical liner shipping decisions and allows the deployment of a heterogeneous ship fleet at each route, considering emissions generated throughout liner shipping operations. The model’s objective maximizes the total turnaround profit generated from liner shipping operations. A decomposition-based heuristic algorithm is presented in this study to solve the model proposed and efficiently tackle large-size problem instances. Numerical experiments, carried out for a number of real-world liner shipping routes, demonstrate the effectiveness of the proposed methodology. A set of managerial insights, obtained from the proposed methodology, are also provided.     

Liner services network design and fleet deployment with empty container repositioning

Transportation demand of shipping container fluctuates due to the seasonality of international trade, thus, every 3–6 months, the liner company has to alter its current liner shipping service network, redeploy ships and design cargo routes with the objective of minimizing the total cost. To solve the problem, the paper presents a mixed integer linear program model. The proposed model incorporates several relevant constraints, such as weekly frequency, the transshipment of cargo between two or more service routes, and transport time. Extensive numerical experiments based on realistic date of Asia–Europe–Oceania shipping operations show that the proposed model can solve real-case problems efficiently by CPLEX. The results demonstrate that the model can reduce ship’s capacity consumption and raise ships’ capacity utilization.     

Slot co-chartering and capacity deployment optimization of liner alliances in containerized maritime logistics industry

With the growing traffic of containerized shipping worldwide, container liners have seen increasing cooperation. Slot co-chartering has drawn wide attention as a way of cooperation within liner alliances. However, liners face the issue of finding proper measures to optimize alliance operations. In response to this, this paper comprehensively considers shipping capacity dispatch for containerized shipping companies within liner alliances. First, we explained the general rules of liner shipping dispatch and analyzed the interfering factors. Second, we created a liner shipping capacity dispatch model without consideration of slot co-chartering costs and a liner shipping capacity dispatch model with consideration of slot co-chartering costs and proposed a new column generation algorithm to solve both problems. Third, we tested our algorithm in a real case of a large Chinese containerized shipping company, which belongs to Ocean Alliance, and an optimal ship dispatch strategy on relevant routes and a decision on optimal slot chartering can be reached. Computational results indicate that the proposed column generation algorithm exhibits effective and efficient performance for large-scale slot co-chartering in liner shipping.     

A large neighbourhood search heuristic for ship routing and scheduling with split loads

The purpose of this paper is to present and solve a new, important planning problem faced by many shipping companies dealing with the transport of bulk products. These shipping companies are committed to carrying some contract cargoes and will try to derive additional revenue from optional spot cargoes. In most of the literature on ship routing and scheduling problems a cargo cannot be transported by more than one ship. By introducing split loads this restriction is removed and each cargo can be transported by several ships. In this paper we propose a large neighbourhood search heuristic for the ship routing and scheduling problem with split loads. Computational results show that the heuristic provides good solutions to real-life instances within reasonable time. It is also shown that introducing split loads can yield significant improvements.     

Optimal fleet design in a ship routing problem

The problem of deciding an optimal fleet (the type of ships and the number of each type) in a real liner shipping problem is considered. The liner shipping problem is a multi-trip vehicle routing problem, and consists of deciding weekly routes for the selected ships. A solution method consisting of three phases is presented. In phase 1, all feasible single routes are generated for the largest ship available. Some of these routes will use only a small portion of the ship's capacity and can be performed by smaller ships at less cost. This fact is used when calculating the cost of each route. In phase 2, the single routes generated in phase 1 are combined into multiple routes. By solving a set partitioning problem (phase 3), where the columns are the routes generated in phases 1 and 2, we find both the optimal fleet and the coherent routes for the fleet.     

改进NSGA-Ⅱ算法的海上搜救调度方法

针对海上搜救资源调度决策困难、干扰多、实时性差、难以实现全局最优问题,本文以黄渤海海域为例,采用改进的非支配排序遗传(NSGA-Ⅱ)算法解决海上船舶搜救资源调度问题.首先,根据AIS以及北斗数据,建立了海上搜救资源的多目标优化模型;其次,改进的NSGA-Ⅱ算法采用基于正态分布交叉(NDX)算子,在扩大搜索范围的基础上,避免陷入局部最优,得到多目标问题完整的Pareto解集;采用综合评价法(TOPSIS)从Pareto解集中求得折衷解,即最终设计的搜救调度方案;最后,在考虑船舶数量约束以及时间约束的条件下,采用改进的NSGA-Ⅱ算法分别与NSGA-Ⅱ算法和贪婪算法进行对比,并采用黄渤海海域船舶采集数据进行仿真.结果表明该算法能够有效解决海上搜救资源调度优化问题.     

设备故障条件下柔性作业车间重调度方法

设备故障是生产实践中最为常见的一类不确定事件,它易对正常的生产计划造成影响。为了有效应对生产中的设备随机故障干扰,对设备随机故障条件下的柔性作业车间调度问题进行了研究,提出了一种基于组合策略的重调度方法。在重调度方法中,对重调度成本进行了系统分析并建立了重调度成本函数,设计了两种重调度策略,并结合免疫算法对遗传算法进行了改进,用于模型的求解计算。通过算例分析,验证了方法的可行性和有效性。实验结果表明,所提出方法能够更好地处理多种情况下的设备故障扰动。     

考虑泊位偏好和岸桥移动的泊位岸桥联合调度

为了更高效地利用码头资源,同时考虑泊位资源和岸桥资源,建立了考虑泊位偏好和岸桥移动频数的泊位岸桥联合调度两阶段模型.第一阶段模型采用船舶到港时间可变的到港策略,建立了以船舶等待成本、泊位偏离成本、延迟离港成本之和最小为目标的混合整数规划模型.第二阶段模型考虑了岸桥的干扰约束,建立了以岸桥移动频数最小为目标的整数规划模型...