Optimal Scheduling of Multiple Rail Cranes in Rail Stations with Interference Crane Areas

In this paper,we consider a multi-crane scheduling problem in rail stations because their operations directly influence the throughput of the rail stations.In particular,the job is not only assigned to cranes but also the job sequencing is implemented for each crane to minimize the makespan of cranes.A dual cycle of cranes is used to minimize the number of working cycles of cranes.The rail crane scheduling problems in this study are based on the movement of containers.We consider not only the gantry moves,but also the trolley moves as well as the rehandle cases are also included.A mathematical model of multi-crane scheduling is developed.The traditional and parallel simulated annealing(SA)are adapted to determine the optimal scheduling solutions.Numerical examples are conducted to evaluate the applicability of the proposed algorithms.Verification of the proposed parallel SA is done by comparing it to existing previous works.Results of numerical computation highlighted that the parallel SA algorithm outperformed the SA and gave better solutions than other considered algorithms.

The Multi-Agent Modeling and Calculated Analysis of Shop Logistics Scheduling in the Manufacturing Enterprises

In this paper,the multi-agent model about shop logistics is set up. This model has 8 agents: raw materials stock agent,process agent,testing agent,transition agent,production information agent,scheduling agent,process agent and stock agent. The scheduling agent has three subagents: manager agent (MA),resource agent (RA) and part agent (PA). MA,PA and RA are communicating equally that guarantees agility of the whole MAS system. The part tasks pass between MA,RA and PA as an integer,which can guarantee the consistency of the data. We use a detailed example about shop logistics scheduling in a semiconductor company to explain the principle. In this example,we use two scheduling strategies: FCFS and SPT. The result data indicates that the average flow time and lingering ratio are changed using different strategy. It is proves that the multi-agent scheduling is useful.

The Information Modeling and Intelligent Optimization Method for Logistics Vehicle Routing and Scheduling with Multi-objective and Multi-constraint

The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.

Modified Shifting Bottleneck Heuristic for Scheduling Problems of Large-Scale Job Shops

A modified shifting bottleneck algorithm was proposed to solve scheduling problems of a large-scale job shop.Firstly,a new structured algorithm was employed for sub-problems so as to reduce the computational burden and suit for large-scale instances more effectively.The modified cycle avoidance method,incorporating with the disjunctive graph model and topological sort algorithm,was applied to guaranteeing the feasibility of solutions with considering delayed precedence constraints.Finally,simulation experiments were carried out to verify the feasibility and effectiveness of the modified method.The results demonstrate that the proposed algorithm can solve the large-scale job shop scheduling problems(JSSPs) within a reasonable period of time and obtaining satisfactory solutions simultaneously.

Exploring Hybrid Genetic Algorithm Based Large-Scale Logistics Distribution for BBG Supermarket

In the large-scale logistics distribution of single logistic center,the method based on traditional genetic algorithm is slow in evolution and easy to fall into the local optimal solution.Addressing at this issue,we propose a novel approach of exploring hybrid genetic algorithm based large-scale logistic distribution for BBG supermarket.We integrate greedy algorithm and hillclimbing algorithm into genetic algorithm.Greedy algorithm is applied to initialize the population,and then hill-climbing algorithm is used to optimize individuals in each generation after selection,crossover and mutation.Our approach is evaluated on the dataset of BBG Supermarket which is one of the top 10 supermarkets in China.Experimental results show that our method outperforms some other methods in the field.

Modeling of container terminal logistics system based on multi-agent

The similarities and differences between the container terminal logistics system(CTLS)and the Harvard-architecture computer system are compared in terms of organization and architecture.The mapping relation and the modeling framework of the CTLS are presented based on multi-agent,and the successful algorithms in the computer domain are applied to the modeling framework,such as the dynamic priority and multilevel feedback scheduling algorithm.In addition,a model and simulation on a certain quay at Shanghai harbor is built up on the AnyLogic platform to support the decision-making of terminal on service cost.It validates the feasibility and creditability of the above systematic methodology.

Modeling and Analysis of Production Logistics Spatio-Temporal Graph Network Driven by Digital Twin

In the process of logistics distribution of manufacturing enterprises, the automatic scheduling method based on the algorithm model has the advantages of accurate calculation and stable operation, but it excessively relies on the results of data calculation, ignores historical information and empirical data in the solving process, and has the bottleneck of low processing dimension and small processing scale. Therefore, in the digital twin(DT) system based on virtual and real fusion, a modeling and analysis method of production logistics spatio-temporal graph network model is proposed, considering the characteristics of road network topology and time-varying data. In the DT system, the temporal graph network model of the production logistics task is established and combined with the network topology, and the historical scheduling information about logistics elements is stored in the nodes. When the dynamic task arrives, a multi-stage links probability prediction method is adopted to predict the possibility of loading, driving, and other link relationships between task-related entity nodes at each stage. Several experiments are carried out, and the prediction accuracy of the digital twin-based temporal graph network(DTGN) model trained by historical scheduling information reaches 99.2% when the appropriate batch size is selected. Through logistics simulation experiments, the feasibility and the effectiveness of production logistics spatio-temporal graph network analysis methods based on historical scheduling information are verified.

低碳自动化码头AGV物流调度和有序充电的联合经济优化

针对现有自动化码头自动导航集卡(AGV)充电策略未能与分布式电源出力充分协同等问题,提出一种自动化码头AGV物流调度和有序充电联合经济优化方法.首先,解析AGV物流调度和充电调度之间的协同关系,构建联合优化框架;其次,针对AGV物流调度模块,提出考虑码头内外集卡道路隔离要求的AGV行驶距离计算方法;再次,针对AGV充电模块,定义AGV充电状态的判断条件和选桩方法;进而以码头购电费用最小为目标,考虑分时电价、风光上网电价、电量平衡约束、终止时刻的剩余电量约束、决策变量的上下限约束、物流调度约束,构建物流调度和有序充电联合优化模型,提出基于改进粒子群优化算法的快速求解方法;最后,通过某码头的实际案例,验证所提方法的有效性和经济性.

乡村振兴与区域物流时空耦合及影响因素

基于协调发展视角,构建乡村振兴系统与区域物流系统评价体系,利用熵权法、耦合协调模型,对长江经济带11个省市2005—2020年乡村振兴系统与区域物流系统综合水平、时空耦合关系进行探究,并借助障碍度模型诊断各系统的主要障碍因素。结果表明:长江经济带各省市乡村振兴系统与区域物流系统发展耦合协调度总体呈上升趋势,两者综合指数整体上均不高,并呈现出物流滞后型发展;两者耦合协调水平,空间分布格局呈现东部>中部>西部、“沿海高,内陆低”特征。由障碍度模型可知,物流发展规模对区域物流发展影响最大,乡村振兴受生态宜居和乡村文明的影响最大。

基于配送服务模式及路径策略的物流无人机调度模型

针对远距离城市空间物流无人机配送问题,提出基于两级多中心场景的无人机(Unmanned Aerial Vehicle, UAV)配送服务模式.综合考虑客户时间、无人机充电和配送路径策略等内容,建立以最小配送总成本为目标的两级多中心物流无人机城市配送调度模型,通过比较标准算例下Cplex优化软件与鲸鱼优化算法(Whale Optimization Algorithm, WOA)、遗传算法(Genetic Algorithm,GA)和灰狼优化算法(Grey Wolf Optimization, GWO)3种启发式算法的实验结果,确定求解算法以设计两阶段算法进行求解.以天津市中心城区为例进行模型求解,并从需求点规模、机型和时间窗3方面进行敏感度分析.研究结果表明:与闭合式路径相比,半开放式路径策略分别节省约11.09%、60.45%的配送总成本和时间窗惩罚成本,成本节省随配送规模增大而增加;提出的固定路径班次模式相比现有按需模式,其可变成本和总成本降幅均高于18%,使用无人机数量减少近28%,生成的班次时刻表和飞行路径时空图可显现不同模式优劣.本文提出的模型对优化远距离城市物流无人机调度方案及配送模式有一定实用价值,可为企业在物流无人机的配送规划方面提供参考.

基于“分类-调度”优化的应急物资动态调度-重调度算法

由于灾情的不确定性和信息延迟,应急救灾物资配送与车辆调度常面临配送过程中出现需求点信息动态变化的复杂环境,如何在动态变化的环境下快速反应,准确高效地实现应急物资重调度显得尤其重要。针对需求点信息动态变化的复杂环境,建立一个应急物资动态调度模型,并提出基于“分类-调度”的动态调度算法,通过k-means分类将原复杂问题降解为多个简单TSP问题,随着需求点信息的变化可动态调整分类,以实现快速重调度;设计相应的路线调整策略以保证方案的可行性;最后利用遗传算法求解。数值试验结果证明了所提方法的有效性。

基于资源池视角的废弃电子产品逆向物流系统协同模型研究

废弃电子产品具有双重属性,其逆向物流网络分布范围广并且数量增长迅速。不同主体对废弃电子产品逆向物流的需求是不同的,单个的逆向物流节点很难同时满足不同需求,如何合理高效地协调各个逆向物流节点的资源,以满足各个逆向物流任务是亟待解决的重要问题。在数字技术的支持下,逆向物流信息协同领域得到丰富和完善,针对传统模式中废弃电子产品回收处理过程存在的资源利用率低、浪费严重等情况,将整个逆向物流网络看作资源池,采用智能体(Agent)处理功能网络的任务迁移模式代替传统方法中大量的数据迁移模式,构建了基于多智能体(Multi-Agent)的废弃电子产品逆向物流系统资源协同模型,最后仿真结果验证了所建模型的有效性和可行性,为废弃电子产品逆向物流系统的资源调度分配提供了新的解决方案。

基于文献计量和知识图谱的电动车辆路径问题研究综述

为揭示电动车辆路径问题领域的研究与发展现状,对CNKI和Web of Science数据库中电动车辆路径问题1994-2022年间的期刊文献进行知识挖掘与分析。基于文献计量学的量化分析与知识图谱的可视化,通过分析文献外部特征和共被引情况,梳理研究热点及热点演进趋势,归纳研究主题,总结出电动车辆路径问题的知识域包括研究主题和应用场景,其中,研究主题由变体研究、充电调度、求解方法三部分构成;对电动车辆路径问题在复杂实际问题、高效求解算法方面的未来发展进行展望,这将为电动车辆路径问题研究的深入化与国际化提供一定的推动作用。

分布式AGV调度研究综述与发展趋势分析

智能物流是智能制造的重要组成部分,也是物流行业发展的必然趋势。自动导引车(automated guided vehicle,AGV)可无缝衔接企业生产系统和仓储系统,是实现智能物流的一种重要技术装备。AGV调度对于提高物流效率、降低能源消耗、减少运输过程中货物的损坏和事故的发生等至关重要。AGV调度问题涉及任务分配和路径规划两个子问题,从调度方式上可分为集中式调度和分布式调度。随着AGV设备智能化水平的提升,分布式AGV系统凭借可扩展性、高可靠性等优势已成为当前研究的热点。首先描述了分布式AGV调度问题;然后分别阐述分析了分布式AGV任务分配和路径规划领域的研究现状;最后对分布式AGV调度未来的发展前景进行了展望。

退役产品拆解车间逆向生产与物流集成调度优化

针对退役产品拆解工艺与物流存在的不确定性,提出一种逆向生产和物流集成调度拆解车间优化方法.分析退役产品拆解车间作业过程中生产与物流相互影响因素,以优化逆向生产与物流过程中的最大完工时长和提前/拖期成本为目标构建车间多目标调度模型.基于物流路径决策,设计一种带基因修复的变异算子改进NSGA-Ⅱ算法的变异准确性,提升算法的求解有效性.以某公司的手机拆解线为例,分析多尺寸的生产、物流算例.结果表明,随着自适应引导车数量的增加,大、小尺寸算例的拖期成本分别减少了57.1%和58.3%,验证了模型的有效性.

基于logistic回归模型的Hadoop本地任务调度优化算法

当一个工作节点有多个本地任务可执行时,默认情况下,调度器都是按照任务被发现的先后顺序来进行执行,效率低下。为了优化对本地任务的调度,提出了一种基于机器学习的Hadoop本地任务调度优化算法。选取定义与任务相关的特征向量,然后基于logistic回归模型的机器学习方式得到各向量的作用权值,将任务进行优先级排序,并通过过载规则不断更新模型。通过实验证明,提出的算法在改善map任务的数据本地性的同时,降低了作业运行时间。

城市物流无人机起飞时刻排序问题研究

文中基于动态优先级计算了无人机起飞的时刻及起飞顺序.选取货物价值、收货点距离和最晚收货时间作为影响排序的主要因素,根据熵权法判断不同影响因素对物流订单优先级的影响程度,结合模糊聚类与三维优先级表调度规则判定物流订单的任务优先水平.并根据订单优先级设定延误成本系数,以最小总延误成本为目标构建物流无人机起飞时刻排序模型;提出相应的改进遗传算法对模型进行求解.数值仿真实验结果表明:所提方法可以安全高效地安排无人机起飞队列顺序,降低无人机配送的总延误成本.

面向无人机物流的分布式任务调度

为提高无人机物流任务调度系统的实时性、可靠性和扩展性,本文提出一种面向通用航空无人机物流的分布式自主任务调度系统,并阐述了该系统的概念、架构、机制和运行模式;为实现物流任务的高效分配,适用于常规物流及紧急物流任务,结合集中调度及边缘自主两种调度模式。实验证明:该系统可为通用航空无人机物流提供一种有效途径,成为未来无人机物流的主要调度模式之一。

基于模糊时间窗的智慧物流越库调度仿真研究

在越库配送中商品和货物到了配送中心后直接可在站台上向客户配送,能够有效降低库存和运输成本,但是对车辆调度效率要求较高。若进出站台的车辆得不到精准排序,会直接影响货物出站时间。为进一步提升越库调度效果,提出基于模糊时间窗的智慧物流越库调度方法。基于物流越库类型的分析结果,结合模糊时间窗制定调度目标函数以及约束条件,构建物流越库调度模型。引入萤火虫算法对调度模型求解,获取模型最佳目标函数值,输出模型调度结果,实现智慧物流的越库调度。实验结果表明,上述方法对物流越库调度模型的求解与最优解的差距最小,且物流调度范围对其影响不大,可在10s之内完成智慧物流的越库调度,应用效率较高。

考虑硬时间窗的车辆调度优化

该文以第三方汽车物流企业为研究对象进行车辆调度优化,根据实际问题进行问卷调研,对企业某货物运输车辆调度方案的现状进行分析,收集信息辅助验证硬时间窗的车辆调度优化问题,利用改进后的C-W节约法和Lingo软件进行车辆调度优化设计,使货物车辆调度行驶的路径最短,调度安排更加合理,从而达到降低运输成本、准时快速高效率运输的目的,最后将结果进行验证。