粒子群算法求解具有机器灵活性的FFSP

对柔性流水车间调度问题(FFSP)进行了分析阐述,在此基础上对某饲料厂的饲料生产过程建立了具有机器灵活性的柔性流水车间调度模型,该模型中存在多台制粒机,既能加工大颗粒饲料,又能加工小颗粒饲料,但是必须在开始加工之前确定各台机器的用途,增加了柔性流水车间调度的难度。利用新型的粒子群算法以最小化最大完工时间为目标对该模型求解,为了克服粒子群算法易陷入局部极值的缺点,提出基于位置相似度的邻域结构,并对邻域内的较优粒子采用基于最大完工时间排序的学习方式进行局部搜索。实验结果表明,该方法有利于克服粒子群算法的早熟缺陷,有效地解决了饲料生产调度问题,有一定的应用价值。     

面向FMS基于改进的混合PSO-GA的多AGV调度算法研究

在柔性制造系统(Flexible Manufacturing System,FMS)中,自动导引小车(Automated Guided Vehicle,AGV)常被用于搬运物料或产品,因此AGV的优化调度成为提高生产效率的关键。AGV的调度除了要考虑AGV的任务分配问题,还需要参考每个操作的花费时间、小车的运行时间等因素。相比于单AGV调度算法,多AGV多任务调度算法需要一个更加复杂的模型来支撑。在考虑AGV的电量状况下,以最小完成时间与调度最少AGV数量作为优化目标,提出了一种改进的混合遗传算法与粒子群算法(PSO-GA),并基于该算法给出了多AGV调度模型,在此基础上进行了仿真实验。结果表明,相较于单一的GA或PSO算法,所提算法在全局寻优收敛与运行时间上有明显的优化效果,而相比于现有的混合PSO-GA算法,其在搜索精度和收敛速度上有进一步提高。     

柔性车间内AGV最优替换比研究

针对柔性车间内AGV最优替换比问题,建立了员工、AGV共同搬运的柔性车间调度模型。该模型以完工时间最小、成本最少为目标,从静态分析与动态分析两方面求得最优替换比。静态分析部分主要采用线性规划完成,动态分析部分采用粒子群算法进行求解。针对动态分析部分,提出启发式规则分配AGV、员工搬运操作。通过算例得出AGV最优替换比的帕累托最优解集。并发现AGV最优替换比和AGV价格有关。     

自动化集装箱港口考虑AGV伴侣的AGV调度优化问题研究

为提高自动化集装箱港口设备的工作效率,提出了一种新的集装箱进出口工艺：堆场—场桥—AGV伴侣—AGV—岸桥。在考虑AGV伴侣容量限制的基础上,建立了带时间窗约束的AGV调度混合整数规划模型,设计了启发式算法求解AGV伴侣时间窗,采用粒子群算法进行求解,得出了相应AGV调度优化方案。求解结果表明,AGV伴侣的设置能有效改善AGV与场桥间的协调性、设备间的等待时间;并且AGV伴侣容量一定时,场桥的等待时间随着AGV的数量增加而减少。     

自动化码头考虑缓冲区的设备协调调度优化

为研究自动化码头缓冲区的设置对装卸设备作业协调性的影响,针对“双小车岸桥+AGV+缓冲支架+自动化轨道吊”的装卸工艺,利用缓冲有限的柔性流水车间调度理论建立集成调度优化模型,设计了以NEH启发式算法产生初始解的遗传算法对模型进行求解,得出相应的设备调度优化方案与完工时间,并通过对比遗传算法与粒子群算法的运算结果验证了提出的模型与算法的有效性,进而分析了不同缓存区容量对完工时间以及设备使用率的影响。结果表明,设置缓冲区能有效提高不同设备之间的作业协调性,显著减少AGV的使用数量与作业完工时间。     

自动化集装箱码头AGV配置与调度耦合问题研究

针对自动导引小车（Automated Guided Vehicle，AGV）数量偏多导致的自动化码头水平运输区域拥堵的情况，采用多学科变量耦合优化设计的方法对自动化码头AGV调度与AGV配置问题进行研究。先以最小化岸边等待时间为目标建立AGV调度模型，再以最小化AGV数量为目标建立AGV配置模型。并将完工时刻和AGV数量作为公用设计变量连接两个模型，建立了协调调度耦合模型。设计算例，利用遗传算法（Genetic Algorithm，GA）收敛速度快的特点对该耦合模型进行求解，经反复迭代计算后得出最优AGV数量与AGV调度方案。最后，扩大算例规模，设计9组实验，比较了GA、粒子群算法（Particle Swarm Optimization，PSO）和蚁群算法（Ant Colony Optimization，ACO）的求解结果，结果表明随着算例规模的增大，GA的求解能力更为突出，从而验证了设计的算法的可行性。     

改进的粒子群算法在多目标车间调度的应用

利用粒子群算法解决车间调度问题,是一种有效的策略。对粒子群算法进行分析,针对多目标的柔性车间调度问题,构建了以加工时间最小化、加工成本最小化和单机器最大负荷最小化的多目标柔性车间调度模型。提出基于交叉变异的变参粒子群算法,以提高其跳出局部最优快速达到全局最优的能力。同时,引入智能小车概念,将运输时间考虑到此调度中。并将该方法用于某离散制造业的柔性车间作业调度中,最后验证了该算法的实用性及高效性。     

基于改进NSGA-Ⅱ的考虑自动引导车充电策略的集成调度

针对自动引导车（AGV）在自动化集装箱码头（ACT）执行任务过程中的电量问题，提出基于改进的非支配排序遗传算法-Ⅱ（NSGA-Ⅱ）的考虑AGV充电策略的集成调度。首先，在岸桥、场桥和AGV集成调度模式下，考虑AGV在不同作业状态下的耗电量，并建立以最小化作业完工时间和总耗电量为目标的多目标混合规划模型；其次，为提高传统NSGA-Ⅱ的性能，设计自适应NSGA-Ⅱ，并将所提算法与CPLEX求解器、NSGA-Ⅱ和多目标粒子群优化（MOPSO）算法进行性能对比；最后，设计AGV不同充电策略并对设备数量配比进行实验研究。算法对比实验结果表明：相较于传统NSGA-Ⅱ算法，自适应NSGA-Ⅱ对双目标的优化分别提升了2.8%和2.63%。利用自适应NSGA-Ⅱ进行的充电策略和设备数量配比实验的结果表明：增加AGV充电次数能够减少AGV的充电时间，且调整设备数量配比至3∶3∶9和3∶7∶3时，场桥和AGV的时间利用率分别达到最高。可见，AGV充电策略及设备数量配比对码头多设备集成调度有一定影响。     

基于粒子群优化和禁忌搜索的混合调度算法

提出了用于解决作业车间调度问题的离散版粒子群优化算法。该算法采用基于先后表编码方案和新的位移更新模型,使具有连续本质的粒子群优化算法直接适用于车间调度问题。同时,利用粒子群优化算法的全局搜索能力和禁忌搜索算法的自适应优点,将粒子群优化算法和禁忌搜索结合起来,设计了广义粒子群优化算法和粒子群—禁忌搜索交替算法两种混合调度算法。实验结果表明,两种混合调度算法能够有效地、高质量地解决作业车间调度问题。     

改进离散粒子群算法求解柔性流水车间调度问题

针对以最小化完工时间为目标的柔性流水车间调度问题(FFSP),提出了一种改进离散粒子群(DPSO)算法.所提算法重新定义粒子速度和位置的相关算子,并引入编码矩阵和解码矩阵来表示工件、机器以及调度之间的关系.为了提高柔性流水车间调度问题求解的改进离散粒子群算法的初始群体质量,通过分析初始机器选择与调度总完工时间的关系,首次提出一种基于NEH算法的最短用时分解策略算法.仿真实验结果表明,该算法在求解柔性流水车间调度问题上有很好的性能,是一种有效的调度算法.     

多搬运任务下考虑碰撞避免的AGV路径规划

为提升自动导引小车在“货到人”仓库中的运行效率,针对AGV-托盘任务分配、单AGV路径规划及多AGV碰撞避免三个子问题的研究,以最小化AGV行驶距离为目标构建数学模型。首先,根据AGV与托盘的双边匹配问题特点设计改进的匈牙利算法求解匹配结果。其次,提出一种二维编码机制的改进遗传算法（improved genetic algorithm,IGA）,采用一种局部搜索算子代替原变异操作,在提高算法搜索性能的基础上使其成功应用于单AGV路径规划问题。然后,利用时空数据设计一种三维网格冲突检测方法,并根据商品SKU数量设定AGV的优先级以降低多AGV执行任务时的碰撞概率。最后,在32 m×22 m的仓库中针对不考虑碰撞与考虑碰撞两种情形进行AGV路径优化分析,给出合理的行驶距离和碰撞次数。IGA与标准遗传算法的对比结果显示,IGA能够在合理的时间内获得更高质量的解,行驶距离减少约1.74%,算法求解时间缩短约37.07%。此外,针对AGV数量灵敏度分析,在不同目标托盘规模下测试不同数量的AGV对行驶距离和碰撞次数的影响,发现14~16台AGV数量是最佳配置,验证了模型的可行性和算法的有效性。     

干扰约束下考虑分组作业面的岸桥AGV联合调度

为解决自动化码头海侧多阶段设备作业的协调问题,加快集装箱在码头内部的周转过程。考虑干扰约束下分组作业面的的岸桥自动导引小车（AGV）联合调度问题。以岸桥、AGV完工时间和AGV等待时间加权总和最小为目标,考虑岸桥实际操作中的干扰约束与AGV堵塞等待等情况,建立岸桥与AGV联合调度优化模型。提出岸桥动态调度与AGV分组作业面调度模式,设计不同规模的算例,并采用遗传算法（GA）进行求解,将计算结果与传统调度模式进行对比。结果表明,该算法能有效提高岸桥与AGV作业效率,降低AGV的等待时间与堵塞次数,为码头实际作业提供依据。     

Dynamic Scheduling and Path Planning of Automated Guided Vehicles in Automatic Container Terminal

The uninterrupted operation of the quay crane (QC) ensures that the large container ship can depart port within laytime, which effectively reduces the handling cost for the container terminal and ship owners. The QC waiting caused by automated guided vehicles (AGVs) delay in the uncertain environment can be alleviated by dynamic scheduling optimization. A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems, in which the scheduling scheme determines the starting and ending nodes of paths, and the choice of paths between nodes affects the scheduling of subsequent AGVs. This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime. A dynamic optimization algorithm, including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm, is designed to solve the optimal AGV scheduling and path schemes. A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.      

紧致密集Auto Store系统AGV路径规划与避碰策略

针对新兴紧致密集仓储系统Auto Store具有短途挪库作业多、顶层AGV冲突多、货架结构性角落多等特点,提出一种离线-在线两阶段AGV优化调度方法。离线路径规划阶段,给出改进双层A*算法,在拓扑图建模划分搜索区域基础上,上层通过考虑冲突的启发式函数和考虑转弯的代价函数寻求可行区域,下层在此区域基础上搜索最优路径。在线AGV运行阶段,针对两AGV冲突,扩充了回退策略和路线重规划策略;针对多AGV冲突,提出一种基于贪心算法的区域避碰决策策略,以控制问题规模。最后利用Flexsim仿真进行了验证,结果表明,较于标准A*算法,改进A*算法能在保证搜索效率的同时获得冲突较少的初始路径方案;较于优先级策略,区域避碰策略能减少AGV等待时间;将二者相结合,能缩短整体作业完成时间,且随着AGV数量和作业任务增多,优势越明显。     

Conflict-free scheduling and routing of automated guided vehicles in mesh topologies

Many storing and manufacturing systems tend to use automated guided vehicles (AGV) for speed, quality and safety as transporting objects. In this paper an integrated algorithm for scheduling and routing of AGVs in mesh-like systems is presented. The main characteristics of the scheduling algorithm are as follows: (1) prediction and prevention of conflicts, (2) arbitrary choice for AGVs to traverse shortest path from source to destination, (3) effect of priority policies to the scheduling result, and (4) no theoretical limitation on the number of participated AGVs. The proposed greedy algorithm for routing reduces the average number of conflicts and is closely related to the scheduling algorithm. We will also present mathematical and statistical models for the analysis of the algorithms.     

Flow-shop path planning for multi-automated guided vehicles in intelligent textile spinning cyber-physical production systems dynamic environment

Aiming at the path planning and decision-making problem, multi-automated guided vehicles (AGVs) have played an increasingly important role in the multi-stage industries, e.g., textile spinning. We recast a framework to investigate the improved genetic algorithm (GA) on multi-AGV path optimization within spinning drawing frames to solve the complex multi-AGV maneuvering scheduling decision and path planning problem. The study reported in this paper simplifies the scheduling model to meet the drawing workshop's real-time application requirements. According to the characteristics of decision variables, the model divides into two decision variables: time-independent variables and time-dependent variables. The first step is to use a GA to solve the AGV resource allocation problem based on the AGV resource pool strategy and specify the sliver can's transportation task. The second step is to determine the AGV transportation scheduling problem based on the sliver can-AGV matching information obtained in the first step. One significant advantage of the presented approach is that the fitness function is calculated based on the machine selection strategy, AGV resource pool strategy, and the process constraints, determining the scheduling sequence of the AGVs to deliver can. Moreover, it discovered that double-path decision-making constraints minimize the total path distance of all AGVs, and minimizing single-path distances of each AGVs exerted. By using the improved GA, simulation results show that the total path distance was shortened.     

Resource management in decentralized industrial Automated Guided Vehicle systems

This paper proposes an advanced decentralized method where an Automated Guided Vehicle (AGV) can optimally insert charging stations into an already assigned optimal tour of task locations. In today's industrial AGV systems, advanced algorithms and techniques are used to control the whole fleet of AGVs robustly and efficiently. While in academia, much research is conducted towards every aspect of AGV control. However, resource management or battery management is still one aspect which is usually omitted in research. In current industrial AGV systems, AGVs operate until their resource level drops below a certain threshold. Subsequently, they head to a charging station to charge fully. This programmed behaviour may have a negative impact on the manufacturing systems performance. AGVs lose time charging at inconvenient moments while this time loss could be avoided. Using the approach, an AGV can choose independently when it will visit a charging station and how long it will charge there. A general constrained optimization algorithm will be used to solve the problem and the current industrial resource management will be used as a benchmark. We use a simple extension of the Traveling Salesman Problem (TSP) representation to model our approach. The paper follows a decentral approach which is in the interest of the authors. The result of the proposal is a compact and practical method which can be used in today's operative central or decentral controlled AGV systems.