Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm

Automated Guided Vehicles (AGVs) are among various advanced material handling techniques that are finding increasing applications today. They can be interfaced to various other production and storage equipment and controlled through an intelligent computer control system. Both the scheduling of operations on machine centers as well as the scheduling of AGVs are essential factors contributing to the efficiency of the overall flexible manufacturing system (FMS). An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs an integral part of the overall scheduling activity. In this paper, simultaneous scheduling of parts and AGVs is done for a particular type of FMS environment by using a non-traditional optimization technique called the adaptive genetic algorithm (AGA). The problem considered here is a large variety problem (16 machines and 43 parts) and combined objective function (minimizing penalty cost and minimizing machine idle time). If the parts and AGVs are properly scheduled, then the idle time of the machining center can be minimized; as such, their utilization can be maximized. Minimizing the penalty cost for not meeting the delivery date is also considered in this work. Two contradictory objectives are to be achieved simultaneously by scheduling parts and AGVs using the adaptive genetic algorithm. The results are compared to those obtained by conventional genetic algorithm.     

Dispatching, routing, and scheduling of two automated guided vehicles in a flexible manufacturing system

This article presents a new approach for planning the dispatching, conflict-free routing, and scheduling of automated guided vehicles in a flexible manufacturing system. The problem is solved optimally in an integrated manner, contrary to the traditional approach in which the problem is decomposed in three steps that are solved sequentially. The algorithm is based on dynamic programming and is solved on a rolling time horizon. Three dominance criteria are used to limit the size of the state space. The method finds the transportation plan minimizing the makespan (the completion time for all the tasks). Various results are discussed. A heuristic version of the algorithm is also proposed for an extension of the method to many vehicles.     

基于遗传算法的配送车辆优化调度

分析了汽车运输的特点和成本,针对配送企业,建立了实际的物流配送,并量化各项评价指标,给出了采用遗传算法进行优化调度的数学模型,并结合两阶段法和改进的遗传算子编写了实现算法。     

应用遗传算法研究FMS刀具需求模型

针对柔性制造系统刀具需求模型及求解中存在的问题,提出以零件加工时间最短为优化目标,以刀具购买资金为约束,基于遗传法求解的新型柔性制造系统(FMS)刀具需求规划模型。通过比较分析,验证了该FMS刀具需求规划模型及其遗传算法求解需求问题的合理性和优越性。     

基于改进遗传算法的物料配送多 AGV 调度优化

为解决混流作业车间中物料配送多自动导引车（ AGV）的调度优化问题,以AGV配送物料行驶时间最短为目标建立数学优化模型,提出了一种改进的遗传算法进行AGV的任务分配和配送路径优化。在算法设计过程中,采用直接反映AGV配送路径和任务分配的整数编码方式,为避免常规交叉变异过程中产生不可行解的情况,改进了交叉变异算子,采用最好－最坏交叉模式和基因段随机交换的变异模式,获得了优化的调度方案。最后,以某重型机械公司装配车间内物料输送AGV调度优化为实例,并与遗传算法和分支定界法进行对比,验证了所提方法的可行性和有效性。     

An adaptive genetic algorithm with multiple operators for flowshop scheduling

Genetic algorithms (GAs) are a class of effective parallel searching algorithms inspired by the idea of “survival of the fittest”, which has been successfully applied to a variety of problems, especially in the fields of manufacturing and scheduling. However, it is reported that traditional GAs often suffer from the weaknesses of premature convergence as well as parameter and operator dependence. So far, many improved methods with adaptive parameters or hybrid structures have been proposed, but there is little literature considering the adaptive control of genetic operators. In this paper, an adaptive GA (AGA) with multiple operators is proposed for flowshop scheduling, which is a typical NP-hard optimisation problem with many industrial applications and has been widely studied in both academic and engineering fields. In AGA, multiple different genetic operators are employed in an adaptive hybrid way to enhance the exploration and exploitation abilities so as to prevent premature convergence and achieve superior performance. It especially important to stress that the utilising ratio of each operator for hybridisation is adaptively and dynamically controlled during the evolutionary searching process. Simulation results based on benchmarks demonstrate the effectiveness of AGA by contrast with traditional GAs. And the effect of the adaptive control of the operator and the effects of some parameters on the optimisation performance are discussed as well.     

A genetic algorithm for scheduling flexible manufacturing systems

General job shop scheduling and rescheduling with alternative route choices for an FMS environment is addressed in this paper. A genetic algorithm is proposed to derive an optimal combination of priority dispatching rules pdrs (independentpdrs one each for one Work Cell WC), to resolve the conflict among the contending jobs in the Giffler and Thompson GT procedure. The performance is compared with regard to makes-pan criteria and computational time. The optimal WCwise-pdr is proved to be efficient in providing optimal solutions in a reasonable computational time. Also, the proposed GA based heuristic method is extended to revise schedules on the arrival of new jobs, and on the failure of equipment to address the dynamic operation mode of flexible manufacturing systems. An iterative search technique is proposed to find the best route choice for all operations to provide a feasible and optimal solution. The applicability and usefulness of the proposed methodology for the operation and control of FMS in real-time are illustrated with examples. The scope of the genetic search process and future research directions are discussed.     

Experimental investigation of an FMS due-date scheduling problem: Evaluation of machine and AGV scheduling rules

Although extensive research has been conducted to solve design and operational problems of automated manufacturing systems, many of the problems still remain unsolved. This article investigates the scheduling problems of flexible manufacturing systems (FMSs). Specifically, the relative performances of machine and automated guided vehicle (AGV) scheduling rules are analyzed against various due-date criteria. First, the relevant literature is briefly reviewed, and then the rules are tested under different experimental conditions by using a simulation model of an FMS. The sensitivity to AGV workload, buffer capacity, and processing-time distribution is also investigated to assess the robustness of the scheduling rules.     

基于面向对象遗传算法的柔性车间调度研究

针对柔性车间作业调度问题,基于面向对象思想设计了一种遗传算法。该算法将实际操作封装成基因类,工件封装成工件类,机床封装成机床类,调度方案封装成染色体类,基于堆栈思想设计了染色体的创建方式,提出了全新的染色体解码方式及变异方式。通过仿真实例证明了该算法的有效性和实用性。     

Analysis of the number of automated guided vehicles required in flexible manufacturing systems

In this paper we examine some of the general principles and analysis methods that are used to design an automated material handling system. The paper focuses on one type of automated handling system that seems especially suitable for automation in discrete-product manufacturing. These types of automated handling systems are called automated guided vehicle systems. These systems are most applicable for the automation of low-and medium-volume handling situations, where the routeing of materials is more individualised. For guided vehicles, a new quantitative method for analysing these systems is developed in the paper. Examples are presented to demonstrate the method.     

柔性制造系统中生产调度问题求解的一种符号算法

讨论了基于有序二叉决策图的有界Petri网符号分析,给出了其中镜像计算的一种新算法,进而建立了柔性制造系统生产调度问题的符号有序二叉决策图求解算法.该算法在求解过程中通过对状态空间及其搜索过程中相关数据的有序二叉决策图表示,避免了状态和搜索的显示枚举,实现了隐式高效操作,进而改善了算法的计算性能.仿真实验表明了算法的有效性.     

Simultaneous scheduling of machines and vehicles in an FMS environment with alternative routing

Scheduling of flexible manufacturing systems is a well-known NP-hard problem which is very complex, due to additional considerations like material handling, alternative routing, and alternative machines. Improvement in the performance of a flexible manufacturing system can be expected by efficient utilization of its resources, by proper integration and synchronization of their scheduling. Differential evolution is a powerful tool which proved itself as a better alternative for solving optimization problems like scheduling. In this paper, the authors addressed simultaneous scheduling of both machines and material handling system with alternative machines for the makespan minimization objective. The authors proposed a machine selection heuristic and a vehicle assignment heuristic which are incorporated in the differential evolution approach to assign the tasks, to appropriate machine and vehicle, and to minimize cycle time.     

基于遗传算法和代理的集成调度系统框架

基于多代理系统分布式调度和集中式调度的优点,提出了将二者有机结合的系统框架,分为总体调度和多代理调度网络。前者采用遗传算法优化筛选不同的调度方案,后者则由加工资源代理和协作协议构成的代理网络,保证调度对动态制造环境的适应性。每个生产订单优先级编码为一个染色体基因,每个染色体代表一个调度方案,由多代理调度网络按照订单优先级和调度协议安排工序加工时间,然后采用提前／拖期惩罚计算染色体适值,并以此为基础优选出较优染色体,经过若干代后得到最终调度方案（订单优先级和工序时间）。最后,给出了调度实例,验证了该框架的可行性。     

知识化制造环境下模糊调度模型和算法

为解决实际生产中一些不精确调度知识的描述问题,通过引入模糊理论中的测度概念,建立了模糊调度模型和求解该模型的混合模糊遗传算法.首先,在变速并行机生产环境下,针对工件加工时间和交货期的不确定性,基于可能性测度和必然性测度的定义,提出了工件拖期可信度指标,用于衡量工件发生拖期的可能性;然后,基于工件拖期可信度指标,建立了以最小化工件平均拖期可信度为目标的混合整数规划模型,通过分析该调度模型,得到最优模糊调度的相关性质;最后,以上述工作为基础,给出一种混合模糊遗传算法的求解方法,并以某电机制造企业为例,对所提出的算法进行了有效性验证.     

Clustering algorithms to optimize the tool handling system management in an FMS

Tool management is recognized as a critical issue in flexible manufacturing facilities management. This article addresses the issue of tool management in a flexible system installed in an avionics components factory. The system is composed of two machining centers equipped with local tool magazines of limited capacity. A tool handling system is in charge of tool movements between the tool room and the two machines. Each machine is able to perform any operation, provided that it is equipped with the suitable tool. In this kind of installation, tool allocation must be determined, and tool movements must be synchronized in order to minimize operating costs, or, equivalently, maximize the productivity of the system. We propose an approach to production planning based on a clustering algorithm, which takes into account the tool requirements of each part program in the production batch. We also propose two different heuristics for the scheduling problem. A case study was conducted on the facility mentioned above. Two conflicting objectives can be identified for this kind of production system: the reduction of tools to be shared among machines and the reduction of workload unbalance. The tests and comparison made demonstrate how the proposed procedure leads to superior results in terms of both objectives.     

基于改进遗传算法的 AGVS 单双向混合路径规划

针对自动导引车系统路径规划问题,首先提出了一种新的路径网络模型,即单双向混合路径网络布局。然后在仔细分析该种路径布局的特征和优势的基础上,使用改进的遗传算法实现其路径网络的规划,并详细描述了算法步骤。最后,通过对两个自动搬运系统进行路径规划、系统建模、系统仿真和对比分析,验证了单双向混合路径网络布局的优越性和可行性。     

求解Job-shop调度问题的混合遗传算法

分析了单件生产车间调度问题,提出了适合Job-shop调度的算法-混合遗传算法.通过分析几种求解该问题的典型混合遗传算法,说明了混合遗传算法是求解该问题的可行且有效的方法,并且在具体的环境下有一定的优越性。     

遗传算法在车间作业排序中的实现

描述了一种应用于车间作业排序的遗传算法的实现,该算法借助于对链表的灵活运用,使整体的运算时间得到提高,特别是对适应值的计算方式,既减轻遗传操作时合法性效验的负担,又模拟了加工过程,从而得到非常直观的运算结果.     

运用带有记忆库的遗传算法求解作业车间调度问题

在遗传算法的基础上,提出了一种带有记忆库的遗传算法,用于求解生产调度问题。该算法通过轮换的方法,分析了记忆库充满后如何更新和识别相同个体的问题,从而达到将加工任务分配到不同的并行机器上去执行,以利于机器的负载平衡。仿真结果表明,运用带有记忆库的遗传算法不但使整个加工过程的执行时间得到优化,而且各类机器完成的操作数相同、使用的时间也较为平均,达到了设计目标。同时,该算法的计算速度较快,话用干较大规模作业车间调度问题的求解。     

禁忌-并行遗传算法在作业车间调度中的应用

根据并行遗传算法与禁忌搜索算法的优点,提出了一种禁忌一并行遗传算法。该算法能够避免早熟,不仅保证了算法的全局收敛,而且提高了算法收敛速度和解的质量,特别是在求解大规模问题上具有优势。仿真结果证明了该算法的有效性。