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.     

Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm

The increased use of flexible manufacturing systems (FMS) to efficiently provide customers with diversified products has created a significant set of operational challenges. Although extensive research has been conducted on design and operational problems of automated manufacturing systems, many problems remain unsolved. In particular, the scheduling task, the control problem during the operation, is of importance owing to the dynamic nature of the FMS such as flexible parts, tools and automated guided vehicle (AGV) routings. The FMS scheduling problem has been tackled by various traditional optimisation techniques. While these methods can give an optimal solution to small-scale problems, they are often inefficient when applied to larger-scale problems. In this work, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple objectives, i.e., minimising the idle time of the machine and minimising the total penalty cost for not meeting the deadline concurrently. The memetic algorithm presented here is essentially a genetic algorithm with an element of simulated annealing. The results of the different optimisation algorithms (memetic algorithm, genetic algorithm, simulated annealing, and particle swarm algorithm) are compared and conclusions are presented .     

可重构装配线多目标优化调度研究

针对可重构装配线调度存在的问题,综合考虑影响可重构装配线调度的三个主要因素,即最小化空闲和未完工作业量、均衡零部件的使用速率、最小化装配线重构成本,建立了可重构装配线多目标优化调度的数学模型。提出了一种基于Pareto多目标遗传算法的可重构装配线优化调度方法,该算法综合运用了群体排序技术、小生境技术、Pareto解集过滤及精英保留策略,并采用了交叉概率和变异概率的自适应重构策略。实例仿真表明该算法具有比其他遗传算法更高的求解质量。

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Scheduling optimization of flexible manufacturing system using cuckoo search-based approach

In the present work, a cuckoo search (CS)-based approach has been developed for scheduling optimization of a flexible manufacturing system by minimizing the penalty cost due to delay in manufacturing and maximizing the machine utilization time. To demonstrate the application of cuckoo search (CS)-based scheme to find the optimum job, the proposed scheme has been applied with slight modification in its Levy flight operator because of the discrete nature of the solution on a standard FMS scheduling problem containing 43 jobs and 16 machines taken from literature. The CS scheme has been implemented using Matlab, and results have been compared with other soft computing-based optimization approaches like genetic algorithm (GA) and particle swarm optimization found in the literature. The results shown by CS-based approach have been found to outperform the results of existing heuristic algorithms such as GA for the given problem.     

柔性作业车间多自动导引小车和机器的集成调度

针对含有AGV的柔性作业车间调度问题,提出基于时间窗和Dijkstra算法的混合遗传算法。建立了AGV/机器的双资源调度数学模型;采用3种解决策略处理多AGV路径规划冲突和碰撞;为了将机器和AGV调度集成考虑,设计了三链式编码结构及AGV编码链的交叉、变异算子,同时在遗传算法的解码操作中将Dijkstra算法与时间窗原理相结合,以精确地为任务小车规划出一条无碰撞无冲突的最短路径;算例对比验证了该算法的可行性、有效性和优越性。     

Taboo search algorithm for item assignment in synchronized zone automated order picking system

The idle time which is part of the order fulfillment time is decided by the number of items in the zone; therefore the item assignment method affects the picking efficiency. Whereas previous studies only focus on the balance of number of kinds of items between different zones but not the number of items and the idle time in each zone. In this paper, an idle factor is proposed to measure the idle time exactly. The idle factor is proven to obey the same vary trend with the idle time, so the object of this problem can be simplified from minimizing idle time to minimizing idle factor. Based on this, the model of item assignment problem in synchronized zone automated order picking system is built. The model is a form of relaxation of parallel machine scheduling problem which had been proven to be NP-complete. To solve the model, a taboo search algorithm is proposed. The main idea of the algorithm is minimizing the greatest idle factor of zones with the 2-exchange algorithm. Finally, the simulation which applies the data collected from a tobacco distribution center is conducted to evaluate the performance of the algorithm. The result verifies the model and shows the algorithm can do a steady work to reduce idle time and the idle time can be reduced by 45.63% on average. This research proposed an approach to measure the idle time in synchronized zone automated order picking system. The approach can improve the picking efficiency significantly and can be seen as theoretical basis when optimizing the synchronized automated order picking systems.     

车间调度问题及其进化算法分析

为了研究车间调度问题,分析调度过程和调度结果,提出最小化空闲时间处理过程和不同空闲时间处理顺序规则。根据最小化空闲时间处理过程,设计进化算法的初始种群生成过程、重组算子和变异算子。为保持种群的多样性,在选择算子中引入广义海明距离,在总体流程中加入种群修正过程。经典的调度基准问题试验表明：最小化空闲时间处理过程高效可靠;进化算法能缩小搜索空间、提高搜索效率和避免早熟收敛现象,稳定可靠。     

基于改进遗传算法的流水车间调度研究与仿真

针对流水车间调度过程中的物料流程混乱、设备负荷不均衡的问题,用矩阵表示工件、工序和机器之间的约束关系,通过引入线性自适应算子对交叉算子和变异算子加以改进,对经典流水车间调度问题Car1(11×5)进行了改进遗传算法实现,此外在考虑传输过程的情况下,对该典型问题的加工过程进行了仿真分析。     

A new parallel genetic algorithm for solving multiobjective scheduling problems subjected to special process constraint

This study presents a multiobjective scheduling model on parallel machines (MOSP). Compared with other scheduling problems on parallel machines, the MOSP is distinct for the following characteristics: (1) parallel machines are nonidentical, (2) the type of jobs processed on each machine can be restricted, and (3) the multiobjective scheduling problem includes minimizing the maximum completion time among all the machines (makespan) and minimizing the total earliness/tardiness penalty of all the jobs. To solve the MOSP, a new parallel genetic algorithm (PIGA) based on the vector group encoding method and the immune method is proposed. For PIGA, its three distinct characteristics are as follows: Firstly, individuals are represented by a vector group, which can effectively reflect the virtual scheduling policy. Secondly, an immune operator is adopted and studied in order to guarantee diversity of the population. Finally, a local search algorithm is applied to improve the quality of the population. Numerical results show that it is efficient, can better overcome drawbacks of the general genetic algorithm, and has better parallelism.     

Simultaneous minimization of total tardiness and waiting time variance on a single machine by genetic algorithms

This paper considers a single machine scheduling problem, with the objective of minimizing a linear combination of total tardiness and waiting time variance in which the idle time is not allowed. Minimizing total tardiness is always regarded as one of the most significant performance criteria in practical systems to avoid penalty costs of tardiness, and waiting time variance is an important criterion in establishing quality of service (QoS) in many systems. Each of these criteria is known to be non-deterministic polynomial-time hard (NP-hard); therefore, the linear combination of them is NP-hard too. For this problem, we developed a genetic algorithm (GA) by applying its general structure that further improves the initial population, utilizing some of heuristic algorithms. The GA is shown experimentally to perform well by testing on various instances.     

柔性加工环境中机器和AGV的集成调度

研究由几台加工中心（WS）和一台（多台）自动导向小车（AGV）组成的柔性制造系统（FMS）的调度问题,描述了采用AGV的FMS高度的特点,建立了可变工艺路径、包含AGV的FMS调度问题的模型,形成一种新的基于有向图的可变路径表示方法,采用结合启发式规则优点的遗传算法（GA）同时调度机器和AGV,使得AGV调度成为FMS集成环境下调度的一部分,提出了面向可变路径特征的交叉算子,采用自适应的交叉、变异策略和灵活的群体控制策略,对几个调度实例进行了计算。     

基于遗传算法的柔性制造系统机床选择问题研究

由于激烈的市场竞争环境,灵活的制造商应该以最快的速度向市场推出产品,以最少的成本进行生产,从而拥有使消费者满意的巨大能力.而具有快速时间响应和高度柔性的制造系统是必要的.在假设的条件下,构建基于零件加工时间和成本加权和为目标的柔性制造系统机床选择数学模型,在模型中考虑机床的维修成本.用C语言实现遗传算法在柔性制造系统机床选择中的应用,并与以前的例子进行比较.最后通过实验对遗传算法的参数进行分析.     

A genetic-algorithm-based optimization model for scheduling flexible assembly lines

In this paper, a scheduling problem in the flexible assembly line (FAL) is investigated. The mathematical model for this problem is presented with the objectives of minimizing the weighted sum of tardiness and earliness penalties and balancing the production flow of the FAL, which considers flexible operation assignments. A bi-level genetic algorithm is developed to solve the scheduling problem. In this algorithm, a new chromosome representation is presented to tackle the operation assignment by assigning one operation to multiple machines as well as assigning multiple operations to one machine. Furthermore, a heuristic initialization process and modified genetic operators are proposed. The proposed optimization algorithm is validated using two sets of real production data. Experimental results demonstrate that the proposed optimization model can solve the scheduling problem effectively.     

自动化分拣仓库中多AGV调度与路径规划算法

自动化分拣仓库由多自动导引小车(AGV)同时作业,对大量包裹进行快速分拣。如何为AGV确定搬运包裹序列并规划无冲突的路径,是分拣作业的关键所在。为提高分拣效率,以最小化最大搬运完成时间为目标,定义了冲突AGV的优先级,提出一种生成无路径冲突的路径规划算法;进而,综合考虑AGV调度和路径规划,提出一种改进差分进化算法,算法采用反学习方法生成初始种群,运用自适应的变异和交叉概率进行进化操作,设计动态差分进化策略来提高收敛速度,并设计交换邻域和基于关键AGV的插入邻域进行局部搜索。通过数据实验验证了算法的有效性,并对关键问题参数进行了分析。     

面向航空发动机装配线的知识化制造自适应优化调度

为了解决不确定生产环境下的航空发动机装配调度问题,设计了一种面向航空发动机装配线的知识化制造自适应优化调度算法。算法采用强化学习和过程仿真相结合的调度策略求解方式,以最小化提前期惩罚费用和完工时间成本为调度目标,给出了航空发动机装配的Q学习自适应调度模型;针对装配调度问题定义了四个新的调度规则,定义了航空发动机装配的四个状态特征用于对系统状态进行描述,并针对调度目标设计了合理的回报函数。仿真实验结果表明,在调度过程中,采用提出的Q学习方法在多数情况下都远优于其他规则,尤其在装配任务到达频繁的情况下,总体上表现出更好的优势,显示了良好的自适应性能。     

A Genetic Algorithm Approach to the Scheduling of FMSs with Multiple Routes

Usually, most of the typical job shop scheduling approaches deal with the processing sequence of parts in a fixed routing condition. In this paper, we suggest a genetic algorithm (GA) to solve the job-sequencing problem for a production shop that is characterized by flexible routing and flexible machines. This means that all parts, of all part types, can be processed through alternative routings. Also, there can be several machines for each machine type. To solve these general scheduling problems, a genetic algorithm approach is proposed and the concepts of virtual and real operations are introduced. Chromosome coding and genetic operators of GAs are defined during the problem solving. A minimum weighted tardiness objective function is used to define code fitness, which is used for selecting species and producing a new generation of codes. Finally, several experimental results are given.     

基于工件聚类的并行机调度算法及其应用

针对纺织生产广泛存在的带工件释放时间、以最小化总拖期工件数和总拖期时间为目标的大规模并行机调度问题,提出一种基于工件聚类的遗传算法。该算法将求解过程分为工件聚类和工件排序两个阶段。在工件聚类阶段,基于影响并行机调度性能的重要调度特征量,采用改进的模糊C-均值聚类方法将所有待上机工件分为多个聚类;在工件排序阶段,采用基于规则编码的遗传算法,优化各聚类内工件的加工顺序。数值计算结果及实际应用效果表明,所提出的算法适用于求解带工件释放时间的大规模并行机调度问题。     

Multi-objective integrated optimization research on preventive maintenance planning and production scheduling for a single machine

Preventive maintenance (PM) planning and production scheduling are among the most important problems in the manufacturing industries. Researchers have begun to investigate the integrated optimization problem of PM and production scheduling with a single objective. However, many industries have trade-offs in their scheduling problems where multiple objectives must be considered in order to optimize the overall performance of the system. In this paper, five objectives, including minimizing maintenance cost, makespan, total weighted completion time of jobs, total weighted tardiness, and maximizing machine availability are simultaneously considered to optimize the integrated problem of PM and production scheduling introduced by Cassady and Kutanoglu. Multi-objective genetic algorithm (MOGA) is used to solve the integrated optimization problem. To illuminate the conflicting nature of the objective functions, decision-makers’ preferences of the multiple objectives are not integrated into the MOGA. The total weighted percent deviation, which represents not only the preferences of the objectives but also the deviations of the solutions, is proposed to help decision-makers select the best solution among the near-Pareto optimal solutions obtained by the MOGA. A numerical example reveals the necessity and significance of integrating optimization of PM and production scheduling considering multiple objectives.     

流水车间作业提前/拖期调度问题研究

在非正规性能指标提前／拖期调度问题中,工件的加工顺序和每个加工活动的开始时刻都属于需要优化的变量,增加了求解的难度。针对这一问题,提出了采用分层调度模式求解流水车间提前／拖期调度问题的联合算法。首先,采用遗传算法对加工顺序进行寻优;其次,在给定调度序列的情况下采用启发式算法对加工开始时刻进行优化,制定插入机器空闲时段的策略,确定何时插入空闲时段和空闲时段的大小,即在给定顺序下确定工件加工活动的开始时刻,以满足在加工完所有工件后,使提前惩罚费用与拖期惩罚费用之和最小。数值计算结果证明了该联合算法的有效性。     

基于生物遗传算法的FMS生产调度算法

根据遗传算法,提出了一种FMS生产调度的新算法,该算法不仅适于FMS的静态调度问题,而且由于其计算复杂性低、计算量少的特点,同样也适于FMS的动态调度问题,有效地为解决自动化生产系统的生产调度问题提供了新方法.