双资源多目标车间调度算法研究

针对双资源约束的车间调度问题,考虑机器和操作工人两种生产资源对各种目标的约束影响,提出一种基于遗传算法和禁忌搜索算法的混合调度算法,使用多目标决策理论,使生产周期、工件总延误时间、设备闲置时间、人员闲置时间的综合指标值为最小,得到多目标的最优解或次优解.最后对算法进行试验,试验结果证明该算法可行,具有很好的搜索性能和效率.     

多目标批量生产柔性作业车间优化调度

研究批量生产中以生产周期、最大提前/最大拖后时间、生产成本以及设备利用率指标(机床总负荷和机床最大负荷)为调度目标的柔性作业车间优化调度问题。提出批量生产优化调度策略,建立多目标优化调度模型,结合多种群粒子群搜索与遗传算法的优点提出具有倾向性粒子群搜索的多种群混合算法,以提高搜索效率和搜索质量。仿真结果表明,该模型及算法较目前国内外现有方法更为有效和合理。最后,从现实生产实际出发给出多目标批量生产柔性调度算例,结果可行,可对生产实践起到一定的指导作用。     

工艺路线可变的双资源双目标车间调度优化

将遗传算法与启发式调度规则相结合 ,研究了工艺路线可变的双资源双目标的作业车间调度优化问题。在探讨过程中 ,不仅考虑到了每个工件有几条可行的工艺路线 ,而且考虑到了工件的调度受到机床、工人等资源的制约 ,以及在加工过程中发生的储存费用、机床的加工费用和工人的劳动费用对工件调度的影响 ,设计了以生产周期和生产成本综合优化为目标的适应度函数。启发式调度规则使该算法具有较高的局部搜索效率 ,遗传算法保证了解的全局最优性。最后给出了算例 ,并对计算结果进行了分析和讨论     

基于瓶颈工序的多资源多目标机械加工车间调度研究

为求解多资源约束的机械加工车间调度问题,建立了包括最大完工时间、平均流经时间、总拖期惩罚和生产成本在内的多目标优化模型,并结合免疫遗传算法和约束理论提出了一种基于瓶颈工序的机械加工车间调度算法。算法依据约束理论提出了一种基于工序的多参数级联编码方法和基于鼓-缓冲器-绳索(DBR)的四阶段解码方法,以及有效的交叉、变异操作。基于瓶颈工序的免疫操作及基于浓度的选择更新机制,保证了多目标优化问题的收敛性以及Pareto解集的多样性。仿真结果表明了该算法的可行性和有效性。     

单元型制造车间的双资源调度研究

研究了单元型制造车间的双资源调度.针对新到达的工件,首先将其分配到对应的加工单元上,或构建一虚拟加工单元,并采用四轮分配法为每道工序分配设备,采用遗传算法进行加工排序后,再通过四轮分配法为已分配任务的设备分配工人.给出了调度系统的结构及一个双资源调度的实例.     

求解多目标作业车间调度问题的双种群遗传算法

针对多目标作业车间调度问题,提出一种将正逆序调度方法与生成调度活动的遗传算法相结合的双种群遗传算法.该算法利用活动调度缩减解空间,提出采用正、逆序遗传调度算法分别在不同种群优化不同目标函数,将多目标问题分解成多个单目标问题.在进化过程中,通过个体迁移算子加快多个目标的并行搜索,并提出了一种构造Pareto解集的精英锦标赛法则.通过基于Benchmark算例的仿真实验,验证了该算法求解多目标作业车间调度问题的有效性.     

双资源约束作业车间动态调度研究

针对双资源约束作业车间动态调度优化问题的求解特点,基于局部时窗调度思想构造包含动态扰动分析、动态扰动评估以及动态调度方法在内的三层动态调度系统,并结合最小化调度方案修订的动态调度实际需求,设计基于染色体还原机制与局部工序的分阶段调度优化的混合动态调度算法,最后通过仿真实验验证了基于局部时窗调度思想的动态调度方法有效降低随机扰动影响的良好应用效果。     

多目标模糊作业车间调度问题研究

研究了具有模糊加工时间和模糊交货期的多目标作业车间调度问题,首先给出了基于模糊优先规则的编码新方式,染色体的每一位表示在GT算法迭代过程中,对应机器上发生的某次冲突,根据该基因位对应的优先规则消除。然后设计了基于个体密集距离的多目标进化算法,该算法利用密集距离进行外部档案维护和适应度赋值。最后将多目标进化算法应用于模糊作业车间调度问题,以最大化最小一致指标和最小化模糊最大完成时间,并和其他算法比较。计算结果验证了多目标进化算法在模糊调度方面良好的搜索性能。     

多工艺路线多资源多目标的作业调度优化

针对多工艺路线多加工资源多目标的作业调度问题,提出了一种启发式活动调度算法,将该方法与多目标遗传算法及模糊优选技术相结合,得到了一种新调度算法.基于工序的染色体编码方法和基于活动启发式算法的交叉算子的运用,有效地缩小了遗传算法的搜索空间.将随机产生的权系数与模糊优选技术相结合,有助于遗传算法搜索到多个优良的调度方案,这为决策者得到最满意的调度方案提供了保证.仿真结果表明该算法是可行的,与国外学者的同类研究相比,具有一定的优越性.     

An approach to process production reactive scheduling

Process manufacturing has some characteristics that make it different from other types of industry. In practice, there are many examples of process industrial settings where production resources are shared and there is no intermediate storage between two processing units. Perhaps the most important issue usually to be excluded from the academic discussion of process production scheduling/control problems is the uncertainty of the duration of real chemical processes. These properties of process manufacturing result in blocking, no-wait, and other constraints that must be taken into account during designing the production scheduling system. The first part of the paper addresses some features of process manufacturing and their influence on production scheduling. The main part of the paper analyzes the scheduling problems under the presence of typical process constraints and unpredictable process duration times and introduces a simple and reliable approach to avoid these problems. The proposed concept is presented on a case study of an industrial two-stage washing batch subprocess. It is considered that the presented approach would be relatively easy to implement in a process production scheduling system and that it can contribute to more effective process production scheduling.     

A hybrid genetic algorithm to minimize makespan for the single batch machine dynamic scheduling problem

This paper considers a single batch machine dynamic scheduling problem, which is readily found in the burn-in operation of semiconductor manufacturing. The batch machine can process several jobs as a batch simultaneously, within the capacity limit of the machine, and the processing time is represented by the longest processing time among all jobs in a batch. For a single batch machine problem with arbitrary job release time, we proposed an improved algorithm (merge-split procedure) to refine the solution obtained by the LPT-BFF heuristic, and two versions of a hybrid genetic algorithm (GA) are introduced in this paper. Each version of the hybrid GA diversifies job sequences using the GA operators in stage 1, forms batches in stage 2, and finally sequence the batches in stage 3. The difference is that merge-split procedures are involved in the second version of the hybrid GA. Computational experiments showed that the hybrid GA would obtain satisfactory average solution quality and the merge-split procedures would be good at reinforcing the solution consistency of the hybrid GA.     

Integrated optimization of production planning and scheduling for a kind of job-shop

This paper addresses an integrated job-shop production planning and scheduling problem with setup time and batches. It not only considers the setup cost, work-in-process inventory, product demand, and the load of equipment, but also the detailed scheduling constraints. That is a way different from the traditional hierarchical production planning method. The hierarchical methods do not consider the detailed scheduling constraints, so it cannot guarantee to obtain a feasible production plan. Here the integrated problem is formulated as a nonlinear mixed integer program model. And in order to simultaneously optimize the production plan and the schedule, an improved hybrid genetic algorithm (HGA) is given. In the model, the detailed scheduling constraints are used to compute the accurate load of a device in order to obtain a feasible production plan. The heuristic scheduling rules such as the shortest processing time (SPT) and the longest processing time (LPT) are used to generate a better initial solution. Also, a subsection coding strategy is offered to convert the planning and scheduling solution into a chromosome. At last, a comparison is made between the hybrid algorithm and a hierarchical production planning and scheduling method, showing that the hybrid algorithm can solve the problem effectively.     

基于多阶段批调度的混合型生产过程的建模与优化

根据混合型生产过程生产方式复杂的特点,采取分阶段的批调度方法建立模型,并且在模型中充分考虑了中间缓冲区的库存容量、生产线的加工容量以及客户的交货期等约束条件,在此基础上达到最低生产成本和最短的加工时间的多重调度目标。同时构造了基于该模型的启发式算法,降低运算速率、提高结果的准确度,从而使得该调度模型以能够得到最优的生产调度方案。实例仿真结果表明了该模型的可行性,同时为制造企业提供了一种有效的决策手段。     

An integrated decision support system for FMS production planning and scheduling problems

This paper discusses planning and scheduling problems for efficient use of an FMS and presents an integrated decision support system for dealing with these problems.The decision support system, FMSDS (Flexible Manufacturing Systems Decision Support System), includes several modules: data handling module, part selection module, loading module, load adjusting module, scheduling module and simulation module.This paper suggests the solution methodology of each subproblem. Also, an integrated interface scheme between the subproblems is presented. The interface scheme considers the relationships between the subproblems and generates solutions using hierarchical and looping approaches.FMSDS is made up of six alternative models considering three loading objectives and two production order processing strategies. Performance comparison among the six alternatives is shown using the non-terminating simulation techniques.     

车间生产调度系统的虚拟仿真研究

阐述车间生产调度问题及其特点,详细分析仿真调度的原理和特点;并结合车间调度的具体情况,提出一套车间生产调度系统虚拟仿真的解决方案.     

Design and implementation of an automatic material handling system in a batch production environment

This paper presents the problem definition and the related research activities on the modeling, simulation, design and implementation of an automatic material handling system in a batch production environment. The colored and timed Petri nets are used to model and simulate the automatic material handling system. The Petri net modeling and simulation enable the determination of the design parameters of an automatic guided vehicle and an automatic storage/ retrieval system, and also the estimation of the expected performance capability of the whole system. Finally, the whole system has been implemented for a specific batch production line. The comparison of the performance capability expected by the Petri net simulation and that measured from the real-time running of the whole system shows a good agreement.     

车间批量生产调度及基于OpenGL的仿真研究

针对作业车间批量生产的调度及仿真问题,根据等分批原则,建立分批调度的数学模型,之后把分好的子批看成一个新的工件,对其重新进行编号并计算新的加工时间,利用遗传算法良好的全局搜索性能和禁忌搜索算法优良的局部搜索性能,引入混合优化算法进行生产调度;然后利用OpenGL建立生产车间以及加工设备的三维模型,并赋予场景初始化、设备初始化功能和视点移动、模型整体变换功能;最后利用分批调度计算得出的最终结果,根据工件中每个工序的开始加工时间和加工消耗时间,设置触发器和定时器,并通过读取相应的加工设备编号确定加工的位置坐标,进行实时地调度仿真。     

生产排程与模型构建研究

生产排程是生产运作管理的基本问题，通过仿真等方法可以编排和优化生产排程，提高及时交货率和设备利用效率。首先分析生产排程的基本概念，生产排程的三个子过程，以及生产排程问题的解决方法。然后讨论了生产排程涉及到的主要因素，并使用面向对象技术分析订单类、设备类、人员类、存储类、工件类等模型构建的主要因素，最后给出了生产排程的UML静态结构图，为设计生产排程软件奠定了基础。