棉纺织企业多目标优化计划调度系统开发

讨论了多目标优化生产调度计划系统的结构功能和部分关键功能的设计，分析了该系统如何把多目标优化启发式算法、自定义式机台分布图及GANTT图仿真模型相结合，实现生产调度计划的优化编制、调度单的自动生成和实时跟单以及生产计划的合理制定。     

流程工业拟实优化调度系统

流程工业生产调度是典型的多工序、多约束、多目标的调度问题,实现优化调度非常复杂。在分析调度建模问题的基础上,提出了拟实优化调度思想和流程工业拟实优化调度原理。给出了流程工业拟实优化调度系统框架及其系统分析设计方法。实现了基于可视化生产系统建模、优化求解、过程模拟、过程监控等为一体的流程工业优化调度。在制药企业的实际应用表明,该系统建模简单、提高了生产调度能力和生产效率。     

基于新型分布式算法的批量流水线调度方法研究

在企业生产经营活动中,生产计划是最重要的依据,而生产计划是由调度系统来实施完成的,批量流水线调度问题是一个合理分配资源的过程,从而达到优化一个或多个目标的目的。优化的批量流水线调度方案,能提高企业的生产效率,并在一定程度上降低生产成本,目前的批量流水线调度方案存在一定问题,笔者提出基于新型分布式算法的批量流水线调度方法。     

基于TSAPO的柔性作业车间计划和调度

为使多目标柔性作业车间计划与调度的制定更适合实际生产的动态变化,提出增加动态反馈的闭环柔性作业车间计划模型及二阶式蚁群粒子群混合优化算法TSAPO。通过增加动态监视功能,及时更新和反馈实际生产数据。利用对优化目标的二阶段分解,设计带有反馈机制的调度算法。实验结果证明,该算法在求解多目标柔性作业车间调度问题中具有较好的优化效果。     

汽车装配车间生产计划调度系统的设计与开发

论述了汽车装配车间在企业顺利生产中的重要性,建立了车间生产计划与调度的同时优化模型。基于免疫算法以概率1收敛的特点,选用免疫算法并将之进行适当组合得到交替式免疫算法来对该模型进行求解,以达到计划和调度同时优化的目的。在进行理论研究的基础上,根据南京某汽车厂的实际需求设计出了生产计划与调度系统方案,并使用组件化技术,开发出了基于．NET平台和B／S模式的汽车装配车间生产计划与调度系统,最终满足实际生产要求,达到了高效信息管理的目标。     

混合算法在钢构企业生产调度系统中的应用

针对钢构企业生产调度约束复杂、寻优困难等问题,提出了多目标生产调度的生产调度模型,并利用蚁群遗传混合算法求解生产调度计划。以某钢结构企业设计实现的生产管理系统为依托,研究了生产调度系统的执行情况,通过不同方法进行模拟计算和比较,验证了该多目标生产调度模型的解可节省生产时间,从而指导钢结构生产以提高效率。     

一种炼钢–连铸生产计划一体化编制方法

炼钢和连铸生产调度是钢铁企业生产调度的重要内容而且二者密切相关.目前采用的调度方法多是分别编制炼钢炉次计划和连铸浇次计划,还没有协调处理二者的有效办法.提出了一种炼钢-连铸生产计划的一体化编制方法.首先通过求解炼钢炉次计划编制的多目标优化模型得到炉次计划的多个候选方案,在此基础上通过求解以最小化计划数为目标的连铸浇次计划数学模型得到浇次计划,最后根据浇次计划从炉次计划的候选方案中确定炉次计划.求解过程中使用了多目标模拟退火算法和改进变邻域搜索算法.基于实际生产数据的仿真试验表明了所提方法的有效性.     

流程工业计划调度技术研究与发展分析

计划调度系统是企业生产活动的组织和管理中心,是提高企业综合效益的有效途径,该文分析了流程工业计划调度系统特征,全面深入地研究了生产计划与调度问题的模型框架及求解技术,并针对目前流程工业计划调度研究中存在的问题,从系统结构、建模优化、智能求解等方面提出了流程企业计划调度技术的发展趋势,为流程企业的生产优化组织与运作提供了方法论的指导。     

汽车装配车间生产计划调度系统的设计与开发

论述了汽车装配车间在企业顺利生产中的重要性,建立了车间生产计划与调度的同时优化模型.基于免疫算法.以概率1收敛的特点,选用免疫算法并将之进行适当组合得到交替式免疫算法来对该模型进行求解,以达到计划和调度同时优化的目的.在进行理论研究的基础上,根据南京某汽车厂的实际需求设计出了生产计划与调度系统方案,并使用组件化技术,开发出了基于.NET平台和B/S模式的汽车装配车间生产计划与调度系统,最终满足实际生产要求,达到了高效信息管理的目标.     

基于优先数的智能生产调度系统

生产调度是企业生产中的关键环节,也是实施CIMS的重点。该文介绍了一种基于优先数的动态、多目标优化生产调度方法,并构造了一个基于优先数的智能生产调度系统。系统通过Agent对生产调度问题中的各个目标优化,给出不同目标的指标值,同时给出企业在不同时候对不同目标的偏好值,计算各个生产任务的优先数,根据任务的优先数安排生产。     

基于改进型批决策与调度建模的生产计划调度系统的研究

热轧型钢生产工艺复杂,其生产中极易出现由于计划调度安排不当而产生的交货期延误、库存超负荷等问题。针对以上问题研究设计了MES生产计划调度系统,改进了批决策调度策略用于数学建模,利用自适应遗传算法求解生产调度计划。以此为基础,为某热轧企业设计实现了生产计划调度系统,并通过真实的热轧型钢订单、原料、设备等数据,对模型改进前后的计划编制方法进行模拟与比较,验证了利用该改进型批决策与调度模型编制的热轧型钢生产调度计划可节省生产时间、降低设备调度时间,以此来指导热轧型钢的生产可切实减少交货延误和减少库存占用率,并提高企业利润率。     

轧制批量计划问题的模型及算法研究

网络建模在系统分析中是最有效的方法之一，广泛地应用于工业工程和生产调度中，应用组合优化中著名的车辆调度问题分析、研究钢生产中传统的轧制批量计划问题，提出一种新的具有优化分割功能的遗传算法，并进行了计算机仿真、仿真结果证实该方法的有效性。     

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.     

Integrating planning and scheduling

Planning and scheduling research is becoming an increasingly interesting topic in the artificial intelligence area because of its immediate application to real problems. Although the last few years have seen dramatic advances in planning systems, they have not seen the same advances in the methods of solving planning and scheduling problems. In this paper, an intuitive way of integrating independent planning and scheduling processes is presented which achieves better performance in the process of solving planning and scheduling problems. The integrated system has the advantage of obtaining a final plan that is executable and optimal. Moreover, the system discards any partial plan as soon as the plan becomes invalid, improving its performance. Experience demonstrates the validity of this system for tackling planning and scheduling problems.     

基于AGVs路径规划的物流智能调度系统设计与实现

为了解决现有物流调度系统低效缓慢、容错率低的问题,设计了基于自动导引运输车(AGV,automated guided vechicle)和路径规划优化算法的物流智能调度系统;系统搭配了AGV的物流调度硬件,又结合了路径规划理论,开发了基于Petri网络的智能路径规划算法;通过算法性能对比得知,路径规划算法设计了最优调度路径,确保了较高的准确率和工作效率;系统测试结果显示,基于AGVs路径规划的物流智能调度系统能够在各种物流环境或者库房基地完成调度任务,很好地解决了物流企业在忙碌期的繁杂调度问题;基于AGVs路径规划的物流智能调度系统提高了物流调度的自动化程度,保证了物流调度和道路运输的效率,有效推动了商业模式和市场规范的发展。     

Integration of process planning and scheduling— a review

In recent years, a few researchers have addressed the need for the integration of process planning and scheduling functions in order to achieve superior overall system performance. Many of these researchers have discovered that the potential savings are substantial when process planning and scheduling are integrated. It has been reported that typical scheduling objectives, such as minimizing makespan, maximizing equipment utilization, etc, can be significantly improved as the result of integration of these two important manufacturing system functions. In this paper, we present a review of the reported research in this area, discuss the extent of applicability of various approaches, and suggest directions for future research.     

A list-scheduling heuristic for the short-term planning of assessment centers

Many companies operate assessment centers to help them select candidates for open job positions. During the assessment process, each candidate performs a set of tasks, and the candidates are evaluated by some so-called assessors. Additional constraints such as preparation and evaluation times, actors’ participation in tasks, no-go relationships, and prescribed time windows for lunch breaks contribute to the complexity of planning such assessment processes. We propose a multi-pass list-scheduling heuristic for this novel planning problem; to this end, we develop novel procedures for devising appropriate scheduling lists and for generating a feasible schedule. The computational results for a set of example problems that represent or are derived from real cases indicate that the heuristic generates optimal or near-optimal schedules within relatively short CPU times.     

Integration of process planning and scheduling—A modified genetic algorithm-based approach

Traditionally, process planning and scheduling for parts were carried out in a sequential way, where scheduling was done after process plans had been generated. Considering the fact that the two functions are usually complementary, it is necessary to integrate them more tightly so that performance of a manufacturing system can be improved greatly. In this paper, a new integration model and a modified genetic algorithm-based approach have been developed to facilitate the integration and optimization of the two functions. In the model, process planning and scheduling functions are carried out simultaneously. In order to improve the optimized performance of the modified genetic algorithm-based approach, more efficient genetic representations and operator schemes have been developed. Experimental studies have been conducted and the comparisons have been made between this approach and others to indicate the superiority and adaptability of this method. The experimental results show that the proposed approach is a promising and very effective method for the integration of process planning and scheduling.     

Temporal reasoning for manufacturing: A hybrid representation and complexity management

Temporal considerations play a key role in the planning and operation of a manufacturing system. The development of a temporal reasoning mechanism would facilitate effective and efficient computer-aided process planning and dynamic scheduling. We feel that a temporal system that makes use of the expressive power of the integral language and the computational ease of the point language will be best suited to reasoning about time within the manufacturing system. The concept of a superinterval, or a collection of intervals, is used to augment a hybrid point-interval temporal system. We have implemented a reasoning algorithm that can be used to aid temporal decision making within the manufacturing environment. Using the quantitative results obtained by measuring our program's performance, we show how the superinterval can be used to partition large temporal systems into smaller ones to facilitate distributed processing of the smaller systems. The distributed processing of large temporal systems helps achieve real-time temporal decision-making capabilities. Such a reasoning system will facilitate automation of the planning and scheduling functions within the manufacturing environment and provide the framework for an autonomous production facility.     

Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling

Traditionally, process planning and scheduling were performed sequentially, where scheduling was implemented after process plans had been generated. Considering their complementarity, it is necessary to integrate these two functions more tightly to improve the performance of a manufacturing system greatly. In this paper, a mathematical model of integrated process planning and scheduling has been formulated. And, an evolutionary algorithm-based approach has been developed to facilitate the integration and optimization of these two functions. To improve the optimized performance of the approach, efficient genetic representation and operator schemes have been developed. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show that the integrated process planning and scheduling is necessary and the proposed approach has achieved significant improvement.