柔性生产中基于人员任务匹配度的人员调度

文章针对单件小批量生产的柔性生产方式,考虑操作人员技能的多样性和对于不同任务技能水平的差异性等特点,根据操作人员对各任务的累计工作时间计算任务人员之间的匹配度,在此基础上建立柔性生产中资源受限下人员调度数学模型;通过启发式算法得到模型的初始解,并运用改进的遗传算法对模型进行求解,得到最优的人员调度方案以实现任务总完成时间最小的优化目标;最后结合具体实例验证了该求解模型的准确性与可行性。     

基于禁忌搜索的柔性作业车间分批调度

针对柔性作业车间的分批调度问题,提出了一种基于禁忌搜索算法的柔性分批调度算法.首先进行批次初始划分;然后利用禁忌搜索算法优化批次加工路线和子批加工顺序;最后在零件子批数不变的前提下,利用批量变化局部搜索进行批量分割的优化,实现了最小化完工时间和机床负荷的调度目标.实例分析结果证明了柔性分批调度算法的可行性、高效性和适用性.
     

Associating Memory Through Case-Based Immune Mechanisms for Dynamic Job-Shop Scheduling

Knowledge plays an active role in job-shop scheduling, especially in dynamic environments. A novel case-based immune framework was developed for static and dynamic job-shop problems, using the associative memory and knowledge reuse from case-based reasoning (CBR) and immune response mechanisms. A 2-level similarity index which combines both job routing and problem solution characteristics based on DNA matching ideas was defined for both the CBR and immune algorithms. A CBR-embedded immune algorithms (CBR-IAs) framework was then developed focusing on case retrieval and adaptation methods. In static environments, the CBR-IAs have excellent population diversity and fast convergence which are necessary for dynamic problems with jobs arriving and leaving continually. The results with dynamic scheduling problems further confirm the CBR-IAs effectiveness as a problem solving method with knowledge reuse.     

基于目标级联法的作业车间调度的研究

作业车间调度是一种典型的组合优化问题，大规模的调度优化是该类问题的难点。本文针对求解大规模的车间调度问题的需要，基于层次性优化的目标级联法，建立一种带层次性的车间调度模型，并以由两个零件族制造单元组成的车间调度为例，验证所建立的车间调度模型。研究表明，基于目标级联法的车间调度模型为大规模车间分层调度问题解决提供一种有效方法，而且能根据加工时间和工件数量动态合理地分配各制造单元的机器数量和工件的加工路径。     

New and “Stronger” Job-Shop Neighbourhoods: A Focus on the Method of Nowicki and Smutnicki (1996)

Examination of the job-shop scheduling literature uncovers a striking trend. As methods for the deterministic job-shop problem have gradually improved over the years, they have come to rely on neighbourhoods for selecting moves that are more and more constrained. We document this phenomenon by focusing on the approach of Nowicki and Smutnicki (Management Science, 1996, 42(6), 797–813), noted for proposing and implementing the most restrictive neighbourhood in the literature. The Nowicki and Smutnicki (NS) method which exploits its neighbourhood by a tabu search strategy, is widely recognised as the most effective procedure for obtaining high quality solutions in a relatively short time. Accordingly, we analyse the contribution of the method's neighbourhood structure to its overall effectiveness. Our findings show, surprisingly, that the NS neighbourhood causes the method's choice of an initialisation procedure to have an important influence on the best solution the method is able to find. By contrast, the method's choice of a strategy to generate a critical path has a negligible influence. Empirical testing further discloses that over 99.7% of the moves chosen from this neighborhood (by the NS rules) are disimproving—regardless of the initial solution procedure or the critical path generation procedure employed. We discuss implications of these findings for developing new and more effective job-shop algorithms.     

Heuristic Control of a Constraint-Based Algorithm for the Preemptive Job-Shop Scheduling Problem

In the recent years, constraint programming has been applied to a wide variety of academic and industrial non-preemptive scheduling problems, i.e., problems in which activities cannot be interrupted. In comparison, preemptive scheduling problems have received almost no attention from both the Operations Research and the Artificial Intelligence community. Motivated by the needs of a specific application, we engaged in a study of the applicability of constraint programming techniques to preemptive scheduling problems. This paper presents the algorithms we developed and the results we obtained on the preemptive variant of the famous job-shop scheduling problem. Ten heuristic search strategies, combined with two different constraint propagation techniques, are presented, and compared using two well-known series of job-shop scheduling instances from the literature. The best combination, which relies on limited discrepancy search and on edge-finding techniques, is shown to provide excellent solutions to the preemptive job-shop scheduling problem. A mean relative distance to the optimal solution of 0.32% is achieved in five minutes, on instances with 10 jobs and 10 machines (100 activities).     

求解作业车间调度问题的一种自适应遗传算法

针对作业车间调度问题，提出了最小化空闲时间的处理过程及其变异算子，设计了一种自适应遗传算法．该算法根据个体的特征确定交叉和变异次数，并根据种群特征不断修正种群．经典的调度基准问题测试表明：自适应措施能够有效保持种群的多样性，可以采用非常小的种群规模；最小化空闲时间的变异算子缩小了算法的搜索空间，大大提高了搜索效率．     

基于多Agent的柔性作业车间调度研究

针对多目标柔性作业车间调度问题,构造了基于多Agent的车间调度模型,研究了多Agent之间的交换协调机制．提出一种改进遗传算法并封装在策略Agent中,通过对编码规则、解码算法与交叉、变异算子进行改进,提高了调度优化算法的实用性和优化效果,实现了多目标动态调度,提高了系统的适应性和健壮性．某企业车间应用实例证明其可以满足车间调度高效、稳定的要求．     

周期列车运行图的多目标模型及基于Job-shop的遗传算法研究

计算机编制客运专线周期列车运行图问题已成为国内外研究的热点问题之一.在充分研究国内外周期与非周期列车运行图的规划理论与方法的基础上,构建了我国客运专线周期列车运行图的多目标模型,并将之转化为具有优先级结构的单目标模型;然后结合运行图的数学本质与周期性,设计了基于Job-shop的遗传算法,弥补了国外基于PESP理论所开发的周期列车运行图的算法不足.最后结合京津客运专线实例来验证算法的有效性.