基于混合遗传算法的作业车间调度问题的研究与仿真

作业车间调度问题(JSP)是一类典型的NP-hard问题,遗传算法(GA)由于其隐合并行性和全局解空间搜索两大优点而成为解决JSP问题的常用工具.但是,由于JSP问题本身的特点,普通遗传算法难以在解此类问题时得到满意解,最突出的问题就是过早收敛于某一局部最优解,使算法效率降低.在此基础上,将遗传算法和模拟退火算法相结合,提出了一种基于遗传和模拟退火的混合算法,该算法将模拟退火算法赋予搜索过程时变性融入其中,具有明显的概率跳跃性,并选取了典型问题进行分析和仿真研究.仿真结果表明,与传统的遗传算法相比该方法是行之有效的.     

基于模拟退火遗传算法的多项目调度问题研究

针对多资源约束条件下的多项目调度问题,提出了一种模拟退火遗传算法的求解方法.该方法首先分别对普通的遗传算法和模拟退火算法进行改进,然后在遗传算法中插入模拟退火操作,通过模拟退火操作来克服遗传算法容易陷入局部最优解的缺陷,同时该方法也继承了遗传算法收敛速度快的特点.最后的实例计算结果表明该算法能克服模拟退火算法和遗传算法的缺点,获得比其它算法更优的解,与其它启发式算法及智能算法相比具有更高的求解效率.     

混合遗传算法求解配送车辆调度问题

车辆调度优化是物流配送的关键环节。针对有时间窗的车辆调度问题,综合考虑了路网中的交通状况,提出改进的车辆调度模型。并针对这个模型,设计了混合遗传算法,采用自适应策略调整交叉和变异概率,引进有效的交叉和变异算子,并结合模拟退火算法缓解遗传算法的选择压力,避免早熟收敛。仿真结果表明该算法与标准遗传算法相比有更好的性能。     

应用遗传模拟退火算法实现资源受限项目调度

针对以最小化项目工期为目标的资源受限项目调度问题（RCPSP）,提出将模拟退火算法融合到遗传算法中,以改善遗传算法局部搜索性能,增强进化能力的遗传模拟退火算法——RCPSPGSA。在每次进化迭代过程中,下一代种群的个体需经过模拟退火算法改进,并通过在每次迭代结束前进行降温操作保证遗传算法和模拟退火算法具有相同的收敛方向和速度。算法在RCPSP标准测试问题库PSPLIB上进行数值仿真实验,并采用正交实验分析法解决参数选择问题。实验结果证明选择的参数组合具有突出的性能,RCPSPGSA是求解RCPSP的有效算法。     

自适应最优保存的模拟退火遗传调度算法研究及其应用

该文对调度算法做了简单的介绍。在结合已有的模拟退火算法和遗传算法的基础上,改进了现有的遗传调度算法,自适应地保存最优个体,并对其进行模拟退火。与简单最优保存遗传调度算法进行了比较,结果表明新的算法比原有算法搜索能力更强,在跳出局部最优方面也有改进,有效地解决了原有遗传调度算法的早熟现象。     

模拟退火遗传算法在车间作业调度中的应用

研究车间作业调度系统,使资源达到优化配置.针对提高产品质量,缩短周期,传统遗传算法应用于车间作业调度过程中易出现收敛速度慢、易陷入局部最优,导致作业调度效率极低.为了提高车间作业调度的效率,提出一种模拟退火遗传算法的车间作业调度方法.在遗传算法种群更新过程引入模拟退火机制,防止早熟现象的产生,使种群在更新迭代过程中保持了多样性,加快了收敛速度,克服遗传算法过早收敛的缺陷.采用的SA-GA算法能够在最短时间找作业调度的最优解,对30个车间作业调度标准测试案例进行了仿真.仿真结果表明,使相对平均误差降低了4.6%,极大的提高了车间作业调度效率,验证了在实际生产中应用的可行和优越性.     

求解车间作业调度问题的混合遗传模拟退火算法

为了克服传统遗传算法解决车间作业调度问题的局限性,结合遗传算法(GA)和模拟退火算法(SA)的优点,提出一种混合遗传模拟退火算法(GASA),以便高效地解决车间作业调度问题.该算法既发挥了遗传算法收敛速度快、模拟退火算法搜索面广的优点,又克服了前者收敛容易早熟而后者收敛速度较慢的问题.在算法的操作细节上,加入自适应调整的遗传操作及最优个体保留策略,以及增加记忆功能的模拟退火操作与收敛准则.从而既防止了算法会陷入局部最优解的问题,又提高了算法的收敛速度及搜索效率.将提出的混合遗传模拟退火算法(GASA)应用于Muth和Thompson基准问题的实验运行,证明了该算法的高效性和有效性.     

基于模拟退火算法和遗传算法的图像降噪研究

文中首先介绍了遗传算法、模拟退火算法以及图像降噪的原理,然后重点论述了基于模拟退火算法和遗传算法的图像降噪方法与试验步骤,最后通过实验仿真论述了这种方法优于简单遗传算法。     

求解巡警调度问题的遗传退火算法

科学合理地调度巡警服务资源是提高公安部门执法能力的重要因素,为了优化调度方案并提高执法信息化水平,建立了巡警调度问题的数学模型,提出一种求解巡警调度问题的遗传退火算法。通过化简约束条件,方便了遗传算法的编码与解码、遗传与变异算子的设计。在遗传算法中引入模拟退火算法的Boltzmann更新机制,有效提高了算法的寻优能力。实验结果证明,遗传退火算法具有比传统遗传算法更强的寻优能力。     

基于混合遗传算法的车间生产调度问题研究

解决车间生产调度问题的目的不仅仅是为了缩短生产周期,更重要的是为了提高生产效率,降低生产成本。现大部分国有制造企业在车间生产过程中采用的是人工调度,调度时主要依赖于调度经验,调度效率不高且易出错。将遗传算法和模拟退火算法相结合,提出了解决车间调度问题的混合遗传算法,并给出了一种编码方法以及建立了相应的解码规则。遗传算法的引入保证了解的全局最优性,仿真后表明了该混合算法的可行性和有效性,且能够有效地提高搜索效率,改进了收敛性能。     

基于遗传和禁忌搜索算法求解车间调度优化问题

针对柔性生产环境下的车间调度问题,在考虑遗传算法早熟收敛问题和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法结合起来,提出了基于遗传和禁忌搜索的混合动态优化调度算法,并用实例对该算法进行了仿真研究。结果表明,此算法有很好收敛精度,是可行的，并且能够在扰动发生后提供新的调度计划，与传统的调度算法相比较，体现了明显的优越性。     

双资源生产车间调度问题的研究

将遗传算法(GA)和模拟退火算法(SA)相结合研究了双资源生产车间的调度优化问题,该混合算法将机床设备和工人合理地分配给加工任务,使评价性能指标获得最优。通过与国内外学者的算法进行比较,本算法获得的生产周期最短,机床利用率和工人利用率都较高,并且在某些情况下,平均流动时间也较短。因此可以证明本算法具有一定的优越性。     

Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem

A data mining based approach to discover previously unknown priority dispatching rules for job shop scheduling problem is presented. This approach is based on seeking the knowledge that is assumed to be embedded in the efficient solutions provided by the optimization module built using tabu search. The objective is to discover the scheduling concepts using data mining and hence to obtain a set of rules capable of approximating the efficient solutions for a job shop scheduling problem (JSSP). A data mining based scheduling framework is presented and implemented for a job shop problem with maximum lateness as the scheduling objective.     

Applying a hybrid artificial immune systems to the job shop scheduling problem

In today’s economy, manufacturing sectors are challenged by high costs, low revenues. As part of the managerial activities, scheduling plays an important role in optimizing cost, revenue, profit, time, and efficiency by optimization of available resources. The objective of this research is to evaluate the existing artificial immune system (AIS) principles, models, and applications, and to develop an algorithm applicable to job shop scheduling problems. The developed algorithm was based on the theories of the positive selection algorithm and the clonal selection principle. To test the algorithm, ten job shop scheduling problems were evaluated using the new AIS model. To validate the results, the same job scheduling problems were evaluated using a genetic algorithm (GA) model. The results of the two evaluations were compared against each other using the dimensions of optimality and robustness. The testing revealed that the AIS model was slightly less competitive than the GA model in the optimality test but beat the GA in robustness. Another key finding was that the robustness of the model increased as the best solutions produced by the model were closer to the known optimal.     

求解作业车间调度问题的改进的遗传算法

描述了作业车间调度问题,提出一种求解作业车间调度问题的改进的遗传算法.该算法对交叉算子和变异算子进行改进,能有效避免局部最优.通过对实例的计算和分析,取得了良好的调度效果,论证了该算法的有效性和稳定性.     

A two-stage genetic algorithm for multi-objective job shop scheduling problems

This paper presents a two-stage genetic algorithm (2S-GA) for multi-objective Job Shop scheduling problems. The 2S-GA is proposed with three criteria: Minimize makespan, Minimize total weighted earliness, and Minimize total weighted tardiness. The proposed algorithm is composed of two Stages: Stage 1 applies parallel GA to find the best solution of each individual objective function with migration among populations. In Stage 2 the populations are combined. The evolution process of Stage 2 is based on Steady-State GA using the weighted aggregating objective function. The algorithm developed can be used with one or two objectives without modification. The genetic algorithm is designed and implemented with the GALIB object library. The random keys representation is applied to the problem. The schedules are constructed using a permutation with m-repetitions of job numbers. Performance of the proposed algorithm is tested on published benchmark instances and compared with results from other published approaches for both the single objective and multi-objective cases. The experimental results show that 2S-GA is effective and efficient to solve job shop scheduling problem in term of solution quality.     

求解车间作业调度问题的一种改进遗传算法

针对标准遗传算法收敛速度慢和易陷入局部最优的问题,在总结已有经验的基础上对标准遗传算法提出改进:采用基于工序的编码、解码方式,每一次遗传操作后对种群采用循环选择并保留最优个体,对交叉操作和变异概率的计算提出了一系列改进方法,避免遗传算法产生无用解或陷入局部优化,以提高效率。通过实验验证,改进后的算法具有可行性,并且可以得到十分满意的结果。     

作业车间调度转换瓶颈算法的不可行解问题

文章讨论了作业车间调度问题转换瓶颈算法的一个缺陷。转换瓶颈算法是解决作业车间调度最小makespan(完工时间)问题的很有效的启发式算法。它是基于反复的解决某些单机调度问题。然而在转换瓶颈算法中用Carlier算法解单机调度问题并不总能得到可行解,文中给出了一个反例证明了有产生不可行解的情况。另外,文章还以简洁的方法证明了转换瓶颈算法若用Schrage算法替代Carlier算法解单机调度问题不会产生不可行解。     

Two-machine flow shop scheduling integrated with preventive maintenance planning

This paper investigates an integrated optimisation problem of production scheduling and preventive maintenance (PM) in a two-machine flow shop with time to failure of each machine subject to a Weibull probability distribution. The objective is to find the optimal job sequence and the optimal PM decisions before each job such that the expected makespan is minimised. To investigate the value of integrated scheduling solution, computational experiments on small-scale problems with different configurations are conducted with total enumeration method, and the results are compared with those of scheduling without maintenance but with machine degradation, and individual job scheduling combined with independent PM planning. Then, for large-scale problems, four genetic algorithm (GA) based heuristics are proposed. The numerical results with several large problem sizes and different configurations indicate the potential benefits of integrated scheduling solution and the results also show that proposed GA-based heuristics are efficient for the integrated problem.     

相关工件车间调度问题的拓扑算法

针对加工装配型离散制造企业实际生产的特点,提出了一类用于表示工序之间偏序关系的相关工件车间调度问题。为了利用已有的求解表示工序之间的线序关系的传统车间调度算法求解相关工件车间调度问题,设计了一种拓扑算法,该算法能够将工序之间的偏序关系转化为线序关系,将相关工件车间调度问题转化为传统的车间调度问题,通过实证研究,结果表明了拓扑算法是可行和高效的。