船体不规则分段的动态空间调度算法

提出了一种基于遗传算法的针对船体不规则分段的动态空间调度方法。基于遗传算法和临界多边形算法直接对不规则分段进行优化调度，提高空间的利用率。首先，利用遗传算法产生多个可行的分段调度序列。然后，基于临界多边形算法及重心最低策略，根据分段调度序列一一确定各不规则分段的空间位置。充分考虑到空间调度问题所特有的动态性和时空关联性，同时以场地利用率和未延迟加工分段所占比例的综合加权和作为评价函数，实现了调度方案的全局优化。以江苏澄西船厂大型散货船的119个不规则分段作为实验数据，并通过多个算法的比较，发现该动态调度算法可以快速收敛得到较优解，并且在空间利用率和延迟分段指标上有了提高，最终可以得到比较理想的调度结果，证明了所提方法在动态空间调度问题上的有效性和优越性。     

粒子群遗传混合算法求解考虑传输时间的FJSP

在某些生产制造场景中,工件在不同机器间的传输时间对车间调度的总拖期具有重要影响,本文基于此扩展了总拖期最小的柔性作业车间调度模型。针对问题模型的复杂性,采用粒子群优化算法和遗传算法的混合算法进行求解。在初始化过程以一定概率优选加工时间和传输时间短的机器并排除调度频繁的机器,使种群在保持多样性的前提下尽量选择优化结果好的个体;采用线性调整的方式动态改变交叉概率和变异概率的值,使种群在遗传算法的不同阶段具有不同的搜索强度;采用粒子群优化算法进行局部搜索,弥补了遗传算法局部搜索能力的不足。最后采用本文方法和其他方法求解柔性作业车间调度问题实例,并对比不同水平层次传输时间下的总拖期,验证了本文方法的有效性。     

基于遗传禁忌算法的双资源约束下并行生产线调度研究

并行生产线调度问题兼有并行机器和流程车间调度问题的特点,是一类新型的调度问题。在考虑遗传算法早熟收敛特性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,提出了基于遗传算法和禁忌搜索算法的双资源并行作业车间的调度优化问题算法,即考虑到了产品的调度受到机器、工人等资源制约的影响,对算法中种群的构造,适应度计算,遗传操作等方面进行了研究,最后给出了实例仿真和结论。     

实时系统中单处理器调度算法的优化设计研究

研究实时系统的建模与调度问题是运筹与控制领域研究的热点问题, 对实时系统中的单处理器的调度算法进行了分析与研究, 特别是对其中的单调速率算法和最早时间限优先算法进行了深入的研究, 指出单调速率算法是一种典型的静态调度算法, 并且证明了单调速率算法是单处理器最优的静态优先级调度算法, 同时还指出最早时间限优先算法是一种典型的动态优先级调度算法,证明了最早时间限优先算法是单处理器的最优的动态优先级调度算法. 最后, 为了更好地进行实时系统的建模与调度, 引入了一种新的对任务执行行为进行抽象的方法--T-LET平面方法, 利用这种方法建立了单处理器流调度模型和BLREF调度算法, 并指出这种模型和算法都具有很强的几何背景.     

蚁群遗传混合算法

将蚁群遗传混合算法分别求解离散空间的和连续空间优化问题.求解旅行商问题的混合算法是以遗传算法为整个算法的框架,利用了蚁群算法中的信息素特性的进行交叉操作;根据旅行商问题的特点,给出了4种变异策略;针对遗传算法存在的过早收敛问题,加入2-0pt方法对问题求解进行了局部优化.与模拟退火算法、标准遗传算法和标准蚁群算法进行比较,4种混合算法效果都比较好,策略D的混合算法效果最好.求解连续空间优化问题是以蚁群算法为整个算法的框架,加入遗传算法的交叉操作和变异操作,用测试函数验证了混合蚁群算法的正确性.     

区际救援物资中转调度的动态决策模型与算法

考虑灾害救援中灾区对应急物资的持续消耗,研究了区际多品种救援物资的动态中转调度问题.综合考虑各阶段调度费用、运输费用和库存费用总和最小化的救援物资中转调度安排和库存规划,建立了一个区际救援物资中转调度动态决策模型,并设计了一种矩阵编码的协进化遗传算法.最后通过一个算例验证了模型和算法的有效性.     

含不相关机的动态可重入柔性流水车间问题的混合DABC-GA算法

为了改善生产线的物流平衡和加强阶段间的时间衔接,扩展一般可重入柔性流水车间调度理论,以最小化总加权完工时间为目标,研究了每阶段含不相关并行机的动态可重入柔性流水车间问题,工件在各阶段的加工时间取决于加工它的机器。鉴于所研究问题为NP-hard问题,首先,建立整数规划模型;其次,设计元胞矩阵编码方案,提出融合离散人工蜂群算法和遗传算法的一种混合算法以获得问题的近优解;最后,为了评估混合算法的性能,将所提出算法和一些元启发式算法进行了不同规模问题的对比测试,实验结果说明了所提算法的有效性。     

时变条件下带时间窗车辆调度问题的模拟退火算法

带时间窗车辆调度问题(VRPTW)是一类要求满足容积和时间窗约束的车辆路径优化问题,现 有大部分相关文献只考虑了车辆行驶速度恒定的情况,忽略了各种动态因素的影响.本文研究的时变条件下带时间窗车辆调度问题将车辆行驶速度考虑成时变分段函数,并利用模拟退火算法进行求解,最后通过实验结果说明算法的有效性.     

考虑模糊交货期的柔性作业车间动态调度研究

针对柔性作业车间调度在机器故障扰动情况下的动态性,采用基于事件与周期混合驱动的滚动窗口再调度策略进行动态调度.对于工件交货期模糊的情况采用梯形交货期窗口表示,并运用字典序多目标规划的方法,以平均流经时间最小、能耗最小、客户满意度最大为目标,建立多目标柔性作业车间动态调度模型,并设计了改进的自适应免疫遗传算法,在对种群进行初始化时,将初始化机器、初始化工序及随机初始化结合在一起,并对模型进行求解.将算例仿真结果与遗传算法所得的结果进行对比,验证算法的有效性.     

基于改进模拟植物生长算法的集装箱码头插船调度优化

为解决临时接受计划外船舶到港作业的插船调度问题,建立了综合考虑港口安排插船作业的成本最小优化模型,将模拟植物生长算法(PGSA)改进后进行求解。经过对实际案例进行计算分析后表明,所建模型和算法可以有效解决上述问题并取得了较好结果。为验证算法的有效性,同时引入遗传算法进行计算对比,结果显示经改进的PGSA在求解过程中具有较好的收敛速度与精确度。采用本文建立的模型和算法能够快速解决临时插船的调度调整问题,为集装箱码头在特殊情况下泊位调度优化提供了解决问题的思路和方法。     

A genetic algorithm for service level based vehicle scheduling

In many practical applications, vehicle scheduling problems involve more complex evaluation criteria than simple distance or travel time minimization. Scheduling to minimize delays between the accumulation and delivery of correspondence represents a class of vehicle scheduling problems, where: the evaluation of candidate solutions is costly, there are no efficient schemes for evaluation of partial solutions or perturbations to existing solutions, and dimensionality is limiting even for problems with relatively few locations. Several features of genetic algorithms (GA's) suggest that they may have advantages relative to alternative heuristic solution algorithms for such problems. These include ease of implementation through efficient coding of solution alternatives, simultaneous emphasis on global as well as local search, and the use of randomization in the search process. In addition, a GA may realize advantages usually associated with interactive methods by replicating the positive attributes of existing solutions in the search process, without explicitly defining or measuring these attributes. This study investigates these potential advantages through application of a GA to a service level based vehicle scheduling problem. The procedure is demonstrated for a vehicle scheduling problem with 15 locations where the objective is to minimize the time between the accumulation of correspondence at each location and delivery to destination locations. The results suggest that genetic algorithms can be effective for finding good quality scheduling solutions with only limited search of the solution space.     

求解柔性作业车间调度的动态群智能优化算法

针对柔性作业车间调度问题,提出一种新型两阶段动态混合群智能优化算法.算法初始阶段采用动态邻域的协同粒子群进行粗搜索,第二阶段提出了基于混沌算子的蜂群进行细搜索,既增强了种群多样性,又提高了算法搜索精度,实现了全局搜索与局部搜索能力的有效平衡.针对柔性作业车间调度问题特点,采用独特的编码方式和位置更新策略来避免不合法解的产生.最后将此算法在不同规模的实例上进行了仿真测试,并与最近提出的其他几种具有代表性的算法进行了比较,验证了算法的有效性和优越性.     

Single machine stochastic JIT scheduling problem subject to machine breakdowns

In this paper we research the single machine stochastic JIT scheduling problem subject to the machine breakdowns for preemptive-resume and preemptive-repeat.The objective function of the problem is the sum of squared deviations of the job-expected completion times from the due date.For preemptive-resume,we show that the optimal sequence of the SSDE problem is V-shaped with respect to expected processing times.And a dynamic programming algorithm with the pseudopolynomial time complexity is given.We discuss the difference between the SSDE problem and the ESSD problem and show that the optimal solution of the SSDE problem is a good approximate optimal solution of the ESSD problem,and the optimal solution of the SSDE problem is an optimal solution of the ESSD problem under some conditions.For preemptive-repeat,the stochastic JIT scheduling problem has not been solved since the variances of the completion times cannot be computed.We replace the ESSD problem by the SSDE problem.We show that the optimal sequence of the SSDE problem is V-shaped with respect to the expected occupying times.And a dynamic programming algorithm with the pseudopolynomial time complexity is given.A new thought is advanced for the research of the preemptive-repeat stochastic JIT scheduling problem.     

A hybrid metaheuristic for the resource-constrained project scheduling problem

This study presents a hybrid metaheuristic ANGEL for the resource-constrained project scheduling problem (RCPSP). ANGEL combines ant colony optimization (ACO), genetic algorithm (GA) and local search strategy. The procedures of ANGEL are as follows. First, ACO searches the solution space and generates activity lists to provide the initial population for GA. Next, GA is executed and the pheromone set in ACO is updated when GA obtains a better solution. When GA terminates, ACO searches again by using a new pheromone set. ACO and GA search alternately and cooperatively in the solution space. This study also proposes an efficient local search procedure which is applied to yield a better solution when ACO or GA obtains a solution. A final search is applied upon the termination of ACO and GA. The experimental results of ANGEL on the standard sets of the project instances show that ANGEL is an effective method for solving the RCPSP.     

云制造平台加工能力分享调度优化研究

云制造平台加工能力分享的调度优化是云制造模式的核心运营问题。为提高云制造平台对参与能力分享企业资源的利用率,解决实际云制造需求环境下企业级制造资源调度问题,达到云制造平台动态实时的要求,以最小化工期和成本为目标函数,构建了基于平台并面向分享的云加工能力调度模型。为快速求解多个云任务订单的最优调度序列,引入任务简易编码和随机权重,设计了改进的二阶粒子群算法,并提出可分享加工能力时间窗的更新策略。最后,通过算例验证云制造平台加工能力调度机制的可行性和有效性。     

优化交货期窗口的两阶段供应链排序问题

研究一类优化交货期窗口的两阶段供应链排序问题. 优化交货期窗口是指交货期窗口的开始与结束时刻是决策变量, 不是输入常量. 两阶段是指工件先加工, 后运输: 加工阶段是一台加工机器逐个加工工件;运输阶段是无限台车辆分批运输完工的工件. 工件的开始运输时刻与完工时刻之差定义为工件的储存时间, 且有相应的储存费用. 若工件的运输完成时刻早于(晚于)交货期窗口的开始(结束)时刻, 则有相应的提前(延误)惩罚费用. 目标是极小化总提前惩罚费用、总延误惩罚费用、总储存费用、总运输费用以及与交货期窗口有关的费用之和. 针对单位时间的延误惩罚费用不超过单位时间的储存费用、单位时间的储存费用不超过单位时间的提前惩罚费用的情形, 给出了时间复杂性为O(n^{8})的动态规划算法.     

Balanced train timetabling on a single-line railway with optimized velocity

This paper aims to find an optimal balanced schedule with the least delay-ratio (i.e., the ratio of the total delay time and the total free-run time) by considering the impacts of the train velocity. A rigorous optimization model is proposed under the consideration of feasible speed constraint for finding the optimal velocity for each train on the railway line. To obtain an approximately optimal scheduling strategy, a combination of the improved TAS (ITAS) method and the genetic algorithm (GA), called GA-ITAS method, is in particular proposed to effectively solve the proposed model. The results of numerical experiments demonstrate the efficiency and effectiveness of the proposed approaches.     

A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop

In this paper a new mixed-integer linear programming (MILP) model is proposed for the multi-processor open shop scheduling (MPOS) problems to minimize the makespan with considering independent setup time and sequence dependent removal time. A hybrid imperialist competitive algorithm (ICA) with genetic algorithm (GA) is presented to solve this problem. The parameters of the proposed algorithm are tuned by response surface methodology (RSM). The performance of the algorithm to solve small, medium and large sized instances of the problem is evaluated by introducing two performance metrics. The quality of obtained solutions is compared with that of the optimal solutions for small sized instances and with the lower bounds for medium sized instances. Also some computational results are presented for large sized instances.     

急件订单干扰下虚拟单元重调度

虚拟单元生产中,针对急件订单干扰情况,研究了考虑序位相似性,即尽量保持初始工序的加工次序的虚拟单元重调度问题。为了应对急件订单干扰,设置了各工件工序可用机器集合和相应的加工时间集合,构建了以序位相似性最大和急件订单完工时间、系统总流程时间最短为目标的多目标非线性整数规划模型。针对模型自身特征,采用了遗传—蚁群算法相结合的优化算法求解模型。最后,以船舶实际生产为例,验证了模型的可行和优越性,以及算法的有效性。     

考虑路径冲突的AGV配置与调度优化

合理调度有限的码头资源以满足船舶的装卸时间要求是自动化集装箱码头的重要目标之一。针对自动化集装箱码头自动导引车(automated guided vehicle,AGV)配置与调度问题,考虑船舶装卸时间要求和AGV运输过程中的路径冲突,提出分阶段调度策略。将船舶装卸作业分为卸船阶段、装卸同步阶段、装船阶段三个阶段,在每个阶段中,建立以最小化最大完工时间和最小化AGV空载和等待时间为双目标的调度优化模型,并设计基于NSGA-Ⅱ的启发式算法求解。根据本阶段的实际完工时间,从最优解集中选择下一阶段AGV的配置与调度方案。最后对比其他调度方案表明本文调度方案能够满足集装箱船的装卸时间要求,且提高了AGV的利用率,更符合码头实际作业要求。