并行机问题的模拟退火调度算法研究

研究了一类调度目标是最小化最大完成时间的并行机调度问题.考虑到此问题的NP-hard特性,引入模拟退火算法思想以获取高质量近优解.分析了现有此问题模拟退火算法的缺陷,定义了关键机器和非关键机器,设计了一个包含局部优化的模拟退火算法.除了交换变换,还引入插入变换以改变各子调度中作业个数.大量的随机数据实验用于验证算法解的质量和计算效率,实验结果表明该模拟退火算法能够在有限时间内为大规模问题求得高质量满意解.     

单机单转包商调度与外包联合优化问题的改进启发式算法

针对以总完工时间与总外包费用加权和为优化目标、总外包费用不超过给定上限的单机单转包商调度与外包联合优化问题,设计出一种改进的剔除型启发式算法。该算法通过运用动态规划技术求解新的辅助问题来获取初始外包工件集,并引入判定条件提前从初始外包工件集中剔除特定工件。为满足对总外包费用的上限约束,还利用新型的启发式筛选次序族逐一确定从当前外包工件集中剔除的工件。在仿真实验中,通过生成大量的测试算例,对比分析了改进算法与另2种已报道算法在求解质量、计算时间上的表现情况。实验结果表明所提出的改进算法在解的整体质量上具备显著的比较优势,并且能在5.6秒内完成对工件总数为1500的测试算例的求解。     

带运输机的流水车间调度的最优算法

研究带运输时间的流水调度:在该问题中有两台机器A,B和一个运输机V,n个工件,工件需要先在机器A上加工然后在机器B上加工最后被运输机V运往目的地,而且运输机V最初停在机器B旁边.模型的目标是使所有工件都运往目的地的时间最短.文中给出了三种情况下的最优调度算法:i)A,B机器加工工件顺序给定时我们给出了线性时间的最优算法;ii)所有的工件加工时间在机器B上时间相等时我们给出了时间复杂度为O(nlogn)的最优算法;iii)机器B上工件最短加工时间大于等于机器A上工件最长加工时间时给出了时间复杂度为O(n~2)的最优算法.     

基于改进模拟退火算法的推动式生产-配送协调优化

为减小物资生产与配送不协调造成的成本及生产资源浪费,建立了考虑推动式生产调度的物资配送优化模型,并针对标准模拟退火算法受随机因素影响易陷入局部最优的缺点,设计带有回火与缓冷操作的改进模拟退火算法对模型求解,确定了优化的车辆配送路线以及物资生产计划。对比实验结果表明:相对于单纯的物资配送优化模型,考虑推动式生产调度的配送优化模型,能够有效减小物资滞留时间以及配送延误成本;相较于标准模拟退火算法,改进算法搜索到了更优解,且计算结果的标准差减小了93.42%,稳定性更好;同时,改进模拟退火算法具有较低的偏差率,在中小规模算例中求解质量较高,平均偏差率在0.5%以内。     

置换流水车间调度问题的中心引力优化算法求解

目前求解置换流水车间调度问题的智能优化算法都是随机型优化方法,存在的一个问题是解的稳定性较差。针对该问题,本文给出一种确定型智能优化算法——中心引力优化算法的求解方法。为处理基本中心引力优化算法对初始解选择要求高的问题,利用低偏差序列生成初始解,提高初始解质量;利用加速度和位置迭代方程更新解的状态;利用两位置交换排序法进行局部搜索,提高算法的优化性能。采用置换流水车间调度问题标准测试算例进行数值实验,并和基本中心引力优化算法、NEH启发式算法、微粒群优化算法和萤火虫算法进行比较。结果表明该算法不仅具有更好的解的稳定性,而且具有更高的计算精度,为置换流水车间调度问题的求解提供了一种可行有效的方法。     

基于变分辨率网格的模拟退火算法在形状优化问题上的应用研究

形状优化是一类复杂的优化问题,在工业上经常作为结构优化的一个分支出现.它以几何形状作为优化对象,需要求得某种性能条件最优的几何形状,一般来说对建模和计算量的要求都比较高.模拟退火是一种用途非常广泛的优化算法,可以处理各种复杂的优化问题.但标准的模拟退火算法在处理形状优化问题时,由于搜索空间的范围太大,经常陷入局部最优解,所以需要耗费巨大的计算量,实用性有限.本文讨论了在变分辨率的离散网格中的模拟退火算法,将低分辨率网格下的最优结果过渡到高分辨率网格下作为一个良好的初始解,这样可以有效地规避局部最优点,缩小搜索范围,极大地提高模拟退火算法的效率.并以蚱蜢问题为算例,对算法的效果进行了验证.     

动态连续蚁群系统及其在天基预警中的应用

存在监控冲突的天基中段预警传感器调度优化是一个动态、高维、复杂多约束的非线性优化问题,其解空间的高维度与状态复杂性直接制约了智能优化算法的运用.本文以任务分解与任务复合优先权计算为基础,通过二级分离机制将解空间维度与状态复杂性降低至适于连续蚁群(continuous ant-colony optimization,CACO)处理的全局优化形态,构建出相应的优化子路径集.在此基础上,针对监控冲突导致的状态变化特性,从局部搜索递进与募集的角度提出适于传感器调度优化的MG-DCACO(double direction continuous ant-colony optimization based mass recruitment and group recruitment)算法,成功将智能优化算法应用于基于低轨星座的天基中段预警中.最后对算法的收敛性进行论证,并通过与已有规则调度算法的对比得出MG-DCACO算法可获得优于规则调度算法的全局最优解.     

双机成比例无等待流水线重调度干扰管理研究

针对由异速机构成的双机成比例无等待流水线的加工特点,研究了机器扰动工况下的生产重调度问题,提出了兼顾初始调度目标(最小化制造期)和扰动修复目标(最小化工件滞后时间和)的干扰管理方法。在最短加工时间优先(SPT)排序规则的最优解特性分析基础上,证明了右移初始加工时间表是事后干扰管理的最优调度方案,建立了基于SPT规则的事前干扰管理模型,设计了基于理想点趋近的多目标处理策略,提出了离散量子微粒群优化与局部搜索机制相结合的启发式模型求解算法。算例实验结果表明,本文提出的干扰管理模型和算法是有效的。     

关于不可微规划中Bundle方法的研究

本文给出了一个解Lipschitz约束极小化问题的算法,它是Bundle方法的改进。在通过解二次规划获得下降方向时,只需计算二次规划的一个ε最优解,并且所需要的信息量是可控制的。由于改进了线搜索规则,因而算法的实现过程及其收敛性的证明都比以往的Bundle方法简洁。算法具有良好的收敛性,即它所形成的点列的每个聚点都是稳定点。     

一种双目标带约束工件成类的平行机器调度启发式规则

提出了一种快速而有效的启发式规则(fam ily slack,简称FSLACK),来求解极小化总延误时间和极小化最大完工时间两个目标,工件按产品类型成组,带模具数量约束的平行机器生产调度问题.本文提出的FSLACK与EDD、LPT及SLACK进行了比较.随机订单的测试结果表明,本文提出的启发式规则在求解双目标带约束工件成类的平行机器调度问题上是有效的.这表明该算法可以应用在成型加工业的现场作业调度.     

Meta-heuristic approaches for minimizing total earliness and tardiness penalties of single-machine scheduling with a common due date

This study addresses a class of single-machine scheduling problems involving a common due date where the objective is to minimize the total job earliness and tardiness penalties. A genetic algorithm (GA) approach and a simulated annealing (SA) approach utilizing a greedy local search and three well-known properties in the area of common due date scheduling are developed. The developed algorithms enable the starting time of the first job not at zero and were tested using a set of benchmark problems. From the viewpoints of solution quality and computational expenses, the proposed approaches are efficient and effective for problems involving different numbers of jobs, as well as different processing time, and earliness and tardiness penalties.     

Single-machine scheduling with deteriorating jobs and learning effects to minimize the makespan

This paper studies the single-machine scheduling problem with deteriorating jobs and learning considerations. The objective is to minimize the makespan. We first show that the schedule produced by the largest growth rate rule is unbounded for our model, although it is an optimal solution for the scheduling problem with deteriorating jobs and no learning. We then consider three special cases of the problem, each corresponding to a specific practical scheduling scenario. Based on the derived optimal properties, we develop an optimal algorithm for each of these cases. Finally, we consider a relaxed model of the second special case, and present a heuristic and analyze its worst-case performance bound.     

Construction of factory schedules using reverse simulation

A real-world, multi-stage, industrial scheduling problem is presented. An algorithm is described that converts a sequence of jobs into a complete schedule. Backward simulation is used to determine minimum storage requirements when scheduling each job, and to calculate the minimum amount of delay required. Combining this algorithm with a metaheuristic, such as simulated annealing, results in an effective algorithm for schedule optimization.     

An approximate algorithm for a high-multiplicity parallel machine scheduling problem

We consider a high-multiplicity parallel machine scheduling problem where the objective is to minimize the weighted sum of completion times. We suggest an approximate algorithm and we prove that it is asymptotically exact. The algorithm exploits a convex quadratic relaxation of the problem to fix a partial schedule, consisting of most jobs, and then assigns the residual jobs following a simple and general rule. The quality of the obtained solution is evidenced by some numerical tests.     

Parallel machine scheduling with common due windows

With the prevalence of on-time scheduling, timely product submission has become a crucial contributor to customer satisfaction. Studies examining on-time scheduling primarily seek to determine the minimum weighted sum of earliness and tardiness penalties. This study assumes that all machines are identical. Furthermore, this study assumes that jobs are independent and share a common due date window when investigating scheduling problems involving parallel machines with a minimum total number of early and tardy jobs (or maximum number of on-time jobs). This study presents related theorems and a novel simplified algorithm based on the problem. Additionally, rule characteristics are examined, and simulated data are used to verify the effectiveness and timeliness of the proposed algorithm. The theoretical proof and data test results all indicate that the proposed approach obtains the best solution within the shortest time.     

Simulated annealing and genetic algorithms for minimizing mean flow time in an open shop

This paper considers the problem of scheduling n jobs on m machines in an open shop environment so that the sum of completion times or mean flow time becomes minimal. It continues recent work by Bräsel et al. [H. Bräsel, A. Herms, M. Mörig, T. Tautenhahn, T. Tusch, F. Werner, Heuristic constructive algorithms for open shop scheduling to minmize mean flow time, European J. Oper. Res., in press (doi.10.1016/j.ejor.2007.02.057)] on constructive algorithms. For this strongly NP-hard problem, we present two iterative algorithms, namely a simulated annealing and a genetic algorithm. For the simulated annealing algorithm, several neighborhoods are suggested and tested together with the control parameters of the algorithm. For the genetic algorithm, new genetic operators are suggested based on the representation of a solution by the rank matrix describing the job and machine orders. Extensive computational results are presented for problems with up to 50 jobs and 50 machines, respectively. The algorithms are compared relative to each other, and the quality of the results is also estimated partially by a lower bound for the corresponding preemptive open shop problem. For most of the problems, the genetic algorithm is superior when fixing the same number of 30 000 generated solutions for each algorithm. However, in contrast to makespan minimization problems, where the focus is on problems with an equal number of jobs and machines, it turns out that problems with a larger number of jobs than machines are the hardest problems.     

Total tardiness minimization in permutation flowshop with deterioration consideration

In this paper, we consider a permutation flowshop scheduling problem with deteriorating jobs. The objective is to minimize the total tardiness of all jobs. A branch-and-bound algorithm incorporating with a dominance property and a lower bound is developed. Furthermore, two metaheuristic algorithms, the simulated annealing algorithm, and the particle swarm optimization method, are proposed. Finally, computational studies are given.     

Minimizing the Number of Tardy Jobs in a Permutation Flowshop Scheduling Problem with Setup Times and Time Lags Constraints

This paper studies the permutation flowshop scheduling problem with sequence dependent setup times and time lags constraints minimizing the number of tardy jobs. Dependent setup times are defined as the work to prepare the machines between two successive jobs. Time lags are defined as intervals of time that must exist between every couple of successive operations of the same job. Two mathematical programming formulations are proposed for the considered problem. A simulated annealing algorithm is also developed to solve the problem. Computational experiments are presented and discussed.     

Resource constraint scheduling with a fractional shared resource

We consider a scheduling problem motivated by mining in remote off-grid areas. In this model, mines have pre-assigned mineral processing jobs and their own machine for executing these jobs. Each job also needs a certain amount of electricity in order to get completed. The electricity, on the other hand, is of limited supply and must be shared between the mines. We present a mathematical formulation of the problem and a Lagrangian relaxation based heuristic. Computational results which compares our heuristic with genetic algorithm and simulated annealing are also presented.     

A comparison of local search methods for flow shop scheduling

Local search techniques are widely used to obtain approximate solutions to a variety of combinatorial optimization problems. Two important categories of local search methods are neighbourhood search and genetic algorithms. Commonly used neighbourhood search methods include descent, threshold accepting, simulated annealing and tabu search. In this paper, we present a computational study that compares these four neighbourhood search methods, a genetic algorithm, and a hybrid method in which descent is incorporated into the genetic algorithm. The performance of these six local search methods is evaluated on the problem of scheduling jobs in a permutation flow shop to minimize the total weighted completion time. Based on the results of extensive computational tests, simulated annealing is found to generate better quality solutions than the other neighborhood search methods. However, the results also indicate that the hybrid genetic descent algorithm is superior to simulated annealing.