同顺序加工调度问题的一种精确求解方法

同顺序加工调度问题是NP问题,分析了这类问题的特点及求解的难点,结合广度优先搜索方法的特点,提出了启发式双侧广度优先搜索方法,混合使用动态规划方法、下界算法和近似求解方法求解同顺序加工调度问题.实验结果表明,启发式双侧广度优先搜索方法求解同顺序加工调度问题时,可以大大减少搜索次数,适合于求解工序较少的同顺序加工调度问题;如果下界算法较好,还能快速求解工序较多的同顺序加工调度问题.     

两机零等待流水车间调度问题的启发式算法

为实现两机零等待流水车间调度问题的总流程时间最小化,结合问题的结构信息提出了一种快速求解近优解的启发式算法。在该类问题中,工件在每台机器上的操作包括调整、加工和移除3部分,且调整和移除时间都与工件的加工时间相互分离。首先分析了该类问题的优化性质,结合优化性质进而构造出求解算法。在中小规模和大规模问题上,将启发式算法的结果分别与最优解和最优解的下界值进行了比较。大量数值计算实验表明了该算法的有效性和解决大规模实际问题的潜力。     

启发式算法和遗传算法在生产调度中的应用

根据现代制造企业对生产调度的要求,提出了一种启发式算法和遗传算法相结合的生产调度系统结构.使用启发式规则为各工序分配机器,制定加工计划,调用遗传算法对加工计划进行排序,生成最终调度结果.通过将CLIPS推理机嵌入到系统中,实现了决策规则和程序体的分离,易于规则的修改和进一步扩展,系统更具有柔性.由启发式规则生成遗传算法的部分初始种群,提高了初始种群的质量.通过将任务分解,降低了问题求解的复杂度,并由实例验证了其可行性.     

启发式算法和遗传算法在生产调度中的应用

根据现代制造企业对生产调度的要求,提出了一种启发式算法和遗传算法相结合的生产调度系统结构。使用启发式规则为各工序分配机器,制定加工计划,调用遗传算法对加工计划进行排序,生成最终调度结果。通过将CLIPS推理机嵌入到系统中,实现了决策规则和程序体的分离,易于规则的修改和进一步扩展,系统更具有柔性。由启发式规则生成遗传算法的部分初始种群,提高了初始种群的质量。通过将任务分解,降低了问题求解的复杂度,并由实例验证了其可行性。     

调整时间与顺序相关的等同并行机调度

调整时间与顺序相关的等同并行机调度在生产服务业与制造业中有着十分广泛的应用背景,具有计算复杂性的主要特点。调整时间与顺序相关的等同并行机调度是将被加工工件集的各工件分配给等同并行机资源,并安排工件的加工次序。它是决策的一种形式,其目的是优化一个或多个目标。研究以最小化被加工工件最大完工时间为目标的调整时间与顺序相关的等同并行机调度,建立该问题的数学规划模型,根据问题的结构特点开发基于两段式染色体表达的遗传算法以获得该问题的近似最优解;在所建立数学规划模型的基础上,引入所求解问题的下界对近似最优解的质量进行评价。对具有不同规模的问题实例进行计算试验,计算结果表明所设计的遗传算法能够在可接受的计算时间内获得合理的解。     

并行多机成组工作总流水时间调度问题

有N个成组工件将在M台并行一致的机器上加工，当一个工件接在不同组的工件之后时需要装设，而接在同组工件之后时不需要重新装设，目标函数是使总的通过时间最短，这是一个NP难题，最优解很难找到笔者在文中提出了一个启发式算法，为了验证该算法的结果，又提出了一个求解最优解下界的线性规划模型，并用分枝定界法求解出下界解，在中小规模问题条件下，将下界解、启发式的解及最优解进行比较，证明了下界解的有效性，然后，在中等规模水平上，将启发式算法的结果与下界解进行了比较，最终证明该启发式算法具有解决大规模实际问题的潜力。     

模具虚拟企业项目调度遗传算法研究

为解决模具虚拟企业资源受限的多项目调度问题，提出了多项目调度的数学模型。在此基础上采用了一种混合遗传算法的求解方法，该方法分两个阶段进行：先采用优先规则启发式方法产生一组可行解，该过程可视为遗传算法解的初始化；然后对这组可行解采用遗传算法进行优化，以得到次优解或者近似最优解。最后通过实际算例所得结果并与其他方法的比较分析验证了该方法的有效性。     

一种新型的作业车间调度算法的研究与实现

将工件的剩余加工时间分为相对剩余加工时间和绝对剩余加工时间 ,提出了优先分配启发式算法的一种新的优先分配规则 ,即相对剩余加工时间最大的概念 ,把调度分成多个阶段的部分调度 ,通过比较部分调度集合中的可调度工序的相对剩余加工时间 ,求解出每个部分调度的最优解 ,从而使整个调度达到全局最优或近似最优。最后 ,开发出调度软件 ,验证了算法在工程中的可行性、有效性。     

两阶段法求解1.5维切割问题

切割问题亦称为下料问题.多维切割问题是机加工、木材加工和造纸等行业在生产中经常遇见的实际问题.1.5维切割问题是介于一维和二维切割问题之间的一类问题.针对1.5维切割问题的特殊性,采用两阶段方法进行求解.第一阶段是将1.5维切割问题转化为一维切割问题,第二阶段首先利用一种启发式算法得到一组初始解,然后使用粒子群算法对初始解进行优化,得最终解.     

求解流水车间调度问题的瓶颈指向启发式算法

针对最小化时间表长的流水车间调度问题,提出一种根据工件加工时间特征构建工件调度的瓶颈指向启发式算法。首先,为构建初始工件排序,充分利用各机器负荷一般不相等的特点,瓶颈阶段前加工时间较短而之后加工时间相对较长的工件优先开始加工;其次,当有工件等待加工时,根据工件在瓶颈机器前或后加工时间的特征调整工件加工顺序;最后,采用邻近工件成对交换和插入的方式改进初始调度。当瓶颈机器趋于中间阶段,或瓶颈机器上工件的加工时间趋于增加时,求解效果较好。数据实验表明算法是有效的。     

An efficient bi-objective heuristic for scheduling of hybrid flow shops

This paper considers the problem of scheduling n independent jobs in hybrid flow shop environment with sequence-dependent setup times to minimize the makespan and total tardiness. For the optimization problem, an algorithm namely; bi-objective heuristic (BOH) is proposed for searching Pareto-optimal frontier. The aim of the proposed algorithm is to generate a good approximation of the set of efficient solutions. The BOH procedure initiates by generating a seed sequence. Since the output results are strongly dependent on the initial solution and in order to increase the quality of output results algorithm, we have considered how the generation of seed sequence with random way and particular sequencing rules. Two methods named Euclidean distance and percent error have been proposed to compare non-dominated solution sets obtain of each seed sequence. It is perceived from these methods that the generation of seed sequence using earliest due date rule is more effective. Then, the performance of the proposed BOH is compared with a simulated annealing proposed in the literature and a VNS heuristic on a set of test problems. The data envelopment analysis is used to evaluate the performance of approximation methods. From the results obtained, it can be seen that the proposed algorithm is efficient and effective.     

适应性禁忌搜索算法求解带回程的时变速度车辆路径问题

为求解带回程的时变速度车辆路径问题，建立了问题的数学模型并提出适应性禁忌搜索算法求解。适应性禁忌搜索算法为两阶段的启发式方法，改进固定速度下的启发式方法用于生成时变速度下的初始解，然后运用适应性禁忌搜索算法进一步优化，包括邻域生成规则定义，采用Hash表存储搜索过程中的解，检测解的重复状态，定义逃离局部搜索区域规则。对改进的标准问题进行测试，同时与最近邻域搜索算法的结果作比较，结果表明算法是有效的。与固定速度情形相比较，时变速度模型得到的调度方案更加合理。     

冗余混联式钻铆机床姿态调整轨迹优化

针对混联式自动钻铆机床因冗余自由度导致钻铆姿态多样化的问题，提出了一种基于动态规划的姿态调整轨迹优化方法，该方法通过离散化钻铆点姿态的可行解空间来构建姿态调整轨迹的全连接层模型，采用动态规划方法计算姿态调整的最优轨迹。分析了钻铆过程中的冗余自由度，基于旋量方法给出了逆运动学解。阐述了壁板钻铆姿态调整轨迹优化原理，以系统能量消耗最小为优化目标，给出了笛卡儿空间姿态调整最优轨迹，将其映射到关节空间进行关节运动轨迹光顺。实验验证表明所提方法有效，可提高飞机壁板钻铆效率。     

Optimization of alignment in semiconductor lithography equipment

Recent enhanced integration of semiconductors permits less and less overlay error at exposure. Accordingly, higher-order alignment adjustment, which considers not only linear but also nonlinear error components, has been recently carried out. However, conventional higher-order alignment adjustment methods are based on least squares, and thus are only approximation approaches toward the goal of maximizing die yields. In particular, outliers taking place at exposure or alignment measurement strongly affect alignment adjustment and decrease die yields. From such a background, this paper models a problem finding optimal higher-order alignment adjustment under the purpose of maximizing die yields as an integer programming problem. Furthermore, we evaluate the proposed method based on integer programming and conventional least-squares approaches using numerical simulation. The experimental results demonstrate that the proposed method produces a solution that is excellent compared to the conventional approaches, irrespective of existence of outliers.     

基于双闭环PID的挖掘机泵扭矩控制策略

发动机与泵扭矩的不匹配所造成发动机转速波动是挖掘机能量浪费的主要原因.分析了发动机与泵稳定工作条件下的扭矩匹配关系,试验研究了泵扭矩调整的影响因素,确定了调整泵吸收扭矩让发动机工作在调速特性曲线的电控节能控制目标,建立了基于PID（Proportion Integration Differentiation）的泵扭矩调整方案并开展了泵扭矩调整试验.试验结果表明：挖掘机液压系统自带压力切断功能可实现无负荷和溢流工况下的节能控制,电控系统仅在泵正常工作范围内通过改变比例阀电流调整泵扭矩,泵扭矩的控制策略在工作初始和工作过程中的调整方法有差异,采用的双闭环PID控制方法能保证发动机稳定在调速特性范围内,这为开发节能效果更好的挖掘机节能控制系统打下基础.     

A heuristic method for the combined location routing and inventory problem

The combined location routing and inventory problem (CLRIP) is used to allocate depots from several potential locations, to schedule vehicles’ routes to meet customers’ demands, and to determine the inventory policy based on the information of customers’ demands, in order to minimize the total system cost. Since finding the optimal solution(s) for this problem is a nonpolynomial (NP) problem, several heuristics for searching local optima have been proposed. However, the solutions for these heuristics are trapped in local optima. Global search heuristic methods, such as tabu search, simulated annealing method, etc., have been known for overcoming the combinatorial problems such as CLRIP, etc. In this paper, the CLRIP is decomposed into two subproblems: depot location-allocation problem, and routing and inventory problem. A heuristic method is proposed to find solutions for CLRIP. First of all, an initial solution for CLRIP is determined. Then a hybrid heuristic combining tabu search with simulated annealing sharing the same tabu list is used to improve the initial solution for each subproblem separately and alternatively. The proposed heuristic method is tested and evaluated via simulation. The results show the proposed heuristic method is better than the existing methods and global search heuristic methods in terms of average system cost.     

A Tabu Search Algorithm for Single Machine Scheduling with Release Times,Due Dates,and Sequence-Dependent Set-up Times

In this paper, we present a tabu search algorithm that schedules N jobs to a single machine in order to minimise the maximum lateness of the jobs. The release times, due dates, and sequence-dependent set-up times of the jobs are assumed to exist. We modified the original tabu search method to be suitable for the scheduling problem. The proposed tabu search algorithm is composed of two parts: a MATCS (modified apparent tardiness cost with set-ups) rule for finding an efficient initial solution, and the tabu search method to seek a near optimal solution from the initial solution. The experimental results show that the tabu search algorithm obtains much better solutions more quickly than the RHP (rolling horizon procedure) heuristic suggested by Ovacik and Uzsoy.     

Sequencing of an M machine flow shop with setup, processing and removal times separated

In this research, a flow shop scheduling problem in which setup, processing and removal times are separated, with the objective of minimizing makespan, is considered. A tabu search based heuristic is presented for solving the addressed problem. The proposed heuristic begins with the construction of artificial processing times for each operation; then a modified NEH algorithm is used to generate an initial solution, followed by a designed tabu search procedure applied for further improvement of the solution. The proposed heuristic, as well as the existing one, is evaluated in a large number of randomly generated problems. The results of the experimental investigations of the proposed heuristic algorithm and the existing heuristic in meeting the objective of makespan are also reported. It is found that the solution quality of the proposed tabu search heuristic is better than that of the existing heuristic, but for large size problems more computational efforts are needed; however, the CPU time is still acceptable.     

Solving reverse logistics vehicle routing problems with time windows

A vehicle routing problem with simultaneous pick-up and delivery in closed-loop logistics network optimization is studied in this paper. Since, in practice, material pick-up and delivery are only allowed to take place on certain time periods, we consider the reverse logistics vehicle routing problem with time windows. A mixed integer programming model is proposed to formulate the considered problem. A heuristic solution approach for solving the model is developed due to the NP-hard nature of solving the model. The heuristic solution is then used as an initial solution of a simulated annealing procedure for improved solutions. The proposed heuristic method and the simulated annealing procedure yield very promising solutions in much less computational time when compared with optimal solutions generated by exact solution procedures. Numerical examples are presented to illustrate the developed model and solution methods.